essay on corona virus in india

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The Rise and Impact of COVID-19 in India

Affiliations.

1 School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
2 VIT-BS, Vellore Institute of Technology, Vellore, India.
PMID: 32574338
PMCID: PMC7256162
DOI: 10.3389/fmed.2020.00250

The coronavirus disease (COVID-19) pandemic, which originated in the city of Wuhan, China, has quickly spread to various countries, with many cases having been reported worldwide. As of May 8th, 2020, in India, 56,342 positive cases have been reported. India, with a population of more than 1.34 billion-the second largest population in the world-will have difficulty in controlling the transmission of severe acute respiratory syndrome coronavirus 2 among its population. Multiple strategies would be highly necessary to handle the current outbreak; these include computational modeling, statistical tools, and quantitative analyses to control the spread as well as the rapid development of a new treatment. The Ministry of Health and Family Welfare of India has raised awareness about the recent outbreak and has taken necessary actions to control the spread of COVID-19. The central and state governments are taking several measures and formulating several wartime protocols to achieve this goal. Moreover, the Indian government implemented a 55-days lockdown throughout the country that started on March 25th, 2020, to reduce the transmission of the virus. This outbreak is inextricably linked to the economy of the nation, as it has dramatically impeded industrial sectors because people worldwide are currently cautious about engaging in business in the affected regions.

Keywords: COVID-19; India; SARS-CoV-2; economy; safety measures.

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Published: 21 April 2021

Strategy for COVID-19 vaccination in India: the country with the second highest population and number of cases

Velayudhan Mohan Kumar ORCID: orcid.org/0000-0002-8477-6679 1 ,
Seithikurippu R. Pandi-Perumal ORCID: orcid.org/0000-0002-8686-7259 2 ,
Ilya Trakht 3 &
Sadras Panchatcharam Thyagarajan ORCID: orcid.org/0000-0002-4585-5243 4

npj Vaccines volume 6 , Article number: 60 ( 2021 ) Cite this article

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Drug discovery
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Free vaccination against COVID-19 commenced in India on January 16, 2021, and the government is urging all of its citizens to be immunized, in what is expected to be the largest vaccination program in the world. Out of the eight COVID-19 vaccines that are currently under various stages of clinical trials in India, four were developed in the country. India’s drug regulator has approved restricted emergency use of Covishield (the name employed in India for the Oxford-AstraZeneca vaccine) and Covaxin, the home-grown vaccine produced by Bharat Biotech. Indian manufacturers have stated that they have the capacity to meet the country’s future needs for COVID-19 vaccines. The manpower and cold-chain infrastructure established before the pandemic are sufficient for the initial vaccination of 30 million healthcare workers. The Indian government has taken urgent measures to expand the country’s vaccine manufacturing capacity and has also developed an efficient digital system to address and monitor all the aspects of vaccine administration.

A global survey of potential acceptance of a COVID-19 vaccine

COVID-19 vaccines: progress and understanding on quality control and evaluation

Looking beyond COVID-19 vaccine phase 3 trials

Introduction.

A year has passed since the first case of novel coronavirus infections was detected in China’s Wuhan province. During the initial period of the disease, the efforts were concentrated on preventing and slowing down transmission 1 , 2 , 3 , 4 , 5 , 6 . Global analysis of herd immunity in COVID-19 has shown the urgent need for efficacious COVID-19 vaccines 7 . Currently, the vaccine development efforts have started to come to fruition as some of the leading vaccine candidates have shown positive results in the prevention of clinical disease 8 , 9 , 10 , 11 , 12 .

Although not mandatory, India with its estimated population of 1380 million (as of 2020) is planning to administer the vaccine to all its citizens who are willing to take it. Importation of vaccines might not be the best option for India due to its large population. According to the International Air Transport Association (IATA), it would require thousands of flights to transport the vaccine from the production sites abroad to the distribution areas.

India, which has a robust vaccine development program, not only plans for domestic manufacture of COVID-19 vaccine but also for its distribution in countries that cannot afford to buy expensive vaccines from the Western world. In India, the data emanating from clinical trials of different vaccines support their eligibility for emergency authorization, even though some of the final details are not available yet. The emphasis now is on the quality control, quality production, and cost control of these vaccines to make them affordable to even the poorest nations in the world.

COVID-19 vaccine candidates in clinical trials in India

COVID-19 vaccine candidates that are under production and in clinical trials in India are among the leading products internationally. Apart from India’s indigenous COVID-19 vaccines, some local pharmaceutical and biotech companies have signed collaborative agreements with foreign-based vaccine developers. These collaborations range from conducting clinical trials to large-scale manufacturing of vaccines and their distribution. The list of eight vaccine candidates, currently undergoing clinical trials in India, is shown in Table 1 and their technical details are given in Table 2 13 .

Covishield by the Serum Institute of India

Serum Institute of India (SII), Pune, has signed agreements with a few manufacturers such as Oxford-AstraZeneca, Codagenix, and Novavax. It is now producing at a large scale, the Oxford-AstraZeneca Adenovirus vector-based vaccine AZD1222 (which goes under the name “Covishield” in India), and it has stockpiled about 50 million doses 14 . The company will produce 100 million doses per month after January 2021. SII will ramp up its capacity further to produce 2 billion doses per year. Covishield is produced under the “at-risk manufacturing and stockpiling license” from the Drugs Controller General of India (DCGI), and the Indian Council for Medical Research (ICMR). The ICMR funded the clinical trials of the Covishield vaccine developed with the master stock from Oxford-AstraZeneca.

The SII and ICMR have jointly conducted a Phase II/III, observer-blind, randomized, controlled study in healthy adults at 14 centers in India, for comparison of the safety of Covishield (manufactured in India) versus the original Oxford-ChAdOx1 in the prevention of COVID-19 disease. A total of 1600 eligible participants of ≥18 years of age or older were enrolled in the study. Of these, 400 participants were part of the immunogenicity cohort and were randomly assigned in a 3:1 ratio to receive either Covishield or Oxford-ChAdOx1, respectively. The remaining 1200 participants from the safety cohort were randomly assigned in a 3:1 ratio to receive either Covishield or Placebo, respectively. The safety, immunogenicity, and efficacy data of ChAdOx1 administered in two doses containing 5 × 10 10 viral particles on 23,745 participants aged ≥18 years or older from clinical studies outside India showed the vaccine efficacy to be 70.42% 15 . The safety and immunogenicity data generated from the clinical trial in India was found to be comparable with the data from previous trials conducted outside of India.

The SII has applied to DCGI for permission to do clinical trials in India for Covovax (NVX-CoV2373) developed in partnership with Novavax 16 . They are hopeful of launching the vaccine by June 2021. The US-based pharma claims that their Covid jab was found to be 89.3% effective in a UK trial.

Covaxin by Bharat Biotech Ltd

India’s first domestic COVID-19 vaccine, Covaxin TM , developed and manufactured by Bharat Biotech International Limited, in collaboration with the National Institute of Virology of ICMR, is one of the two vaccines of the company, undergoing clinical trials, and is being stockpiled under an “at-risk manufacturing and stockpiling license”.

Covaxin TM is an inactivated-virus vaccine, developed in Vero cells. The inactivated virus is combined with Alhydroxiquim-II (Algel-IMDG), chemosorbed imidazoquinoline onto aluminum hydroxide gel, as an adjuvant to boost immune response and longer-lasting immunity. This technology is being used under a licensing agreement with Kansas-based ViroVax. The use of the Imidazoquinoline class of adjuvants (TLR7/8 agonists), shifts the T-cell response towards Th1, a T-Helper 1 phenotype (which is considered safer than Th2 responses against SARS-CoV-2) and reduces the risk of immunopathologically mediated enhanced disease 17 .

Bharat Biotech Ltd and ICMR began Phase-III trials of Covaxin TM on November 16, 2020, with 26,000 volunteers across 25 centers in India. According to the company, it is the largest clinical trial in India for a COVID-19 vaccine. The firm has generated safety and immunogenicity data in various animal species such as mice, rats, rabbits, Syrian hamster, and also conducted challenge studies on non-human primates (Rhesus macaques) and hamsters. All these data have been shared by the firm with the drug regulatory body of India. Phase-I and Phase-II clinical trials were conducted on approximately 800 subjects and the results have demonstrated that the vaccine is safe and provides a robust immune response and protection. The Phase-III efficacy trial was initiated in India on 25,800 volunteers and to date, ~22,500 participants have been vaccinated across the country. As per the data available currently, the vaccine is safe. Bharat Biotech has stockpiled 10 million doses of Covaxin and will be ready with another 10 million by February 2021. The company will produce 150 million doses by July–August 2021, and they will be ready with 700 million doses by the end of 2021. The firm is also preparing a protocol to expand the testing of its vaccine in children aged 2–15 years.

ZyCoV-D by Cadila Healthcare (Zydus Cadila)

Production of another domestic COVID-19 vaccine, ZyCoV-D by Cadila Healthcare, Ahmedabad, based on the new plasmid DNA vaccine technology, is supported by the Department of Biotechnology, Government of India. Vaccines based on plasmid DNA technology are not licensed for public use. Plasmids are used as vectors to directly deliver the DNA encoding the target antigens into the body of the recipient. Sequence encoding for the pathogen’s antigen is engineered into recombinant plasmid DNA. It is used as the vaccine vector so that the vaccine antigens are directly produced by human cells, thus eliciting an immune response. The Phase-I trials of this vaccine began on July 13, 2020, on volunteers of 18–55 years of age. As ZyCoV-D showed promise in a Phase-I study, and the drugmaker Cadila is currently finishing Phase-II trials on over 1000 volunteers across nine sites. This vaccine is administered intradermally.

COVID vaccine (still unnamed) by Biological E. Limited

Biological E. Limited (BE) has initiated a Phase-I/II clinical trial in India of COVID-19 vaccine (RBD219-N1) produced in collaboration with Dynavax Technologies Corporation and Baylor College of Medicine. BE’s COVID-19 vaccine candidate is based on classical vaccine technology of a protein antigen, SARS-CoV-2 Spike RBD, adsorbed to the adjuvant Alhydrogel (Alum), in combination with another approved adjuvant, CpG 1018. The RBD of the S1 subunit binds to the angiotensin-converting enzyme-2 (ACE2) receptor on the host cell membrane and facilitates virus entry. The results of these clinical trials are expected to be available by February 2021. BE’s Phase-I/II clinical trial will evaluate the safety and immunogenicity of the vaccine candidate at three different doses in about 360 healthy subjects in the age group of 18–65 years. The vaccination schedule consists of two doses (of the same strength) for each study participant, administered via intramuscular injection, 28 days apart.

A locally developed, but still unnamed, COVID-19 vaccine of BE has also been given regulatory approval for clinical trials in India. Details of the drug trials have not yet been disclosed.

Sputnik V by Dr. Reddy’s Laboratories

Gam-COVID-Vac, trade-named Sputnik V, is a COVID-19 vaccine developed by the Gamaleya National Center of Epidemiology and Microbiology of Moscow, Russia. Sputnik V is a two-vector viral vaccine based on human adenoviruses. Sputnik V uses adenoviruses Ad5 and Ad26 18 . The recombinant adenovirus types 26 and 5 are biotechnology-derived and contain the SARS-CoV-2 S protein cDNA. Both of them are administered into the deltoid muscle. The Ad26-based vaccine is used on the first day and the Ad5 vaccine is used on the 21st day to boost immune responses. Russia’s Sputnik V vaccine stipulates storage at a temperature not higher than −18 °C.

Dr. Reddy’s Laboratories, located in Hyderabad, have received regulatory approval from the DCGI to conduct mid-to-late-stage human trials for Russia’s Sputnik V vaccine in India. Russia’s RDIF-Gamaleya Institute has signed agreements with more than one Indian company for the large-scale manufacture of their Sputnik V vaccine.

mRNA vaccine (still unnamed) by Gennova Biopharmaceuticals Ltd

The latest COVID-19 vaccine candidate that was granted conditional permission for Phases 1 and 2 of the human clinical trials by DCGI is the mRNA vaccine developed by the Pune-based Gennova Biopharmaceuticals Ltd in collaboration with HDT Biotech Corporation, USA.

COVID-19 vaccination in India

The government of India has constituted a National Expert Group on Vaccine Administration for COVID-19 (NEGVAC) to provide guidance on all aspects of COVID-19 vaccine administration in India 19 . According to NEGVAC, the COVID-19 vaccine will be offered first to healthcare workers, frontline workers, and to persons above 50 years of age (with first preference for those above 60), followed by persons younger than 50 years of age with associated comorbidities. The government has set up a committee comprising experts from various specialties including oncology, nephrology, pulmonology, and cardiology to define the clinical criteria, based on which people with comorbidities should be prioritized for Covid-19 vaccination. Committee has recommended that anyone with a congenital heart disease that leads to pulmonary arterial hypertension, end-stage kidney disease, or cancers such as lymphoma, leukemia, myeloma, decompensated liver cirrhosis, primary immune deficiency conditions, and sickle cell anemia should be included in the priority. The latest electoral roll for the general election will be used to identify the population aged 45 years or more. The cut-off date for determining the age will be January 1, 2021. There will be a provision for self-registration for vaccination, for those eligible persons who have been missed out from the rolls for one reason or other, after giving some proof of identity. After vaccinating nearly 300 million of the population in the first phase, the remaining population will receive the vaccine based on the disease epidemiology and vaccine availability.

The Government of India has arranged to procure 600 million doses of the COVID-19 vaccine from the manufacturers highlighted above and is negotiating for another billion doses. Covishield, produced by SII, and Covaxin produced by Bharat Biotech Ltd were procured by the government, and are administered initially. Nevertheless, the government may alter its strategy, as and when the other vaccines are cleared for administration after the clinical trials. While obtaining the vaccine is the first requirement, distribution, and vaccination of the huge Indian population presents a significant logistic challenge. On November 24, 2020, Indian Prime Minister Shri Narendra Modi discussed the vaccine distribution strategy with the chief ministers and other representatives of states and Union Territories (UTs). He visited the three leading companies on November 28, 2020 to have first-hand information, and to assure them of full support from the government.

The companies SII, Bharat Biotech, and Pfizer India had applied for “emergency use authorization” of their vaccines. All their applications were reviewed by the expert panel at the Central Drugs Standard Control Organization (CDSCO) for their suitability for vaccination in this country. During the second round of discussions, Covishield, the vaccine candidate from Pune-based SII, was approved for emergency use by the Subject Expert Committee (SEC) of DCGI on January 1, 2021. They have approved the vaccine to be given in two doses 4–12 weeks apart. This time interval is similar to that employed by the UK, and the company is allowed to deploy its vaccines to priority groups, even though a full safety assessment has not been completed. Bharat Biotech was asked to furnish more data demonstrating the efficacy of its candidate, Covaxin. On January 2, 2021, the SEC gave its approval to Bharat Biotech’s Covaxin coronavirus vaccine also for emergency use. These recommendations, along with rollout modalities, were taken up by the DCGI. In a major development, on January 3, 2021, DCGI approved two COVID-19 vaccines for restricted emergency use in the country 20 . Bharat Biotech’s Covid-19 vaccine Covaxin has been recommended for conditional approval (i.e., to be administered under clinical trial mode) by the DCGI based on Phase 3 immunogenicity data for 24,000 volunteers after the first dose, and for 10,000 volunteers after the second dose. Conditional approval of Covaxin was based on incomplete Phase 3 trial data, in the context of a possible emergency, especially infection by mutant strains. On behalf of SEC, Director, All India Institute of Medical Sciences (AIIMS) Dr. Randeep Guleria explained that the Bharat Biotech vaccine will be used in an emergency when there is a sudden increase in cases and need for vaccination. Covaxin can also be used as a backup if questions arise on the efficacy of the SII’s vaccine. According to Dr. Harsh Vardhan, the Health Minister, Covaxin has immunogens (epitopes) from other proteins, in addition to those from Spike proteins. This makes it more likely to work against variants like the N501Y variant (UK variant) 21 . Moreover, Covaxin data showed that it not only produced antibodies in all the participants but it also sensitized CD4 T lymphocytes that impart a durable immune response. The technology used for Covaxin production allows it to target various components of the virus, like the membrane glycoprotein and nucleoprotein, in addition to the spike protein. Managing Director of Bharat Biotech Dr. Krishna Ella said that they will be able to establish Covaxin’s ability to protect against mutant strains of the novel coronavirus, detected in the UK and 30 other countries. Dr. Ella explained that the approval of the SEC of DCGI only means that the firm will no longer require to have a placebo group in its ongoing clinical trial, and will vaccinate people in an open-label format. The safety and efficacy of the drug was to be closely monitored. However, Bharat Biotech announced on March 3, 2021, the results of the third round of clinicals trials showed that Covaxin was 80.7% effective in preventing COVID-19. After going through Covaxin’s Phase 3 trial data, the subject expert committee gave emergency use authorization for this vaccine, and so Covaxin is no longer administered under clinical trial mode.

Pfizer India has reportedly sought more time, but the company’s mRNA vaccine has already been approved, under emergency use conditions, in a number of countries including the USA and UK, and by the World Health Organisation (WHO). Though the extremely low temperature of −70 °C required for storing the Pfizer vaccine poses a big challenge for its delivery in India, the company has hinted at making the necessary arrangements for the same. However, the present laws in India do not normally permit the usage of any vaccine (like the Pfizer vaccine) that has not undergone proper clinical trials in India. Though people above 50 years of age have been prioritized for vaccination by the government, a decision on the administration of Covishield to those above 60 and below 18 had to wait, as clinical trials have not been carried out yet on these age groups of the population. However, the government had relaxed the rules for marketing drugs in India by introducing the “New Drugs and Clinical Trials Rules, 2019”. The need for local clinical trials was also waived in the new rules if the drug is already been approved by any of the DCGI- approved countries, which the DCGI can decide on a case-to-case basis. Approvals by USA, UK, and WHO can be taken into consideration and DCGI may give approval for mRNA vaccine and also for the administration of Covishield and Covaxin to other age groups.

Regional Director, WHO South–East Asia Region, Dr. Poonam Khetrapal Singh, welcomed the first emergency use authorization given to the COVID-19 vaccine. According to her, this decision taken by India will help to intensify and strengthen the fight against the COVID-19 pandemic in the region. The use of the vaccine in prioritized populations, along with the continued implementation of other public health measures and community participation will be important in reducing the impact of COVID-19.

COVID-19 vaccine distribution: functional cold chain

India has sufficient manufacturing capability for the vaccine (more than 2.4 billion doses annually) and various medical and surgical disposables such as vials, stoppers, syringes, gauze, and alcohol swabs. However, the first bottleneck was the storage and transportation of the vaccines, as this requires very specific temperature regimens. Some of the vaccines under development and production in other parts of the world require storage temperatures as low as −80 °C. Fortunately, the vaccines that India has introduced first for distribution in the country require a storage temperature of 2–8 °C only. The government has been working on measures for the quick and effective distribution of the COVID-19 vaccine. Vaccine manufacturers have started airlifting the vaccines in cold boxes with digital temperature tags to four major depots at Karnal (Haryana), Mumbai, Chennai, and Kolkata, where they are stored in walk-in coolers. From there, planes or insulated vans would transport the vaccines to the designated stores in 37 States/UTs. From these 41 centers, they are further transported to temperature-controlled facilities at the district-level vaccine stores by the State/UT governments. The vaccines are stored in ice-lined refrigerators (ILRs) in districts, from where they are transported to distribution centers in cold boxes and then in ice-packed vaccine carriers to vaccination sites. Real-time remote temperature monitoring of 29,000 cold-chain points is already done through COVID Vaccine Intelligence Network (Co-WIN) vaccine delivery management system, which is a cloud-based digitalized platform. Co-WIN platform was developed by India, but any country can use it. The Indian government will extend assistance for the same.

The initial batches of COVID-19 vaccines are administered through the Universal Immunization Program (UIP) mechanism already operational in India, and it will recruit private cold-chain operators to boost up the capacity. Through UIP, the government is currently, immunizing 26 million children and 30 million pregnant women annually. As UIP has over 26,250 functional cold-chain points at subdistrict or rural level centers (out of its total 28,932 points), vaccines can be stored at facilities not far from the vaccination sites. With the 85,622 cold-chain equipment that UIP has, and the other cold chain infrastructure of the immunization program, the government of India can manage 600 million doses and the private sector can manage 250–300 million doses annually. It could be surmised that with the present capacity, about 400 to 450 million people can be vaccinated in India annually. It indicates that vaccination in India that started from January 2021 would be able to vaccinate only about one-third of the population, by the beginning of 2022, even if it uses the present capacity of the immunization program entirely for COVID-19 vaccination. This is only a theoretical assumption, as India cannot afford to neglect all other vaccination programs under UIP, for the sake of COVID-19. Thus, there is an urgent need to expand the cold-chain infrastructure for storage and transport, as India, the world’s second-most populous nation, moves into the next stage of managing the COVID-19 pandemic. On October 15, 2020 itself, Dr. Harsh Vardhan, the Health Minister of India had directed the states to make a robust plan for vaccine storage and distribution.

The government of India would look for companies, including private ones in each city, which have cold storage facilities and which can take care of distribution, under the regulatory control of the government. Synergistic use of the food cold chain is what the government can use during this time of health emergency. Their facilities would require some minor redesign of storage and transportation. The food cold chain normally has the necessary infrastructure facilities and a complex supply of chain logistics and management.

Manpower requirement

India’s UIP currently has 55,000 cold-chain staff, and 2.5 million health workers. It will be the health workers, as first-line responders who are getting the vaccination initially. According to government officials, the current healthcare infrastructure may not require additional manpower for administering the vaccine to the healthcare workers. For the second round of vaccination of the priority groups such as the elderly population, persons with comorbidities, pregnant women, and children, a much larger number of trained medical and paramedical staff experienced in vaccine administration will be in place to handle the workload. Understandably, these newly recruited staff will receive the vaccination before they become members of the workforce. Vaccination of people above 60 years and those above 45 with comorbidities have already started from March 1, 2021.

The government of India had asked the states to start training the additional personnel. The orientation of vaccinators through a virtual platform had started on December 5, 2020. The government of India has launched ‘Integrated Govt. Online training’ (iGOT) portal on the Ministry of Human Resources and Development (HRD)’s Digital Infrastructure for Knowledge Sharing (DIKSHA) platform for the capacity building of frontline workers on COVID-19. This platform has training resources that may be accessed by health staff in case they are unable to access the training session or if they want to revisit the training resources. The identified manpower from all the states has been trained in handling the Co-WIN system. COVID-19 vaccine is now introduced only after all training is completed in the district/block/planning unit levels.

Though virtual training methods were used wherever possible, the majority of the training was conducted through the classroom platform. The newer training modalities emphasized “the new normal”, i.e., mitigation of the risk of transmission. For this purpose, 49,604 Medical Officers (in 681 districts) were trained on operational guidelines. The government has already trained 2360 trainers, who would, in turn, train immunization officers, cold-chain officers, IEC (Information, education, and communication) officials, and development partners. As we are reporting, more than 7000 of them have already completed their training, though the number of people required will be several times more than that. More than 18,000 new blocks have been created for vaccination. Trained vaccination teams have already been deployed at 1400 blocks. Each vaccination team will have five members consisting of qualified and trained vaccinators, support staff, and security staff.

As India is planning to have COVID-19 vaccination programs in the urban and rural areas simultaneously, midwives and auxiliary nurse midwives, who have a far greater reach in the interiors and rural areas, were included in the first group of health workers trained in vaccination skills. These trained resources will play a crucial role in the health care of people in rural India. In order to expand a vaccination campaign, the government is planning to engage the allied healthcare workforce including pharmacists and public health workers. Pharmacists may be able to do a better job as a second line of “vaccination warriors” as they have a professional knowledge of maintaining the cold chain and keeping the vaccine intact. It is suggested that the 0.8 million-strong pharmacists in the country could play an important role in this endeavor. At the same time, the existing laws and regulations need to be amended to permit pharmacists to administer vaccines.

The majority of vaccines currently under clinical evaluation need to be administered through the intramuscular route. Later, the vaccinators can be trained adequately to have first-hand experience for administering all modes of injections namely intramuscular, subcutaneous, and intradermal modes. In addition, they should be able to handle different brands of COVID-19 vaccine that will become available in the country in the future. Those vaccines may require a different set of norms for storage and administration. After all this training (e.g., train-the-trainer), the trained personnel can be employed in other places and for other vaccination programs even after this pandemic subsides. They are also trained to monitor and manage common adverse reactions to the injection.

A large number of private clinical laboratories, including diagnostic laboratories, have been established, not only in urban areas but also in the rural areas in India. Most of them have good infrastructural and manpower support. If they become part of the COVID-19 vaccination program, it would be beneficial to both the government and the private clinical laboratories. All these ventures will be executed under strict regulatory control, following standard protocol established by the government agencies, with a standard operating procedure (SOP) to guide the trained workforce. States are augmenting the state helpline 104 (which will be used in addition to 1075) for any vaccine/software-related query. Orientation and capacity building of the call center executives have taken place in the states and UTs.

Implementation of the program

COVID-19 vaccination, at least in the initial phase, will be totally under government control. High-level coordination at national, state, and district levels have been established for effective cooperation and collaboration among the key departments involved in COVID-19 vaccination. Twenty-three ministries/departments and numerous developmental partners are involved in planning for the COVID-19 vaccine introduction. Their roles have been described in the operational guidelines issued by the Ministry of Health and Family Welfare, Government of India 19 . Co-WIN system will be linked to existing UIP programs and it will meticulously monitor and follow up on the immunized individuals. The Co-WIN system will be used not only to track enlisted beneficiaries but it will also to ensure that only pre-registered beneficiaries will be vaccinated in accordance with the prioritization. Enlisted beneficiaries can select vaccination sites nearest to their home. Autogenerated SMS/email intimations are sent to the beneficiaries, vaccinators, mobilizers, and supervisors about the date, time, and place of the session. To observe the staggered approach, beneficiaries are advised by mobilizers to come to the session as per the staggered time slot to prevent overcrowding at the session site. As per the guidelines issued by the Centre for the COVID-19 inoculation drive, 100 people will be injected in each session per day. People will be monitored for 30 min after administering the shots for any adverse event. On the basis of the initial experience, some vaccination sites have been permitted to work for 24 h every day and the number of people to be injected in each session has been increased up to 200, to speed up the vaccination. Experience gained in the vaccination of the first round would be helpful for the improvement of the second and subsequent cycles.

The purpose of this perspective was to highlight the overall crux of the vaccine development and vaccination strategies that were implemented during a pandemic in a densely populated country (India). This report can be viewed as a baseline document for future pandemic preparedness, and to effectively tailor and refine the strategies that will help the population at large 22 , 23 .

India is in a privileged position in producing affordable medical, surgical, and essential generic medicines for the world. It is also well-known that India is the world’s largest manufacturer and worldwide distributor of vaccines. The current COVID-19 pandemic has triggered rapid development, emergency use authorization, and unprecedented collaborative efforts from various stakeholders. Although vaccination might be a cost-effective strategy for survival and a better quality of life for the people as well as for the revival of the economy of India, questions remain. For example, vaccination might not work for some individuals. This, in turn, requires a periodic re-evaluation of the vaccine platforms. Owing to these barriers and gaps in our understanding, the efficacy and safety of COVID-19 vaccination through post-marketing surveillance is of paramount importance and requires long-term follow-up. This should account for both successes and failures, outstanding benefits, and/or its superiority over other types of pharmacological and non-pharmacological treatment regimens. Studies are needed nationally and globally, along with transparent sharing of data and reports among all participating companies, institutions, and nations. This is mandatory for periodic evaluation and re-strategizing COVID-19 management plans. These data analyses can hold the keys to the future effective public health management of COVID-19. India’s experience in immunization for COVID-19 offers tips for strategy preparation, not only for countries with similar economic strength and health facilities but also for the world at large.

Data availability

No datasets were generated or analyzed during this study.

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Kumar, V.M., Pandi-Perumal, S.R., Trakht, I. et al. Strategy for COVID-19 vaccination in India: the country with the second highest population and number of cases. npj Vaccines 6 , 60 (2021). https://doi.org/10.1038/s41541-021-00327-2

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BRIEF RESEARCH REPORT article

The effect of covid-19 and related lockdown phases on young peoples' worries and emotions: novel data from india.

$\nMeenakshi Shukla$

1 Department of Psychology, Magadh University, Bodh Gaya, India
2 Department of Psychology, Banaras Hindu University, Varanasi, India
3 Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
4 Division of Psychology, Department of Life Sciences, and Center for Cognitive Neuroscience, College of Health, Medicine and Life Sciences, Brunel University, London, United Kingdom

The COVID-19 pandemic has posed unprecedented stress to young people. Despite recent speculative suggestions of poorer mental health in young people in India since the start of the pandemic, there have been no systematic efforts to measure these. Here we report on the content of worries of Indian adolescents and identify groups of young people who may be particularly vulnerable to negative emotions along with reporting on the impact of coronavirus on their lives. Three-hundred-and-ten young people from North India (51% male, 12–18 years) reported on their personal experiences of being infected by the coronavirus, the impact of the pandemic and its' restrictions across life domains, their top worries, social restrictions, and levels of negative affect and anhedonia. Findings showed that most participants had no personal experience (97.41%) or knew anyone (82.58%) with COVID-19, yet endorsed moderate-to-severe impact of COVID-19 on their academics, social life, and work. These impacts in turn associated with negative affect. Participants' top worries focused on academic attainments, social and recreational activities, and physical health. More females than males worried about academic attainment and physical health while more males worried about social and recreational activities. Thus, Indian adolescents report significant impact of the pandemic on various aspects of their life and are particularly worried about academic attainments, social and recreational activities and physical health. These findings call for a need to ensure provisions and access to digital education and medical care.

Introduction

The COVID-19 pandemic has had far-reaching consequences on the physical and mental health of individuals as well as the health of economies across the globe. While young people may be less susceptible to severe forms of the illness, suffering milder symptoms, lower morbidity, and better prognosis compared to adults ( 1 , 2 ) they have experienced an upsurge in stress ( 3 , 4 ) precipitating loneliness, anxiety and depression in many ( 5 – 8 ). As emotional symptoms in adolescence can become associated with many serious mental health outcomes including suicide, long-term physical health consequences, and significant healthcare burden ( 9 – 11 ), the effect of COVID-19 on young people's mental health could be more damaging in the longer run than the infection itself ( 12 ). Measuring early signs of mental health challenges such as worries and negative emotions in young people is thus an urgent priority for researchers ( 13 , 14 ) as well as policy-makers, including identifying those most vulnerable to mental health difficulties. While this information is crucial for both high- and low-income countries, countries with lower resources dedicated to mental health may benefit more from early forecasts of these needs.

India has one of the highest COVID-19 infection rates in the world with over 2.5 million confirmed cases and the death toll on the rise ( 15 , 16 ). The first case of COVID-19 was identified on January 30, 2020 in Kerala ( 17 ) in a student who had returned from Wuhan, China ( 18 ). However, since March 2020, there has been an upsurge in the spread of the infection. In response, the Government imposed a nationwide lockdown to prevent community transmission of the infection. Despite some regional differences in the extent of lockdown restrictions, based on total COVID-19 cases in that region ( 18 ), everyone in India has experienced closure of educational and training institutions; hotels and restaurants; malls, cinemas, gyms, sports centers; and places of worship. A recent correspondence article by Patra and Patro ( 19 ) speculated that school closures in particular may have been especially damaging for young people and highlighted the urgent need to address mental health issues in Indian adolescents. Yet there have been no such systematic efforts to our knowledge. Here, we report new data from a small cohort of young people from India. We describe their experiences of the COVID-19 pandemic and the impact of COVID-19 pandemic on their daily life. We describe the content of the most common worries reported by young people alongside quantitative measures of current negative and (absence of) positive emotions—symptom-markers of common mental health difficulties such as anxiety and depression. We then assess which young people (in terms of gender, age, and socioeconomic status) are particularly susceptible to reporting more negative emotions and fewer positive emotions. In India, before the pandemic started, public awareness around mental health in young people had been increasing along with the recognition that such problems can be economically costly ( 20 ). Our data can thus signpost emerging, potentially costly mental health problems post-pandemic.

Participants and General Procedures

This study received approval from the Institutional Ethics Committee, Institute of Medical Sciences, Banaras Hindu University (Ref No.: Dean/2020/EC/1975) and King's College London Research Ethics Committee (Ref: HR-19/20-18250). Participants were recruited between June 5, 2020 and July 12, 2020. Prospective participants from different states of North India (Uttar Pradesh, Bihar, New Delhi, West Bengal, Madhya Pradesh, Gujrat) and their parents were identified by circulating information about the study including eligibility criteria (aged 12–18 years; currently residing in India) through social media sites, such as Facebook and WhatsApp. Interested and eligible individuals were sent bilingual (Hindi and English) information sheets (one for young people, one for the parents if the participant was aged 12–17 years). Those who agreed to participate after reading the information sheet received the survey link for both the English and Hindi versions and were requested to complete one based on their language preference. The survey link began with a question about the participants' age. If the participant was 18 years, they viewed and completed a consent form with an electronic signature and their contact details for follow-up assessments. Any participant aged 12–17 years was presented with an assent form with a parental/guardian consent form. To verify that parent/guardian consents were authentic, follow-up phone contact was made with the parent/guardian using the provided contact details. Survey questions were not presented further for incomplete consent/assent forms.

The online survey was developed using Qualtrics software (Qualtrics, Provo, UT). The first third of the survey comprised questions around demographics, personal experiences and knowledge of others who had been infected by the coronavirus, extent of social restrictions and social contact, and the impact of the viral outbreak on various life domains. The second third of the survey included measures of poor mental health such as negative affect, anhedonia (absence of positive affect), and the content of worries. The final third included measures of well-being (positive aspects of mental health), more specific negative emotional experiences (loneliness, boredom) and a cognitive measure (positive and negative future imagery) (presented elsewhere). All Hindi translations used the translation-back-translation method. MS completed the first set of translations, which were back translated by TS. JL checked the back-translations. Where there were definitional discrepancies with the original scale, these were discussed with RP and VK and re-translations were done by MS. The average time taken by the participant to complete the survey was 20 min.

Demographics

Participants submitted information on their age, sex assigned at birth, family monthly income level, and number of family members.

Personal Experiences of and Knowledge of Close Others With COVID-19

Five items (with yes/no responses) measured the extent to which participants had experienced the infection: have you ever been affected or suspected of having the coronavirus infection at any time, do you currently have a confirmed diagnosis of coronavirus infection, are you currently suspected of having a diagnosis of coronavirus infection, have you had a past confirmed diagnosis of coronavirus infection but have now recovered, have you had a past suspected diagnosis of coronavirus infection but have now recovered. Five items (with yes/no responses) assessed whether participants knew others who had experienced the infection, including: a family member, friend, other acquaintance (e.g., classmate), other individual known indirectly (e.g., acquaintance of a family member/friend/acquaintance), know no one with the illness. If the participants endorsed one of the first 4 items, they were asked whether the individual affected had recovered, were still recovering, were hospitalized or had passed away.

Social Restrictions Associated With COVID-19

To describe the extent of reduced social contact, participants indicated the total number of days spent in self-isolation (i.e., not leaving the house), days in which they spent 15 min or more outside the house, days in which they had face-to-face contact with another person for 15 min or more, days in which they had a phone or video call with another person for 15 min or more.

Impact of COVID-19

Participants rated the impact of the outbreak (including associated lockdown measures) on work, study, finances, social life (including leisure activities), relationship with family, physical health, emotions, and caring responsibilities (for children/siblings or elderly/fragile family members) over the last 2 weeks on a 5-point scale (0 = not applicable/none, 1 = very mildly, 2 = mildly, 3 = moderately, 4 = severely). Responses were summed across items to create a total impact score. In the current sample, the internal consistency reliability for the impact items was 0.706.

Content of Worries

Participants were asked to write down their top 3 worries using free text boxes. All free text responses were reviewed by two researchers (MS, TS), who then independently derived “worry categories” based on these responses. The categories proposed by MS and TS were then reviewed by RP, VK, and JL. Where common categories were identified by both researchers these were used in the final worry categories. Where there were differences, these were resolved through discussions, using the life domains listed in the COVID-19 impact questions to help guide the identification of conceptually distinct areas. The final 12 categories along with their descriptions are shown in Table 4 . Using this coding scheme and definitions, all responses were coded by both MS and TS independently to assess inter-rater agreement (Cohen's Kappa reliability). This was 0.98 for Worry 1, 0.90 for Worry 2, and 0.91 for Worry 3.

Negative Affect

The 10 negative affect items from the Positive and Negative Affect Schedule ( 21 ) were used to assess negative emotions. Respondents used a 5- point Likert scale ranging from 1 (very slightly or not at all) to 5 (extremely) to indicate the extent to which they experienced the given mood states during the last 2 weeks. A total negative affect score, ranging from 10 to 50, was created by summing across the scores of individual items. Cronbach's alpha was 0.878.

Nine items (nos. 1, 3, 4, 5, 7, 9, 10, 13, and 14) from the 14-item Snaith-Hamilton Pleasure Scale ( 22 ) were used to index anhedonia, the inability to experience pleasure; the remaining 7 items were deemed unlikely to apply during lockdown phases. Four response options were given for each item (strongly disagree, disagree, agree, or strongly agree), where strongly disagree and disagree were scored 1 and agree and strongly agree, scored 0. A summed score across items therefore ranged from 0 to 14, where higher scores indicated greater absence of positive affect. Cronbach's alpha was 0.723.

Statistical Analyses

After presenting the demographic characteristics of the sample, gender differences in age and income were analyzed using independent sample t -tests. Descriptives of young peoples' personal experiences of the infection, knowledge of others with the infection, the effect of lockdown on social isolation and contact with others and impact across other life domains were presented next. Before conducting any statistical analysis, the data were checked for fulfilling the assumptions for normality ( 23 ). The data did not show serious deviations from normality based on the histogram plots, except a slight positive skew for anhedonia. The skewness and kurtosis values of the data were also within the recommended limit of ±2 ( 24 , 25 ), most being < 1 (except for anhedonia which was >1). Thus, we employed parametric analyses for all the variables except for anhedonia which was explored using non-parametric tests. We investigated the degree to which the overall impact of COVID-19 across life domains varied as a function of gender (using independent samples t -test) and age and family income levels (using bivariate correlations). For the worry data, the percentage of individuals endorsing each worry category was calculated for each of the top 3 worries (first, second, third). However, in the final analysis, we collapsed across the top 3 worries to generate an overall percentage across participants of endorsing that worry among one of their top 3 worries. This meant, for instance, that any participant who rated the same worry across all 3 of their top worries was only represented once. The final percentage of young people endorsing the worry categories was compared across gender and for interpretability, by categorical age groups (Younger adolescents = 12–15 years; Older adolescents = 16–18 years) using chi-square tests. Finally, we presented data on negative affect and absence of positive affect (anhedonia); we investigated how these variables varied across gender, age, and per capita monthly income using multiple linear regression models; we further assessed whether inclusion of interaction terms significantly added to variance explained. Given a slight positive skew for anhedonia, we log-transformed this variable when conducting the regression analysis. To complement the multiple regression analysis of demographic predictors and their interactions, we also ran a series of parametric and non-parametric t -tests and correlations for negative affect and anhedonia, respectively, to assess the extent to which gender, age and family income levels individually associated with these variables. Correlations also assessed the extent to which the overall impact of COVID-19 associated with negative affect and anhedonia.

Demographic Characteristics

The final sample comprised 310 Asian-Indian adolescents (Mean age = 15.69 years; SD = 1.92) of whom 159 were males (Mean age = 15.60 years; SD = 1.98) and 151 were females (Mean age = 15.78 years; SD = 1.87). Males and females did not differ significantly in age, t (308) = −0.84, p = 0.40, d = 0.05. Furthermore, the Levene's test of equality of variances indicated an equal spread of scores in males and females ( F = 0.89, p = 0.34). Only 192 participants provided data for monthly per capita family income, which ranged from 125 to 150,000 Rupees (Mean = 9698.20; SD = 18315.22) with no significant mean or variance differences in the monthly per capita income between males and females [Male Mean = 8343.61; SD = 15065.95; Female Mean = 11439.82; SD = 21768.30; t (190) = −1.16, p = 0.25], d = 0.16, Levene's test of equality of variances: F = 2.63, p = 0.10.

Experiences of COVID-19

Item-level data for personal experiences and knowledge of close others with COVID-19 infections are presented in Table 1 for all participants; and males and females separately. Most young people had not personally experienced or known someone with the coronavirus infection. Of those who did report knowing someone infected with COVID-19, just under half (49.09%) reported that the affected person they knew had recovered from the infection, 12.73% reported that the person was still recovering, 14.54% reported that the known person was hospitalized, while 25.45% participants reported that the affected person passed away.

Table 1 . Personal experience of and knowledge of others with COVID-19 (Of note, while the first set of questions about personal experiences of COVID-19 reflects mutually exclusive response options (therefore adding up to 100%), the set of questions around knowledge of others are not all mutually exclusive. For instance, a participant reporting a family member as well as an acquaintance infected with the virus would be included twice, once when calculating the percentage of participants reporting an infected family member and once when calculating the percentage of participants having an infected acquaintance. Therefore, participants having knowledge of others with COVID-19 do not add up to 100%).

Social Restrictions and Impact of COVID-19

Item-level data for questions around social restrictions and reduced social contact are presented in Table 2 for all participants, for male and females separately; and correlations with age and monthly per capita family income. Compared to males, female participants spent significantly more days in self-isolation and more days engaging in phone or video call for 15 min or more. Participants with lower monthly per capita income spent more days in which they were out for 15 min or more, but fewer days engaging in phone or video calls. Age did not correlate with perceived social restrictions.

Table 2 . Restrictions associated with COVID−19.

Mean ratings of the impact of COVID-19 on various life domains are presented in Table 3 . Looking at how many young people endorsed moderate-to-severe impact for each domain, 43.6% reported this on their work, 56.8% on their studies, and 48.4% on their social life and recreational activities. Just under half of young people reported moderate-to-severe impact of the pandemic on their family relationships (48.4%), on their caring responsibilities (49.4%) and on their physical health (42.6%). However, 52% reported this for their emotions. For finances, moderate-to-severe impact was reported by 26.8% of young people. Sex, age, and per capita monthly income effects were examined on each domain-specific impact score and the total score, summed across mean ratings for each domain ( Table 3 ). No significant associations emerged between age and impact across any domain ( Table 3 ). Males reported higher mean impact scores for relationships with family members and physical health. Participants with lower per capita income experienced more impact of COVID-19 across life domains (indicated by total impact score) than those with higher monthly per capita income.

Table 3 . Impact of COVID-19 on psychosocial domains.

The percentages of young people endorsing each worry category for each of their top 3 worries are presented in the first three columns of Table 4 . These were used to derive the overall percentages of young people endorsing each worry category as one of their top 3 worries presented in Column 4. Using this fourth column, we noted that most participants reported education and studies (Academic) as one of their top worries. The second most common worry of participants centered around “Physical health, fitness, and safety.” Worries about “Social and recreational activities” also emerged as a major concern for several participants, followed by “Finances.” Some participants also listed “Global and societal concerns.” More females reported concerns about “Academic,” and “Physical health, fitness, and safety,” compared to males ( Table 4 ) while male participants reported more worries around “Social and recreational activities” activities than female participants. Comparison of worries across the adolescent groups revealed that while a higher percentage of older adolescents reported each of the worries as one of their top three worries compared to younger adolescents (except for “Unclear” category), the differences were statistically significant only for “Academic,” “Physical health, fitness, and safety,” “Global and societal concerns,” and “Other” categories ( Table 4 ).

Table 4 . Participants' reported content of top three worries over the last 2 weeks.

A stepwise multiple regression was conducted with negative affect as the dependent variable and age, gender, and per capita monthly income as predictors in step 1 and their interaction terms (i.e., age x gender, age x per capita monthly income, gender x per capita monthly income, and age x gender x per capita monthly income) entered in step 2. Results indicated that the model predicted by the demographic variables was non-significant, F (3,187) = 2.11, p = 0.10 (Adjusted R 2 = 0.017). Nor did the inclusion of interaction terms significantly increase variance explained, R 2 change = 0.004, p = 0.36, F (4,186) = 1.79, p = 0.13 (Adjusted R 2 = 0.016). These findings suggested that males and females did not differ on total negative affect, t (305) = −0.90, p = 0.37, d = 0.10 [Male mean = 21.67 (SD = 8.78), Female mean = 22.51 (SD = 7.85)], Levene's test of equality of variances: F = 0.46, p = 0.50. Nor were there significant correlations with age ( r = 0.09, p = 0.10) and per capita monthly income ( r = −0.11, p = 0.13). However, significant correlations emerged between negative affect and reported impact of COVID-19 across life domains ( r = 0.26, p < 0.001). Negative affect correlated (mostly) weakly but significantly with impact of COVID-19 on social life ( r = 0.13, p = 0.02), relationship with family ( r = 0.14, p = 0.01), physical health ( r = 0.20, p < 0.001), emotions ( r = 0.23, p < 0.001), and caring responsibilities ( r = 0.18, p < 0.001), but not with work ( r = 0.11, p = 0.06), study ( r = 0.07, p = 0.22), and finances ( r = 0.11, p = 0.06).

A stepwise multiple regression, similar to that conducted for “negative affect” was conducted for anhedonia but with the log-transformed scores since the anhedonia scores were slightly positively skewed. Results showed that the model with all demographic predictors was non-significant, Model 1: F (3,156) = 1.44, p = 0.23 (Adjusted R 2 = 0.008). Inclusion of interaction terms did not significantly increase the variance explained, R 2 change = 0.000, p = 0.85, F (4,155) = 1.08, p = 0.37 (Adjusted R 2 = 0.002). Assessment of the individual demographic predictors showed that males (Mean Rank = 165.43) reported higher levels of anhedonia than females (Mean Rank = 141.09); Mann–Whitney U = 9838.50, N1 = 156, N2 = 150, p = 0.01. Participants belonging to families with higher monthly per capita income experienced lower levels of anhedonia ( r s = −0.17, p = 0.02). However, there were no significant correlations between reported impact summed across life domains and anhedonia ( r s = −0.02, p = 0.74). While anhedonia correlated positively but weakly with impact of COVID-19 on physical health ( r s = 0.13, p = 0.02), it showed a significant but weak negative relationship with impact of COVID-19 on study ( r s = −0.20, p < 0.001) and social life ( r s = −0.11, p < 0.05). Anhedonia did not correlate significantly with the impact of COVID-19 on work ( r s = 0.01, p = 0.93), finances ( r s = −0.02, p = 0.70), relationship with family ( r s = 0.09, p = 0.13), emotions ( r s = −0.04, p = 0.45), and caring responsibilities ( r s = −0.02, p = 0.73).

This paper describes baseline data for a cohort of Indian adolescents recruited to a study aiming to assess the longitudinal impact of COVID-19 on negative emotions, worries and strategies used to manage these emotions. Participants were recruited at a time when the total number of coronavirus-infected people in India stood at 236,184 and ended when the total number of infections was 879,466, showing a consistent rise during the period of (baseline) data collection ( 16 ). Yet, even during this period of rising infections, personal experiences and knowledge of others who had been exposed to the coronavirus infection were uncommon for most of our participants. Nonetheless, participants reported moderate-to-severe impact of COVID-19. The impact data together with qualitative data on their top worries, underscored academic studies as a salient area of concern for most young people in this cohort, a likely outcome of social distancing measures preventing school attendance and educational progress. Other salient worries for young people were concerns over the health and safety of self and loved ones and the absence of age-typical social and recreational activities, again expected worries emerging due to the pandemic itself and associated lockdown measures. Interestingly, young people commonly reported worries for their own finances as well as the Indian and global economy, and society more generally. Significantly higher percentage of older adolescents (16–18 years) than younger ones (12–15 years) were worried about their academics, physical health and safety, global and societal concerns and other kinds of worries, which can be expected since with increasing age, the academic work and curriculum gets more difficult and late adolescence is also the crucial time for career explorations ( 26 ). Adolescence is a time of emerging independence (taking on more responsibilities for their own future) but also of interdependence, where self-construal becomes linked to roles and commitments to other groups in society ( 27 ). Identifying the content of these stressors and worries can help governments decide where to propose subsequent policy changes and facilitate society-wide measures. Beyond the need for dedicated mental health services (helplines, centers) called for in earlier papers [e.g., ( 28 )], our data specifically underscore the need for investment of resources into the safe opening of schools, changes to the curriculum and/or the provision of digital education to all young people. Reassurance over access to quality medical care is also a priority.

Within these impacts and worries, there were some gender differences. More females than males reported Academic as a top worry (though this gender difference was not replicated in quantitative impact ratings), which is likely since Indian adolescent females have been reported “more sincere” toward studies than Indian adolescent males, potentially meaning they are more committed and motivated to academic achievement ( 29 ). Males reported a greater impact of COVID-19 on physical health in quantitative ratings; in the Indian context male adolescents are more likely to engage in outdoor sports ( 30 ) and experience fewer sociocultural barriers to outdoor physical activity ( 31 ) than female adolescents. This difference between genders where males spent more time out of the house than females, may also have emerged because males identified social and recreational activities as a top concern; females by contrast, followed restrictions associated with COVID reporting more days in social isolation and on phone/video calls. Perhaps relatedly, more females expressed worries over physical health, fitness, and safety from contracting the virus than male participants. Sedentary lifestyles resulting from the lockdown ( 32 ) may not only affect childhood obesity but can also significantly affect mental health of adolescents. Some interesting trends were also noted in relation to socio-economic status (SES) of the participants, as indexed by the per capita monthly income of their families. Lower SES was associated with a higher impact of COVID across life domains but particularly with impacts on physical health and family. Lower SES was associated with more days participants spent outside of the home, which could explain the reported impact on physical health. Adolescents belonging to lower SES may be residing in crowded living situations, which together with parental stress due to the economic crisis ( 33 ), may mean them having to navigate more complicated family dynamics. Higher SES was associated with more days spent on phone/video calls, probably because participants belonging to higher SES have greater access to laptops, smartphones, and/or tablets than those from lower SES.

In terms of negative and (absence of) positive emotions, means reported in our sample using translated versions of standardized questionnaires were commensurate with those reported in general youth population samples in the west ( 34 ). Self-reported negative affect did not correlate with age, SES and did not vary between males and females but was greater in those reporting more impact of COVID-19 across life domains. Males and those from lower SES reported more anhedonia. These findings pursued longitudinally in time can help us to identify those who show propensity for anxiety/depression across time allowing us to signpost need for mental health resources. Although anhedonia was negatively linked with the impact of COVID-19 on study and social life of the participants, these associations were weak.

There are several study limitations. First, the sample has been obtained using convenience sampling methods (using social media) and responders were only from a few North Indian states. Hence it is difficult to say how representative it is of 12–18 year old Indian adolescents. Moreover, given the study survey requirements, only participants who had access to the Internet and had a registered phone number (to verify parental consent) could be recruited, biasing the study sample composition. However, SES classes seemed to be adequately represented since using the Modified BG Prasad Socio-economic Classification 2019 ( 35 ), (although there was some missing data) the sample reflected the entire continuum of SES classes in India. Second, as data was collected online, qualitative responses were unprobed and very often single word answers had to be coded, affecting the reliability of these data. Nonetheless, inter-rater reliability using this coding scheme was high. Third, participants did not report on whether they lived in rural or urban areas of their respective cities, and therefore our data cannot speak to rural-urban differences in adolescents' worries, negative and positive emotions. Future studies should measure and compare the impact of rural and urban populations on these indices of poor mental health. Finally, many of the scales used were not standardized. However, as internal consistencies were acceptable, this study adds potential new measures for future studies of young people in the Indian context.

Conclusions

Our study showed that even though a handful of participants had personal experiences of or knew someone who had been infected by COVID-19, all our participants reported considerable impact of the pandemic on various aspects of their life, which was linked to higher negative affectivity. Adolescents also expressed worries about their studies, physical health and safety as well as social and recreational activities, with some gender differences. While our findings are unable to demonstrate causality between the impact of these COVID-19 related changes and worries, negative affect and anhedonia, nonetheless, the findings highlight the urgent need for government policy makers to take concrete steps to mitigate potential adverse effects of the pandemic on the mental health of Indian adolescents.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Ethics Committee, Institute of Medical Sciences, Banaras Hindu University (Ref No.: Dean/2020/EC/1975); King's College London Research Ethics Committee (Ref: HR-19/20-18250). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

JL, MS, VK, RP, TH, LR, and TS contributed to the conception and design of the study. RP, TS, JL, VK, and MS contributed to the development of study materials, contributed to analysis, and interpretation of study data. MS and TS contributed to acquisition of study data. MS and JL wrote first draft of the paper. VK, RP, TS, TH, and LR critiqued the output for important intellectual content. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: COVID-19, young people, India, worries, emotions

Citation: Shukla M, Pandey R, Singh T, Riddleston L, Hutchinson T, Kumari V and Lau JYF (2021) The Effect of COVID-19 and Related Lockdown Phases on Young Peoples' Worries and Emotions: Novel Data From India. Front. Public Health 9:645183. doi: 10.3389/fpubh.2021.645183

Received: 11 January 2021; Accepted: 26 April 2021; Published: 20 May 2021.

Reviewed by:

Copyright © 2021 Shukla, Pandey, Singh, Riddleston, Hutchinson, Kumari and Lau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jennifer Y. F. Lau, jennifer.lau@kcl.ac.uk ; Tushar Singh, tusharsinghalld@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Getting ahead of coronavirus: Saving lives and livelihoods in India

The COVID-19 pandemic is the defining global health crisis of our time and the greatest global humanitarian challenge the world has faced since World War II. The virus has spread widely, and the number of cases is rising daily as governments work to slow its spread. India has moved quickly, implementing a proactive, nationwide, 21-day lockdown, with the goal of flattening the curve and using the time to plan and resource responses adequately.

Along with an unprecedented human toll, COVID-19 has triggered a deep economic crisis. The global economic impact could be broader than any that we have seen since the Great Depression. 1 In the full briefing materials accompanying Matt Craven, Linda Liu, Mihir Mysore, Shubham Singhal, Sven Smit, and Matt Wilson, “ COVID-19: Implications for business ,” March 2020, McKinsey’s estimates of the global economic impact of COVID-19 suggest that global GDP in 2020 could contract at 1.8 percent and 5.7 percent in scenarios A3 and A1, respectively. This means that India will face a corresponding shrinkage in global demand for its exports in addition to its domestic-production and -consumption challenges. To understand the probable economic outcomes and possible interventions, McKinsey spoke with more than 600 leaders, including senior economists, financial-market experts, and policy makers, in 100 companies across multiple sectors. Based on these inputs, we modeled estimates for three economic scenarios in India (Exhibit 1). 2 The economic scenarios for India are broadly based on McKinsey’s global scenarios in “ COVID-19: Implications for business ,” March 2020, tailored to the Indian situation. All estimates are directional rather than accurate projections or forecasts, and they will evolve over time with new data, inputs, and analysis.

In scenario 1, the economy could contract by about 10 percent in the first quarter of fiscal year 2021, with GDP growth of 1 to 2 percent in fiscal year 2021. In this scenario, the lockdown would be relaxed after April 15, 2020 (when the 21-day deadline is due to expire), with appropriate protocols put in place for the movement of goods and people after that. Our economic modeling suggests that even in this scenario of relatively quick rebound, the livelihoods of eight million workers, including many who are in the informal workforce, could be affected. In other words, eight million people could have their ability to subsist and afford basic necessities, such as food, housing, and clothing, put at severe risk. And with corporate and micro-, small-, and medium-size-enterprise (MSME) failure, nonperforming loans (NPLs) in the financial system could rise by three to four percentage points of loans. The amount of government spending required to protect and revive households, companies, and lenders could therefore be in the region of 6 lakh crore Indian rupees (around $79 billion), or 3 percent of GDP.

In scenario 2, the economy could contract sharply by around 20 percent in the first quarter of fiscal year 2021, with –2 to –3 percent growth for fiscal year 2021. Here, the lockdown would continue in roughly its current form until mid-May 2020, followed by a very gradual restarting of supply chains. This could put 32 million livelihoods at risk and swell NPLs by seven percentage points. The cost of stabilizing and protecting households, companies, and lenders could exceed 10 lakh crore Indian rupees (exceeding $130 billion), or more than 5 percent of GDP.

Scenario 3 could mean an even deeper economic contraction of around 8 to 10 percent for fiscal year 2021. This could occur if the virus flares up a few times over the rest of the year, necessitating more lockdowns, causing even greater reluctance among migrants to resume work, and ensuring a much slower rate of recovery.

Robust measures to stabilize and support households, businesses, and the financial system

Assuming scenario 2 plays out, the potential economic loss in India would vary by sector, with current-quarter output drops that are large in sectors such as aviation and lower in sectors such as IT-enabled services and pharmaceuticals (Exhibit 2). Current-quarter consumption could drop by more than 30 percent in discretionary categories, such as clothing and furnishings, and by up to 10 percent in areas such as food and utilities. Strained debt- service-coverage ratios would be anticipated in the travel, transport, and logistics; textiles; power; and hotel and entertainment sectors.

There could be solvency risk within the Indian financial system, as almost 25 percent of MSME and small- and medium-size-enterprise (SME) loans could slip into default, compared with 6 percent in the corporate sector (although the rate could be much higher in aviation, textiles, power, and construction) and 3 percent in the retail segment (mainly in personal loans for self-employed workers and small businesses). Liquidity risk would also need urgent attention as payments begin freezing in the corporate and SME supply chains. Attention will need to be given to the liquidity needs of banks and nonbanks with stretched liquidity-coverage ratios to ensure depositor confidence.

Given the magnitude of potential unemployment, business failure, and financial-system risk, a comprehensive package of fiscal and monetary interventions may need to be planned, keeping scenario 2 in mind. This might be triggered progressively as situations evolve and as actions are taken to move to the more favorable scenario 1 through effective public-health measures and graded lockdowns.

Further fiscal-, monetary-, and structural-measure possibilities

Several measures have already been announced to provide liquidity, limit the immediate NPL impact, and ease personal distress for needy households in India. These amount to around 0.8 percent of GDP. Additional measures could be considered to the tune of 10 lakh crore Indian rupees, or more than 5 percent of GDP in fiscal year 2021. All the estimated requirements may not necessarily be reflected in the fiscal deficit of the current year—for example, some support may be structured as contingent liabilities that only get reflected when they devolve. However, a package of this order of magnitude may be essential in supporting those dealing with the possible steep declines in aggregate demand and in protecting the financial system from the possible solvency and liquidity risks arising from stressed companies if scenario 2 or scenario 3 plays out.

Household demand could then be boosted beyond the support provided to needy households that the Indian government has already announced. Consideration could be given to an income-support program in which the government both pays for a share of the payroll for the 60 million informal contractual and permanent workers linked to companies and provides direct income support for the 135 million informal workers who are not on any form of company payroll. India’s foundational digital-identity infrastructure, Aadhaar, enables effective mechanisms for direct support, including through the Pradhan Mantri Jan-Dhan Yojana (PMJDY) and Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) programs and to landless Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) beneficiaries. Concessions for home buyers, such as tax rebates for a time-bound period, could stimulate the housing market and unlock the job multiplier.

For bankruptcy protection and liquidity support, MSMEs could receive liquidity lines from their banks, refinanced by the Reserve Bank of India and a loan program for first-time borrowers could be administered through SIDBI. 3 Small Industries Development Bank of India. Substantial credit backstops from the government could be instituted for likely new NPLs Timely payments to MSMEs by large companies and governments could be encouraged by promoting bill discounting on existing platforms.

For large corporations, banks could be allowed to restructure the debt on their balance sheets, and procedural requirements for raising capital could be made less onerous. The Indian government could consider infusing capital through a temporary Troubled Asset Relief (TARP)-type program (such as through preferred equity) in a few distressed sectors (such as travel, logistics, auto, textiles, construction, and power), with appropriate conditions to safeguard workers and MSMEs in their value chains. Banks and nonbanks may also require similar measures to help strengthen their capital, along with measures to step up their liquidity and the liquidity in corporate-bond and government-securities markets.

To manage the macroeconomic consequences of a large stabilization package, the government would also need to consider clearly communicating to the markets and population that these measures are deep but temporary. Given that India’s fiscal resources are constrained, the Reserve Bank of India may need to finance a portion of such incremental government spending. The spending could be tracked as a COVID-19 portion of the budget to boost transparency. The inflationary effects may be low, as lockdowns severely constrict demand and the fiscal support provided would be a substitute for expenditure rather than additional stimulus. Price increases could, however, occur in some sectors, such as food, so appropriate steps would be needed to maintain harvests and keep the food supply chain operating smoothly.

Overall, devising a credible, systemwide, stabilization package would benefit from being executed in a timely fashion so it can influence the pace of recovery and help avoid severe damage to livelihoods, the economy, the financial sector, and society. Many global economies are also facing these issues and having to put in place their own stabilization packages, with similar intent.

Following the first wave of stabilization measures, attention could shift to implementing the structural reforms needed to increase investment and productivity, create jobs quickly, and improve fiscal health. This could mean introducing further reforms in infrastructure and construction and accelerating investments in health, affordable housing, and other urban infrastructure. States could accelerate spending, and institutions such as NIIF 4 National Investment and Infrastructure Fund. could deploy domestic and long-term foreign capital faster. Such reforms could also enable Make in India sectors to become globally competitive and boost exports (such as electronics, textiles, electric vehicles, and food processing), strengthen the financial sector, deepen household financial savings and capital markets, and accelerate asset monetization and privatization to raise resources.

Emergence from lockdown, safeguarding both lives and livelihoods

Countries that are experiencing COVID-19 have adopted different approaches to slow the spread of the virus. Some have tested extensively, carried out contact tracing, limited travel and large gatherings, encouraged physical distancing, and quarantined citizens. Others have implemented full lockdowns in cities with high infection rates and partial lockdowns in other regions, with strict protocols in place to prevent infections.

The pace and scale of opening up from lockdown for India may depend on the availability of the crucial testing capabilities that will be required to get a better handle on the spread of the virus, granular data and technology to track and trace infections, and the build-up of healthcare facilities to treat patients (such as hospital beds by district). In parallel, protection protocols, cocreated with industry, could be designed for different settings (such as mandis [rural markets], construction sites, factories, business-process-outsourcing [BPO] companies, urban transit, and rural–urban labor movement). As an example, industrial areas (such as Baddi, Vapi, and Tirupur) could be ring-fenced and made safe, with local dormitories set up for the labor force and minimal, controlled movement in and out of the site allowed. There could be on-site testing at factories and staggered shifts for workers. While the principles may be the same for construction sites and BPO companies, the specifics would differ.

A geographic lens could be overlaid to determine how quickly the lockdown could be lifted when new protection protocols are in place. Red, yellow, and green zones could be earmarked based on unambiguous criteria, with clear rules for economic activity, entry, and exit. The classification of areas could be updated frequently as the situation evolves. The definition of a “zone” would need to be granular (such as by ward, colony, and building cluster) to allow as much economic activity as is safely possible while targeting infection as accurately as possible. Since there is a very real possibility of the virus lingering on through the year, this microtargeting approach could help decelerate its spread while keeping livelihoods going.

The alternative approach of opening up select industry chains would be less feasible, given that sectors are tightly intertwined. A textile-export factory, for instance, would require chemicals for processing, paper and plastic for packaging, spare parts for its sewing machines, and consumables such as thread. Segregating industrial establishments by size would also be difficult, since smaller suppliers are often bound to the larger manufacturers.

Actions would need to be implemented locally, with different approaches for districts based on their characteristics (such as rural versus urban, industrial versus service oriented, strong versus weak healthcare infrastructure, and heavily infected versus not infected yet). India could consider using the last week of the current lockdown to gear up for local execution, equipping more than 700 of the most appropriate government officers with insights gained from across the world and from ongoing efforts in cities such as Mumbai and states such as Kerala, which are currently fighting the pandemic.

As part of a set of options to consider, based on prior lessons learned in India from repurposing and redeployment of needed skills and expertise for nationwide efforts, such as after floods and natural calamities, these officers could potentially be deputed to work with the district magistrates (DMs) in each district. They could cooperate in dynamically developing and helping execute locally tailored healthcare-expansion efforts, local- or state-level lockdown timetables, and back-to-work protocols. The DMs and deputized officers in districts could potentially be supported by cross-functional centers of excellence (COEs) in states or at the center. These COEs would have medical, administrative, social, economic, and business experts using their considerable knowledge to collect best practices, conduct rapid analysis, and provide valuable suggestions and recommendations to the districts to ensure high-quality implementation.

It is imperative that society preserve both lives and livelihoods. To do so, India can consider a concerted set of fiscal, monetary, and structural measures and explore ways to return from the lockdown that reflect its situation and respect that most important of tenets: the sanctity of human life.

Rajat Gupta is a senior partner and Anu Madgavkar is a partner in McKinsey’s Mumbai office.

The authors wish to thank the leaders of McKinsey India, particularly Kanmani Chockalingam, Vikram Kapur, Alok Kshirsagar, Akash Lal, Renny Thomas, and Hanish Yadav, for their contributions to this article. They also wish to thank Rakesh Mohan—a senior fellow at Yale University’s Jackson Institute for Global Affairs, external adviser to McKinsey Global Institute, and former deputy governor of the Reserve Bank of India—for his contributions to this article.

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COVID-19 in India: Statewise Analysis and Prediction

Palash ghosh.

1 Department of Mathematics, Indian Institute of Technology, Guwahati, India

2 Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore

Bibhas Chakraborty

3 Centre for Quantitative Medicine & Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore

4 Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore

5 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States

Associated Data

Supplementary material.

The highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast.

Most of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources.

Since predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled.

We found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R 2 values corresponding to the logistic and the exponential models are above 0.90, indicating a reasonable goodness of fit. We also provide a web application to see the forecast based on recent data that is updated regularly.

Conclusions

States with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

Introduction

The world is now facing an unprecedented crisis due to the novel coronavirus, first detected in Wuhan, China in December 2019 [ 1 ]. The World Health Organization (WHO) defined coronavirus as a family of viruses that range from the common cold to the Middle East respiratory syndrome coronavirus and the severe acute respiratory syndrome coronavirus [ 2 ]. Coronaviruses circulate in some wild animals and have the capability to transmit from animals to humans. These viruses can cause respiratory symptoms in humans, along with other symptoms of the common cold and fever [ 3 ]. There are no specific treatments for coronaviruses to date. However, one can avoid infection by maintaining basic personal hygiene and social distancing from infected persons.

The WHO declared the coronavirus disease (COVID-19) as a global pandemic on March 11, 2020 [ 4 ]. The disease has spread across 212 countries and territories around the world, with a total of more than 3 million confirmed cases [ 5 , 6 ]. In India, the disease was first detected on January 30, 2020, in Kerala in a student who returned from Wuhan [ 7 , 8 ]. The total (cumulative) number of confirmed infected people is more than 37,000 to date (May 3, 2020) across India. The bar chart in Figure 1 shows the daily growth of the COVID-19 cases in India. After the first 3 cases from January 30 to February 3, 2020, there were no confirmed COVID-19 cases for about a month. The COVID-19 cases appeared again from March 2, 2020, onwards. These cases are related to people who have been evacuated or have arrived from COVID-19–affected countries. From March 20, 2020, onwards, there is an exponential growth in the daily number of COVID-19 cases at the pan-India level.

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Bar chart of daily infected cases (blue) in India. Red bar denotes death. The black curve is a fitted smooth curve on the daily cases.

There are four stages of COVID-19 depending on the types of virus transmission [ 9 , 10 ]. During the first stage, a country or region experiences imported infected cases with travel history from virus-hit countries. During the second stage, a country or region gets new infections from persons who did not have a travel history but came in contact with persons defined in stage 1. Stage 3 is community transmission; in this period, new infection occurs in a person who has not been in contact with an infected person or anyone with a travel history of virus-hit countries. At stage 4, the virus spread is practically uncontrollable, and the country can have many major clusters of infection.

Many news agencies are repeatedly saying or questioning whether India is now at stage 3 [ 9 , 11 , 12 ]. In reality, different Indian states are or will be at various stages of infection at different points in time. Labeling a COVID-19 stage at the pan-India level is problematic. It will spread misinformation to common people. Those states that are at stage 3 require more rapid action compared to others. On the other hand, states that are in stages 1 and 2 need to focus on stopping the community spread of COVID-19.

In this paper, we first discuss the importance of statewise consideration, contemplating all the states together. Second, we will focus on the infected people in each state (considering only those states with enough data for prediction) and build growth models to predict infected people for that state in the next 30 days.

Why Statewise Consideration?

India is a vast country with a geographic area of 3,287,240 square kilometers and a total population of about 1.3 billion [ 13 ]. Most of the Indian states are quite large in geographic area and population. Analyzing coronavirus infection data, considering the entirety of India to be on the same page may not provide us the right picture. This is because the first infection, new infection rate, progression over time, and preventive measures taken by state governments and the common public for each state are different. We need to address each state separately. It will enable the government to use the limited available resources optimally. For example, currently, Maharashtra already has more than 10,000 confirmed infected cases, whereas West Bengal has less than 800 confirmed cases (May 1, 2020). The approaches to addressing the two states must be different due to limited resources. One way to separate the statewise trajectories is to look at when each state was first infected.

In Figure 2 , we present the first infection date along with the infected person’s travel history in each of the Indian states. All the states and the union territories, except Assam, Tripura, Nagaland, Meghalaya, and Arunachal Pradesh, observed their first confirmed infected case from a person who had travel history from one or more already COVID-19–infected countries. The Indian government imposed a complete ban on international flights to India on March 22, 2020 [ 14 ]. Figure 2 justifies government action to international flight suspension. Had it been taken earlier, we could have restricted the disease to only a few states compared to the current scenario.

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When the first case in each state happened with their travel histories. UAE: United Arab Emirates.

Figure 3 shows the curve of the cumulative number of infected people in those Indian states having at least 10 total infected people. Currently, Maharashtra, Delhi, Gujarat, Tamil Nadu, Madhya Pradesh, Rajasthan, and Uttar Pradesh are the states where the cumulative number of infected people have crossed the 2000 mark, with Maharashtra having more than 10,000 cases. Kerala, the first state to have a COVID-19 confirmed case, seems to have restricted the growth rate. There are few states with cumulative infected people in the range of 500-1500. Depending on how those states strictly follow the preventive measures, we may see a rise in the confirmed cases.

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Cumulative number of infected people over time in states with at least 10 infected cases.

Preventive Measures

In Textbox 1 , we list the major preventive measures taken by the Indian Government [ 15 ].

List of major preventive measures taken by the Indian Government.

January 25-March 13, 2020

Health screenings at airports and border crossings

February 26-March 20, 2020

Introduction of quarantine policies: gradually for passengers coming from different countries

February 26-March 13, 2020

Visa restrictions: gradually for different countries

March 5, 2020

Limit public gatherings (closure of some selected public institutions like museums, religious places, and postponing of several local elections to stop public gatherings)

March 11, 2020

Border checks

March 13-15, 2020

Border closure

March 16, 2020

Limit public gatherings (ban on all sorts of public gatherings and meetings, and stopping people from making any congregation)

March 18, 2020

Travel restrictions

March 20, 2020

Testing for the coronavirus disease (before this point, only people who had traveled from abroad were tested; this point onwards, testing was also introduced for symptomatic contacts of laboratory-confirmed cases, symptomatic health care workers, and all hospitalized patients with severe acute respiratory illness)

March 22, 2020

Flight suspensions

Cancellation of passenger train services until March 31, 2020

March 24, 2020

Suspension of domestic airplane operations

March 25, 2020

21-day lockdown of entire country

Cancellation of passenger train services extended to April 14, 2020

March 30, 2020

Increase of quarantine/isolation facilities

April 14, 2020

Extension of lockdown until May 3, 2020

May 1, 2020

Extension of lockdown until May 17, 2020

Data Source

We have used Indian COVID-19 data available publicly. The three primary sources of the data are the Ministry of Health and Family Welfare, India [ 16 ]; COVID-19 India [ 17 ]; and Wikipedia [ 18 ].

Statistical Models

In this paper, we consider the exponential model, the logistic model, and the susceptible-infectious-susceptible (SIS) model for COVID-19 pandemic prediction at the state level. These models have already been used to predict epidemics like COVID-19 around the world, including in China, and for the Ebola outbreak in Bomi, Liberia in 2014 [ 19 - 21 ]. See Multimedia Appendix 1 [ 20 - 22 ] for details about the models.

Using the Models in State-Level Data

The previously mentioned three models will provide a different prediction perspective for each state. The exponential model–based prediction will give a picture of what could be the cumulative number of infected people in the next 30 days if we do not take any preventive measures. We can consider the forecast from the exponential model as an estimate of the upper bound of the total number of infected people in the next 30 days. The logistic model–based prediction will capture the effect of preventive measures that have already been taken by the respective state governments as well as the central government. The logistic model assumes that the infection rate will slow down in the future with an overall “S” type growth curve. In other words, the logistic model tries to explore a situation where there is a full lockdown in the country, leading to an extreme restraint on the people’s movement, hence reducing the rate of infection considerably. Under the effective implementation of the lockdown, it is appropriate to use a logistic model. In this scenario, many people have already been infected; the virus may find it hard to spot more susceptible people. Thus, the virus slows down its spread, causing the flattening in the S-curve at a later stage. Several research papers have used the logistic model in the context of COVID-19 [ 23 - 26 ].

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Study the Effect of Lockdown Using the Daily Infection Rate and SIS Model

Kumar et al [ 31 ] reported the estimated number of people that a person may come in contact with within a day (24 hours) in a rural community in Haryana, India to be 17. They defined contact as having a face-to-face conversation within 3 feet, which may or may not have included physical contact. The estimate of the contact-rate parameter from their paper is 0.70. In practice, only some of all the people who come in contact with a person infected with COVID-19 may be actually infected by the virus. Note that India has already taken many preventive measures to ensure social distancing. In the current scenario, the infection rate based on Kumar et al’s [ 31 ] study could be an overestimate of its present value. However, despite nationwide lockdown, banks, hospitals, and grocery stores are still open to cater to the essential needs of people. We consider here two approaches to study the effect of lockdown and other preventive measures jointly in each state. First , we plot the daily infection rates (DIRs) for each state. The DIR for a given day is defined as:

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India implemented a nationwide lockdown on March 25, 2020. We first considered the incubation period of the novel coronavirus to study the effect of the lockdown. The incubation period of an infectious disease is defined as the time between infection and the first appearance of signs and symptoms [ 32 ]. Using the incubation period, health researchers can decide on the quarantine periods and halt a potential pandemic without the aid of a vaccine or treatment [ 33 ]. The estimated median incubation period for COVID-19 is 5.1 (95% CI 4.5-5.8) days, and 97.5% of those who develop symptoms will do so within 11.5 (95% CI 8.2-15.6) days of infection [ 34 ]. The WHO recommends that a person with laboratory-confirmed COVID-19 be quarantined for 14 days from the last time they were exposed to the patient [ 35 ]. Therefore, if a person was infected before the lockdown (March 25, 2020), they should not infect others except their family members if that person is entirely inside their house for more than 14 days. The WHO also recommends common people to maintain a distance of at least 1 meter from each other in a public place to avoid COVID-19 infection. The effective implementation of social distancing can stop the spread of the virus from an infected person, even when they are outside for some essential business. However, given a highly dense population in most of India, particularly in cities, it may not always be possible to maintain adequate social distance.

Statewise Analysis and Prediction Report

In this section, we depend on inputs from the exponential, logistic, and SIS models along with DIRs for each state. Remembering the words of the famous statistician George Box “All models are wrong, but some are useful,” we interpreted the results from different models jointly. We consider different states with at least 300 cumulative infected cases. For each state, we present four graphs. We have used the state-level data until May 1, 2020. The first and second graphs are based on the logistic and the exponential models, respectively, with the next 30-day predictions. The third graph is the plot of DIRs for a state. Finally, the fourth graph is showing the growth of the active infected patients using SIS model prediction ( “pred” ) along with the observed active infected patients. Table 1 represents the 30-day prediction of the cumulative infected number of people for each state using the logistic model, the exponential model, and a data-driven combination of the two. The corresponding measures of goodness of fit ( R 2 and deviance) are presented in the table in Multimedia Appendix 1 .

Data-driven assessment and 30-day prediction using the logistic and exponential models, and their linear combination.

State	Observed cumulative cases (May 1, 2020)	Maximum DIR in the last 2 weeks	Estimated R from SIS model (data until May 1, 2020)	Data driven assessment of COVID-19 situation	30-day prediction (May 31, 2020)			Observed cumulative cases (May 31, 2020)	Assessment of observed cumulative cases with respect to (LC , exponential)
					Logistic	Linear combination of logistic and exponential (LC )	Exponential (applicable only if the situation is severe)
Andhra Pradesh	1463	0.17	3.22	Severe	2313	4725	16,502	3571	Below
Bihar	426	0.39	3.08	Moderate	16,452	16,472	16,502	3807	Below
Delhi	3515	0.17	2.94	Severe	4262	9650	35,957	19,844	Between
Gujarat	4395	0.27	3.50	Severe	5206	33,736	110,874	16,794	Below
Haryana	313	0.18	1.82	Controlled	321	590	1815	2091	Above
Jammu and Kashmir	614	0.09	2.66	Controlled	724	1124	5170	2446	Between
Karnataka	576	0.06	2.38	Controlled	3711	3711	3713	3221	Below
Kerala	497	0.18	1.96	Controlled	455	740	2040	1270	Between
Madhya Pradesh	2719	0.10	3.36	Severe	3030	6521	37,935	8089	Between
Maharashtra	10,498	0.15	3.50	Severe	17,115	43,963	196,103	67,655	Between
Punjab	357	0.14	2.52	Moderate	419	713	2517	2263	Between
Rajasthan	2584	0.12	2.94	Moderate	2821	6125	30,356	8831	Between
Tamil Nadu	2323	0.12	3.22	Moderate	2241	3967	16,624	22,333	Above
Telangana	1039	0.09	2.66	Controlled	1063	1631	7373	2698	Between
Uttar Pradesh	2281	0.13	2.52	Severe	3016	6566	30,326	8075	Between
West Bengal	795	0.17	3.22	Severe	1261	3225	12,815	5501	Between

a DIR: daily infection rate.

b R 0 : basic reproduction number.

c SIS: susceptible-infectious-susceptible.

d COVID-19: coronavirus disease.

e LC pred : linear combination prediction.

Maharashtra

The situation in Maharashtra is currently very severe with respect to the active number of cases (see Figure 4 ). As of May 1, 2020, the total number of infected cases is 10,498. The logistic model indicates that, in another 30 days from now, the state could observe around 17,100 cumulative infected cases. The DIRs for this state were between 0.03 and 0.15 in the last 2 weeks, and it was more than 0.4 for 2 days at the beginning of April. Note that, for Maharashtra, the lower DIR values of 0.03 may not indicate a good sign since the total number of active infected cases is above 8000. Thus, a DIR value of 0.03 for a day implies 8000 x 0.03 = 240 new infected cases. The curves from the SIS model are alarming as the observed active infected patients (red line, fourth panel) line is far above the predicted line with estimated infection rate at the 80th percentile of observed DIRs (β=0.22). It is apparent from the graphs that even after 30 days of lockdown, Maharashtra has not seen any decline in the number of active cases. The estimated R 0 for Maharashtra obtained from the fitted SIS model is 3.5, which is the highest among all the states. This may also indicate that there could be a large number of people who are in the community without knowing that they are carrying the virus. The state can be considered to be in stage 3.

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Graphs for the state of Maharashtra. SIS: susceptible-infectious-susceptible.

Delhi, being a state of high population density, has already observed 3515 confirmed COVID-19 cases (see Figure 5 ). Based on the logistic model, the predicted number of cumulative infected cases could reach around 4200 in the next 30 days. The DIR has not seen a downward trend in the past few days. The curve (red line, fourth panel) of observed active infected patients was showing a downward trend from April 20 to April 23, 2020. However, the same graph has picked up exponential growth in the last few days. This is an important observation that illustrates why we need a continuous downward trend of active cases for at least 14 days and that a slight relaxation may put a state in the same severe condition where it was earlier. The estimated R 0 for the state obtained from the fitted SIS model being 2.94 is quite alarming. The observed DIR has been currently fluctuating between –0.06 and 0.17 in the last 2 weeks. The occasional high DIR may suggest that there could be many people who are in the community without knowing that they are already infected with COVID-19. The state could be heading to community spread of COVID-19 (stage 3).

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Graphs for the state of Delhi. SIS: susceptible-infectious-susceptible.

The cumulative infected cases in Tamil Nadu is 2323 (see Figure 6 ). The state has observed a high DIR of more than 0.7 for some days in March. Tamil Nadu is one of the states where the effect of lockdown is visible from the declining DIRs from the beginning to the end of April. However, there was again an increasing trend in DIR over the last 3 days. The DIRs were between –0.13 and 0.12 over the previous 2 weeks. The latter part of the curve (red line, fourth panel) of observed active infected patients is showing a decreasing trend first but then an increasing trend again. The estimated R 0 for this southern state obtained from the fitted SIS model is 3.22, which is quite high. The preventive measures need to be maintained to bring down the active cases as well as to stop new infections in this state.

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Graphs for the state of Tamil Nadu. SIS: susceptible-infectious-susceptible.

Madhya Pradesh

This state currently has 2719 cumulative COVID-19 cases (see Figure 7 ). In the later part of the lockdown, after April 10, 2020, the state observed a few days with a DIR more than 0.4. Until now, there is no sight of a declining trend in the DIRs. The same type of conclusion can be drawn from the curves of the SIS model. The curve (red line, fourth panel) of observed active infected patients is in between the curves of the SIS model corresponding to the 50th-75th percentiles’ curves. The same curve is maintaining an exponential growth after April 10. Note that, for Madhya Pradesh, the 50th percentile of observed DIRs was 0.14, which is higher than the 50th percentile of some other states. The estimated R 0 for this state obtained from the fitted SIS model was 3.36, which is pretty high. The high growth of active cases in the latter part of the lockdown is a major concern for this state. It could be a signal of a community spread of COVID-19.

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Graphs for the state of Madhya Pradesh. SIS: susceptible-infectious-susceptible.

The western state of India, Rajasthan, reported 2584 cumulative infected COVID-19 cases (see Figure 8 ). The logistic model indicates that in another 30 days from now, the state could observe around 2800 cumulative infected cases. The state has seen a declining trend in the DIRs during the last part of April. The curve (red line, fourth panel) of observed active infected patients is increasing and is in between the curves of the SIS model corresponding to the 50th-75th percentiles of observed DIRs (0.14-0.27) using the SIS model. In the last 2 weeks, the DIRs for Rajasthan have been fluctuating between –0.05 and 0.12. The active cases in this state have not increased too much in the latter part of April. An increase in recovery cases is one of the reasons. The estimated R 0 for Rajasthan obtained from the fitted SIS model was 2.94. Therefore, the current COVID-19 situation in the state is not controlled yet.

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Graphs for the state of Rajasthan. SIS: susceptible-infectious-susceptible.

The state is currently experiencing exponential growth with 4395 as the cumulative number of COVID-19 cases (see Figure 9 ). Using the logistic model, the cumulative infected cases could reach around 5206 in the next 30 days. There is apparently a stable rather than a declining trend in the DIRs in the last few days. The DIRs were in the range of 0.03-0.27 in the last 2 weeks, which are on the higher side. The curve (redline, fourth panel) of observed active infected patients is close to the curve of the SIS model corresponding to the estimated 75th percentile of observed DIR (β=.26). Surprisingly, in the latter part of the lockdown, the red line is experiencing exponential growth. The estimated R 0 for Gujarat obtained from the fitted SIS model was 3.5, which is one of the highest. This state needs immediate intervention to implement all the preventive measures already taken by the Government strictly.

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Graphs for the state of Gujarat. SIS: susceptible-infectious-susceptible.

Uttar Pradesh

This northern state of India has experienced 2281 cumulative COVID-19 cases (see Figure 10 ). Using the logistic model, the predicted number of cumulative confirmed cases could be around 3000 in the next 30 days. The curve (red line, fourth panel) of observed active infected patients was in between the curves of the SIS model corresponding to the 50th and 75th percentiles of observed DIRs (β=0.12 and 0.23, respectively). The DIR was in the range of –0.02 to 0.13 without a moderately decreasing trend in the last 2 weeks. The overall growth of active cases was still exponential, which is a major concern for the state. The estimated R 0 for the state obtained from the fitted SIS model was 2.52. There could be many unreported cases in the state. In the absence of preventive measures, unreported cases can contribute to spreading the virus in the community.

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Graphs for the state of Uttar Pradesh. SIS: susceptible-infectious-susceptible.

The southern Indian state of Telangana has reported 1039 cumulative infected cases until now (see Figure 11 ). The logistic model predicts that the number of cases for the state will be around 1063 in the next 30 days. In the fourth graph, the curve (red line, fourth panel) shows that the active number of cases has continuously remained below the curve of the SIS model corresponding to the 75th percentile of the observed DIRs (β=0.25). The estimated R 0 for Telangana obtained from the fitted SIS model was 2.66. From April 23, 2020, onwards, there is a visible downward trend in the same line graph. This evidence is also supported by a clear decreasing trend in the DIR for more than 2 weeks. The state is going in the right direction to control the COVID-19 pandemic. However, preventive measures need to be in place to see long-term success against the virus.

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Graphs for the state of Telangana. SIS: susceptible-infectious-susceptible.

Andhra Pradesh

This state has observed 1463 confirmed cumulative infected cases so far (see Figure 12 ). The curve (red line, fourth panel) shows that the number of active cases is now below and close to the curve of the SIS model corresponding to the 75th percentile of the observed DIR (β=0.23). The logistic model predicted that the maximum number of cumulative infected people will be around 2313 in the next 30 days. Despite showing good progress in mid-April, the state is again showing an exponential type growth rate. This state has seen DIRs between –0.04 and 0.17 during the last 2 weeks. The estimated R 0 for this state obtained from the fitted SIS model was 3.22, which is quite high. The state has shown a few short declining trends, without any long-term declining trend in the DIR values. It could be due to many unreported infected cases in the community that is spreading the virus.

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Graphs for the state of Andhra Pradesh. SIS: susceptible-infectious-susceptible.

The southern state of Kerala is one of the few states of India where the effect of the lockdown is observed strongly. The state reported the first COVID-19 case in India. However, Kerala has been able to control the spread of the virus to a large extent to date. The cumulative number of cases reported until now is 497 (see Figure 13 ). It is a state where the curve (red line, fourth panel) of observed active infected patients is going down, which shows that the lockdown and other preventive measures have been effective for this state. The DIR has declined steadily from positive to negative values. However, some spikes in the DIR values can be noticed in the last few days. The estimated R 0 for Kerala obtained from the fitted SIS model was 1.96, which is quite low compared to other states. It can be expected that with the present scenario of the extended lockdown the number of active cases will be few at the end of May.

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Graphs for the state of Kerala. SIS: susceptible-infectious-susceptible.

The state has managed to restrict the cumulative infected cases to 576 until now (see Figure 14 ). The curve (red line, fourth panel) of observed active infected patients is now below the curve of the SIS model corresponding to the 75th percentile of the observed DIRs (β=0.18). Compared to other states, the 75th percentile DIR is on the lower side. The estimated R 0 for the state obtained from the fitted SIS model was 2.38. We can observe the ups and downs of the DIR with an upper bound of 0.2 from early April. This state has seen DIRs between –0.04 and 0.06 during the last 2 weeks. However, the preventive measures need to be maintained to control the spread of the virus.

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Graphs for the state of Karnataka. SIS: susceptible-infectious-susceptible.

Jammu and Kashmir

The northernmost state of Jammu and Kashmir has seen 614 cumulative infected cases so far (see Figure 15 ). The curve (red line, fourth panel) of observed active infected patients has been far below the curve of the SIS model corresponding to the 75th percentile of the observed DIR (β=0.35). The estimated R 0 for the state obtained from the fitted SIS model was 2.66. From April 9, 2020, onwards, the DIR was apparently decreasing. There are some spikes in DIR values occasionally. It could be due to many unreported cases, which are allowing the infection to spread even during the lockdown period. The DIR was in the range of –0.02 to 0.09 in the last 2 weeks.

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Graphs for the state of Jammu and Kashmir. SIS: susceptible-infectious-susceptible.

West Bengal

The state of West Bengal is standing at 795 cumulative infected cases as of now (see Figure 16 ). The DIR values do not show any trend of slowing down in recent times. Based on the logistic model, the predicted cumulative infected cases could be around 1261 in the next 30 days. The curve (red line, fourth panel) of observed active infected patients was above the curve of the SIS model corresponding to the 75th percentile of the DIR (β=0.21). The DIRs were between 0.03 and 0.17 in the last 2 weeks. The cumulative infected cases graphs based on logistic and exponential models (first and second panels), as well as the active cases–based curve (red line, fourth panel) were all showing exponential type growth rates. The estimated R 0 for West Bengal obtained from the fitted SIS model was 3.22, which is quite high. Strict implementation of preventive measures is needed to control the spread of COVID-19 in the state.

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Graphs for the state of West Bengal. SIS: susceptible-infectious-susceptible.

The state of Haryana has observed 313 cumulative infected COVID-19 cases so far (see Figure 17 ). It has reported a very low rate of infection in the latter part of the lockdown except for the last reported day. In the fourth panel, the curve (red line) of observed active infected patients is now far below the curve of the SIS model corresponding to the 50th percentile of observed DIRs (β=0.15) and is showing a decreasing trend in the latter part. The estimated R 0 for the state obtained from the fitted SIS model was 1.82, which is on the lower side. The DIRs were between –0.28 and 0.18 in the last 2 weeks. Under the assumption that there are not too many unreported cases, the situation in Haryana seems to be under control.

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Graphs for the state of Haryana. SIS: susceptible-infectious-susceptible.

The state of Punjab has reported 357 cumulative infected cases until now (see Figure 18 ). Based on the logistic model, the predicted cumulative confirmed cases could be around 419 in the next 30 days. The curve (red line) of observed active infected patients was in between the SIS model curves corresponding to the estimated 75th and 80th percentiles of observed DIRs (β=0.15 and 0.28, respectively). The estimated R 0 for Punjab obtained from the fitted SIS model was 2.52. The DIRs were between –0.05 and 0.14 in the last 2 weeks, which is good given the low number of active infected cases in the state.

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Graphs for the state of Punjab. SIS: susceptible-infectious-susceptible.

The state has reported 426 cumulative infected cases until now (see Figure 19 ). Based on the logistic model, Bihar could see 16,452 total infected cases in the next 30 days. The estimated R 0 for the state obtained from the fitted SIS model was 3.08. It may be an overestimate. However, the DIRs showed no sign to decline in the last 2 weeks, with the highest reported value of 0.39. It may indicate many unreported cases in the state. However, the cumulative infected cases are still low for this state. Effective implementation of preventive measures is needed for the state.

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Graphs for the state of Bihar. SIS: susceptible-infectious-susceptible.

Joint Interpretation of Results From all Models

We consider a data-driven assessment of the COVID-19 situation based on the growth of active cases in recent times (red line, fourth panel in each state plot) along with the DIR values for each state (see Table 1 ). We labeled the condition of a state as severe if we observed a nondecreasing trend in DIR values over the last 2 weeks and a near exponential growth in active infected cases, as moderate if we observed an almost decreasing trend in DIR values over the last 2 weeks and neither increasing nor decreasing growth in active infected cases, and as controlled if we observed a decreasing trend in the last 2 weeks’ DIR values and a decreasing growth in active infected cases. It can be noticed that the logistic model is underpredicting the next 30-day prediction, whereas the exponential model is overpredicting the same. As we have argued earlier, despite nationwide lockdown, people are out of their homes for essential businesses, which can contribute to the spreading of the virus. The maximum value of DIR in the last 2 weeks can capture how severely COVID-19 is spreading in recent times. Note that, for example, a DIR value of 0.10 cannot be interpreted in the same way for two different states with, for example, 500 and 5000 active cases. For the first state, we see 500 x 0.10 = 50 new cases, and for the second state, we observe 5000 x 0.10 = 500 new cases. In an attempt to capture these various subtleties in a realistic prediction, we propose a linear combination prediction (LC pred ) of the logistic and the exponential predictions using the maximum value of DIR over the last 2 weeks (DIR max ) as a weighting coefficient (tuning parameter) as follows:

Such a choice of the tuning parameter λ makes the LC pred equal to the logistic prediction when DIR max is negative with λ=0. On the other hand, the LC pred is equal to the exponential prediction when DIR max is more than 1 with λ=1. When DIR max is in between 0 and 1, the LC pred is a combination of the predictions from the logistic and the exponential models. Given the situation in the entirety India, we recommend LC pred along with the exponential predictions (particularly for states in severe condition) to be used for assessment purposes in each state.

Extensive testing may not be logistically feasible given India’s large population and limited health care budget. The undertesting can significantly impact the logistic prediction and less so the exponential prediction since the first one is underforecasting and the second one is overforecasting. The DIR indirectly captures the undertesting phenomenon. Thus, the LC pred with (a truncated version of) DIR as the weight (λ) can be thought of as a treatment for undertesting, albeit in a limited fashion.

From Table 1 , we can see that out of 16 states for which we have predictions, 10 states lay between the linear combination (LC pred ) and the exponential predictions, 4 states are below the LC preds , and 2 states are above the exponential predictions.

India, a country of approximately 1.3 billion people, has reported 17,615 confirmed COVID-19 cases after 80 days (from January 30, 2020) from the first reported case in Kerala [ 36 ]. In a similar duration from the first case, the United States reported more than 400,000 cases, and both Spain and Italy reported more than 150,000 confirmed COVID-19 cases. To gain some more perspective, note that, the United States has around one-fourth of the Indian population size. Therefore, according to the reported data so far, India seems to have managed the COVID-19 pandemic better compared to many other countries. One can argue that India has conducted too few tests compared to its population size [ 37 ]. However, a smaller number of testing may not be the only reason behind the low number of COVID-19–confirmed cases in India so far. India has taken many preventive measures to combat COVID-19 in much earlier stages compared to other countries, including a nationwide lockdown from March 25, 2020. Apart from the lockdown, people have certain conjectures about possible reasons behind India’s relative success (eg, measures like the travel ban relatively early, use of Bacille Calmette-Guerin vaccination to combat tuberculosis in the population that may have secondary effects against COVID-19 [ 38 , 39 ], exposure to malaria and antimalarial drugs [ 40 ], and hot and humid weather slowing the transmission [ 41 , 42 ]). However, as of now, there is no concrete evidence to support these conjectures, although some clinical trials are currently underway to investigate some of these [ 43 ].

Note that India may have seen fewer COVID-19 cases until now, but the war is not over yet. There are many states like Maharashtra, Delhi, Madhya Pradesh, Rajasthan, Gujarat, Uttar Pradesh, and West Bengal who are still at high risk. These states may see a significant increase in confirmed COVID-19 cases in the coming days if preventive measures are not implemented properly. On the positive side, Kerala has shown how to effectively “flatten” or even “crush the curve” of COVID-19 cases. We hope India can limit the spread and impact of COVID-19 with a strong determination in policies as already shown by the central and state governments.

There are a few other works that are based explicitly on Indian COVID-19 data. Das [ 30 ] has used the epidemiological model to estimate the R 0 at national and some state levels. Ray et al [ 44 ] used a predictive model for case counts in India. They also discussed hypothetical interventions with various intensities and provided projections over a time horizon. Both the papers have used the susceptible-infected-recovered model (or some extension) for their analysis and prediction. As we discussed earlier, considering the great diversity in every aspect of India, along with its vast population, it would be a better idea to look at each of the states individually. The study of each of the states individually would help decide further actions to contain the spread of the disease, which can be crucial for the specific states only. In this paper, we have mainly focused on the SIS model along with the logistic and the exponential models at each state (restricting to only those states with enough data for prediction). The SIS model takes into account the possibility that an infected individual can return to the susceptible class on recovery because the disease confers no long-standing immunity against reinfection. In South Korea, the health authorities discovered 163 patients who tested positive again after a full recovery [ 45 , 46 ]. The WHO is aware of these reports of patients who were first tested negative for COVID-19 using polymerase chain reaction testing and then after some days, tested positive again [ 47 ]. In a scientific brief, dated April 24, 2020, the WHO said, “there is currently no evidence that people who have recovered from COVID-19 and have antibodies are protected from a second infection” [ 48 ]. Several research papers have reported that, even though being infected by the virus may build immunity against the disease in the short-term, it is not a guaranteed fact, and it may not be long-lasting protection [ 49 - 51 ].

A report based on one particular model can mislead us. Here, we have considered the exponential, the logistic, and the SIS models along with the DIR. We have interpreted the results jointly from all models rather than individually. We expect the DIR to be zero or negative to conclude that COVID-19 is not spreading in a certain state. Even a small positive DIR such as 0.01 indicates that the virus is still spreading in the community and can potentially increase the DIR anytime. The states without a decreasing trend in DIR and near exponential growth in active infected cases are Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal. The states with an almost decreasing trend in DIR and nonincreasing growth in active infected cases are Tamil Nadu, Rajasthan, Punjab, and Bihar. The states with a decreasing trend in DIR and decreasing growth in active infected cases in the last few days are Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana. States with nondecreasing DIR need to do much more in terms of the preventive measures immediately to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

Based on the modeling approaches presented in this paper, we have developed a web application [ 52 ] to see the Indian statewise forecast based on recent data that is updated regularly. The web application also offers a 30-day prediction of cumulative cases at the pan-India level by summing up the predicted cumulative cases of considered states.

Abbreviations

COVID-19	coronavirus disease
DIR	daily infection rate
DIR	maximum value of daily infection rate over the last 2 weeks
LC	linear combination prediction
R	basic reproduction number
SIS	susceptible-infectious-susceptible
WHO	World Health Organization

Multimedia Appendix 1

Conflicts of Interest: None declared.

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BRIEF RESEARCH REPORT article

Introduction

Participants and General Procedures

Demographics

Personal Experiences of and Knowledge of Close Others With COVID-19

Social Restrictions Associated With COVID-19

Impact of COVID-19

Content of Worries

Negative Affect

Statistical Analyses

Demographic Characteristics

Experiences of COVID-19

Social Restrictions and Impact of COVID-19

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Getting ahead of coronavirus: Saving lives and livelihoods in India

Robust measures to stabilize and support households, businesses, and the financial system

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Emergence from lockdown, safeguarding both lives and livelihoods

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COVID-19 in India: Statewise Analysis and Prediction

Bibhas Chakraborty

Associated Data

Conclusions

Introduction

Why Statewise Consideration?

Preventive Measures

List of major preventive measures taken by the Indian Government.

Data Source

Statistical Models

Using the Models in State-Level Data

Study the Effect of Lockdown Using the Daily Infection Rate and SIS Model

Statewise Analysis and Prediction Report

Maharashtra