key: cord-1049565-va7b8ob0
authors: Babu, Giridhara R.; Ray, Debashree; Bhaduri, Ritwik; Halder, Aritra; Kundu, Ritoban; Menon, Gautam I.; Mukherjee, Bhramar
title: COVID-19 Pandemic in India: Through the Lens of Modeling
date: 2021-06-30
journal: Glob Health Sci Pract
DOI: 10.9745/ghsp-d-21-00233
sha: e9001bd0f880bb057d92b125e63ec389a2e279d2
doc_id: 1049565
cord_uid: va7b8ob0

We reflect on and review India's COVID-19 pandemic response through the lens of modeling and data. The lessons learned from the Indian context may be beneficial for other countries.

I ndia, the world's largest democracy, declared its first confirmed case of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection on January 30, 2020. It currently reports Asia's largest number of coronavirus disease (COVID-19) infections and deaths (27.7 million reported cases and 322,384 reported deaths as of May 28, 2021) . 1 The actual numbers for both infections and deaths likely far exceed what are officially reported.

In the past year, India has devised some innovative strategies aiming to reduce COVID-19 spread within the constraints of a low-resource setting. It has also made some questionable policy decisions. Lessons learned from the Indian experience for public health, health care, and data infrastructure can be globally valuable. In this commentary, as a team of public health data scientists engaged in modeling the pandemic since early 2020, we reflect on India's journey over the past year.

Epidemiological models help public health planners gauge the future predicted trajectory of epidemics, providing forecasts or estimates for the daily number of infections, hospitalizations, and deaths. Models operate under various assumptions. They can incorporate hypothetical intervention scenarios and assess their relative impact on disease transmission. Because they may help us calibrate our expectations and resource needs for the future, predictive models have drawn significant attention from the media and the public. 2 

There have been many models proposed for the spread of COVID-19 in India. These models can be broadly categorized into 2 genres: exponential/Poisson-type regression models and compartmental epidemiological models. For instance, Ranjan 3 and Gupta and Shankar 4 use the classical exponential model on the daily case counts. The compartmental models include variations of the susceptible-infected-removed (SIR) model. Such models are guided by a set of differential equations relating to the number of susceptible people, the number of infected people (cases), and the number of people who have been removed (either recovered or dead) at any given time. One extension of the SIR model is the susceptible-exposed-infected-removed (SEIR) model that incorporates an additional compartment of truly exposed people which is latent or unobserved. Ray et al. 5 provide a summary of these models and their basic assumptions; Sarkar et al. 6 provide an early comprehensive review, and Purkayastha et al. 7 provide a head-tohead comparison of 5 different models for forecasting, with a focus on India. Such models differ in terms of the data they use, ranging from simple case counts to agesex demography, age-specific contact networks, and mobility data. Some, but not all, models are transparent, explicitly stating assumptions, making their code available, and updating their predictions regularly. 5, 8 Such constant recalibration and updating are critical, given that the reality on the ground changes rapidly.

Providing uncertainty estimates for point predictions is also essential since the predictive ability of these models deteriorates rapidly over times longer than a few weeks. Several authors have pointed out that India is heterogeneous. Allowing different state-and district-level model predictions from multiple groups to aggregate toward national-level predictions is a better approach. 9 It would be audacious to claim the superiority of a single model or base public health decisions solely on one. 10 In principle, ensemble methods that average over predictions across multiple models should provide predictions that benefit from aggregated learning. 11 However, for dynamic systems used for modeling the virus transmission, aggregating results from models with diverse assumptions, structure, and inputs may lead to a lack of interpretability.

While projections based on reported daily casecounts have received the most attention, the differential availability of testing and the high rates of false negatives in the rapid antigen tests (30%-40%) and RT-PCR tests (15%-30%) mandate that investigators either explicitly account for selective and imperfect testing or conduct careful sensitivity analysis. 12, 13 Serosurveys and epidemiological models have confirmed a high degree of covert infections for India, and a reasonable estimate would suggest that more than 90% of infections remain unreported. 12 A recent preprint 12 shows that the estimated case underreporting factor for India using data from April 1 to August 31, 2020, is approximately between 10 and 20, with the death underreporting factor estimated at approximately between 2 and 5 as of September 1, 2020. These estimates are obtained from an extension of the SEIR model accounting for the high false-negative rates of diagnostic tests (misclassification bias) and the symptom-based administration of these tests (selection bias). 12 The infection fatality rate (IFR) for India is estimated to be around 0.1% (using officially reported death counts), whereas the reported case fatality rate (CFR) is 1.4% at the end of the year 2020. There is substantial heterogeneity in the case counts across Indian states. The Figure exhibits heat maps on a logarithmic scale, indicating the number of confirmed cases on July 1, September 1, and November 1, 2020, during the first pandemic wave. These demonstrate the very inhomogeneous spread of COVID-19 in India, which centered largely around the major urban agglomerations in a small number of states over much of the early and intermediate period, expanding only later across the country.

Newer models leveraging information from serosurveys have appeared (e.g., the well-known Institute for Health Metrics and Evaluation model in the United States). 14 In the Indian context, Mandal et al. 15 (Table) . [16] [17] [18] [19] [20] [21] [22] Now that community-wide vaccination programs are underway in many countries, some of these models also consider different vaccine effectiveness profiles. [16] [17] [18] One straightforward approach is to use the traditional SIR or SEIR model, where each compartment is stratified by vaccine status and infecting variant, and to consider time-varying vaccination rates and variant transmission rates. 17 To our knowledge, the impacts of both vaccines and new virus variants have not yet been incorporated in any model from India.

When compared to prediction models for case-/ death-/hospitalization-counts, less public attention has been given to resource allocation models used to deploy health care resources like estimating oxygen supply or the number of hospital beds 23 or risk stratification using individual-level data. 24 Modeling approaches are critical as India rolls out its nationwide vaccine distribution plan based on hierarchical risk prioritization. Such models based on ecological-level data have been recently proposed. 25 

The comparatively low numbers for COVID-19 fatalities per million population in India provide some cheer. There have been suggestions that South Asian populations may be protected from more severe forms of the disease for various reasons. These theories include the possibility that infections from other types of coronaviruses in early life, leading to a stronger innate immune response. 27 The South Asian microbiome may differ in qualitative ways from the Western ones, 28 compulsory childhood vaccination programs may play a role, 29 and a genetic component to protect from the disease may exist. 30 The large number of patients detected by contact tracing who are asymptomatic at the time of testing suggests an overall milder impact of the disease. 31 Plausible explanations in support of India's low fatality rates include crossimmunity, genetics, prior vaccination, younger population, a predominantly outdoor lifestyle in rural areas, and plenty of outside air circulating through homes in urban settings. India is nearly 70% rural whereas European countries are overwhelmingly urban, facilitating the spread of the virus. However, all such hypotheses are conjectural at this point, and no causal association has been established.

Set against these hypotheses is the possibility that any innate advantage to the South Asian population is illusory, arising from inadequate counting of COVID-19 deaths. 32 There is certainly evidence that many deaths due to COVID-19 have not been classified as COVID-19 deaths (e.g., attributing patients' underlying conditions or comorbidities as the cause of death). 33 Evidence of deaths with symptoms suggestive of COVID-19 infection comes from on-the-ground reporting from crematoria and burial grounds, 34 detailed citizen-science-driven studies of obituaries, 35 evidence from the patients' families, and deathcertificates. 35 Estimates of COVID-19 death undercounting range from a factor of 1.5 to 5. 36 A holistic measure of excess mortality due to the pandemic could have been obtained by estimating the excess over all-cause mortality in non-COVID years if comprehensive historical death data were available. 37 There is every reason to believe that in India, as is the case elsewhere, there have been excess deaths indirectly caused by the pandemic, due, for example, to delays in reaching care or compromised capacity for hospital care. On the other hand, confounders such as the abrupt national lockdown in March 2020 in India induced a decline in road deaths and homicides, a decrease in unwarranted medical interventions, and a reduction in respiratory ailments from a decrease in pollution following the lockdown are difficult to account for. 38 Conjectural theories explaining causes of India's comparatively low fatality rates include crossimmunity, genetics, prior vaccination, younger population, and a predominantly outdoor lifestyle in rural areas. 

Since serosurveys from India indicated that at least 30%-40% of people in large urban areas already have experienced a past infection, 39 there were many discussions in the scientific community whether India is on its way to reaching herd immunity induced by natural infections. Recent articles 40 suggest that herd immunity may be impossible to attain and remain an elusive target even with vaccination efforts. Several models had predicted the imminent end of the pandemic at the end of 2020. For example, the government-endorsed supermodel 41 had predicted that there would not be another surge and that the coronavirus crisis would be substantially over by February 2021. However, intermediate outbreaks in some states refuted this naïve optimism, and the second surge made it clear that there are always possibilities of multiple waves of this virus. 42 

Since the middle of February 2021, the curve of reported COVID-19 cases in India has risen steeply. Some regions, such as the city of Pune, where serosurveys showed that more than 50% of the population had been infected in the first wave, are currently showing many cases in the second Abbreviations: u , instantaneous ratio of new detected infectious cases per unit time over the total; COVID-19, coronavirus disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus-2; SEIR, susceptible-exposed-infected-recovered; SGTF, S-gene target failures; SIR, susceptible-infected-recovered; VOC, variant of concern.

Recent articles suggest that herd immunity may be impossible to attain and remain an elusive target even with vaccination efforts. wave. 43 This sharp rise could be due to a confluence of factors, such as the potential effect of waning immunity (recent studies show 84% protection at 7 months from past infections), 44 new variants of concern, mass gatherings due to election campaigns, festivities, religious congregations, the reopening of the crowded public transportation system, as well as a sense of false security in the public that has led to a relaxation of preventive measures like face covering and social distancing. While the daily test positivity rate stayed below 2% for much of the period since October 2020, it is currently at 7.2% on March 30, 2021 (covind19.org 5 ). Models can dataadaptively capture this oscillatory growth, decay, sharp spikes, and falls by dividing the time series into segments. A new class of models is emerging that aims to not just model the virus transmission but also model changes in human behavior over time through predator-prey models like the Lotka-Volterra model. 45 We also need to model reinfection, cross-immunity, and the mutation process of the virus to capture the evolution of the epidemic over time.

Low-and middle-income countries are often denied the same credit for innovation, leadership, and implementation of public health policies as developed nations. Before the pandemic, preparedness indices favored more developed countries, with the UK and the United States listed in the top 5 in the Global Health Security Index (https://www.ghsindex.org). However, even with a modest rank of 57 in the index, India has substantially exceeded expectations, particularly in the way it scaled up testing and treatment facilities during the period of national lockdown in 2020, and managed to reduce overall COVID-19 case-fatality rates from what was expected in 2020. 46 Partnerships with private laboratories and hospital networks have enabled India to scale up testing from just 3,000 initially to more than 1.8 million tests per day. 47 The country expanded the ICU bed capacity by 3 times (63,758 in September versus 21,806 in April), the number of isolation beds to 1.55 million in September compared to 173,000 in April, and the number of designated COVID-care centers (15, 403 in September versus 1,919 in April). 48 It is important to stay prepared and continue to build this infrastructure because sweeping surges can happen, as we are noticing with the current oxygen crisis in India's second wave during April 2021.

The public acceptance of masks and nonpharmaceutical interventions in the early months of the pandemic was impressive, given that close social gatherings are an integral part of the cultural fabric of India. The success of community health worker involvement and syndromic surveillance, including in the most affected slum areas of Mumbai, 49 shows that India's public health approach can provide a unique example for other countries.

The year 2020 ended with at least 3 promising vaccine trials 50 globally, with multiple vaccine trials going on in India. 51 Follow-up studies of those vaccinated to understand the long-term safety and effectiveness of the vaccines will be necessary. Post-marketing studies for COVID-19 vaccines are all the more important owing to limited premarketing data resulting from their expedited development. Dhanda et al. 58 highlight the importance of such studies and the key epidemiological considerations, including active surveillance and careful study design. The Indian Council of Medical Research has set up the National Clinical Registry, a cohort of recovered COVID patients. 59 Monitoring the long-term health of this cohort is crucial as studies have indicated several unexpected post-COVID complications. 60 Vaccination outcomes in this recovered cohort should be of special interest. The economic recovery process for India will require a much longer time horizon and financial strategy. 61, 62 Safely reopening educational institutions and providing transitional support and aid to students and teachers will also be key as we look to the future.

The pandemic has underscored structural barriers as well as deep-rooted problems with India's societal and public health infrastructure. It has displayed the inequities and the lack of poor pandemic preparedness in India. It has helped focus our attention on long-standing questions of the quality of public health systems, the need for better data, the importance of communication, and the need for more interdisciplinary expertise to address the so-called wicked problems that the current pandemic highlights. 63 It is imperative to take this as a teaching example and build strong systems to prepare for future pandemics. This requires substantial resource allocations and leadership to strengthen the agenda of health security, especially in the control of communicable diseases. Addressing alarming levels of air pollution, 64 arresting the high prevalence of noncommunicable diseases, and ensuring adequate support for mental health needs will be pivotal. Investments in public health must increase well above prepandemic levels.

Other factors that support a good public health system, including improved health data infrastructure, should be addressed. A planned digital health identity for citizens of India 65 will help identify elderly individuals, individuals with comorbidities, and essential workers nationwide for vaccination programs. 66 Nationallevel health record data, together with actionable systems to access and mine this data while maintaining data privacy, will enable a more targeted approach to public health and health care in India. Even in the post-inoculated world, when the case counts reduce to a few hundred, India should have a robust surveillance system to track and contact trace future outbreaks of SARS-CoV-2 infection and identify any new variants. Sustained adoption and incentivization of COVIDappropriate behaviors are going to help us avoid massive lockdowns with crushing economic and social consequences.

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Acknowledgments: The authors would like to thank the University of Michigan School of Public Health, The Rogel Comprehensive Cancer Center, and the Michigan Institute of Data Science for supporting this research.

Competing interests: None declared.