Appendix 3: Data requirements for modeling exercise

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are a key part of those delivery systems, as is the training of nurses. Countries in South Asia are doing an admirable job launching the COVID-19 vaccination campaign, but going forward, an expanded health infrastructure would facilitate faster vaccination of their populations. The pandemic has also underscored the value of preventive care. While comorbidities increase the virus’s fatality rate, preventive care is still insufficient, especially for poorer parts of the population. Addressing the problem requires increased primary care investments, which also generate huge gains in income and well-being. Such investments entail a shift from hospital care to primary care and an increase in public spending on health care, which is low in South Asia compared to other regions. Prioritizing groups in a vaccination campaign will always be difficult. The takeaway is that simple rules work best, and as a general principle, priority should be given to the most vulnerable, both from a health and economic perspective. In the case of COVID-19, the elderly are most vulnerable from a health perspective, and (essential) workers who can’t adhere to social distancing are the most vulnerable from an economic perspective.

Appendix 3: Data requirements for modeling exercise

For the models we have specified, three data categories are required: disease surveillance, demographic, and historical consumption. We describe each of these data sources and their uses in turn.

Disease Surveillance Data To estimate the prevalence and potential risk as the pandemic continues, certain disease surveillance data are required.

Confirmed case data The basic epidemiological data required to run the compartmental model comes in the form of confirmed new daily cases for each geography being considered for vaccine distribution. Ideally, each case count time series should be broken down by each demographic category considered for vaccine allocation (age, sex, occupation, etc.). Lacking this breakdown, it is possible to use demographic data or seroprevalence data to disaggregate these counts to each specific population category.

The case time series data are fed into the epidemiological model to project case counts into the future under different vaccination policies. For this analysis, we use data from the COVID19India website, a crowdsourced initiative aggregating official COVID-19 data from across India at a district-specific level.

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Confirmed recoveries In addition to daily new cases, daily recoveries are also a key source of data to estimate the value of vaccination. This is because recovery from prior infection provides a measure of natural immunity from reinfection. The cumulative recovery count time series therefore provides an estimate of natural immunity in a geography.

As with confirmed cases, these data should ideally be broken down into demographic categories; different recovery rates imply that different subpopulations have varying levels of natural immunity, reducing the value of vaccinating that subgroup. Additionally, we also use the COVID19India website for the district-level recovery time series.

Seroprevalence Because of selection issues, confirmed case counts may not accurately reflect the total disease prevalence. Not all cases may be found via hospital testing or by testand-trace procedures, especially given asymptomatic spread of COVID-19. A properly representative seroprevalence survey will estimate the number of people who have recovered from COVID-19 (putting aside the issue of waning antibodies). Serology studies are of limited use in differential diagnoses, so surveys are rare and capture prevalence at a specific point in time. We use seroprevalence to scale the number of confirmed cases and use the trend in confirmed cases to project future prevalence according to the epidemiological model specified above. This requires assuming the trend in actual cases (i.e., including unconfirmed and asymptomatic) is independent of the selection issues in confirmed case counts. We turn to a novel, large sample (N = 26,000) seroprevalence study in the Indian state of Tamil Nadu, conducted in November 2020, for this analysis.

Demographic Data Additionally, the breakdown of each district’s population by vaccination group is needed to set the initial conditions of the epidemiological model. Moreover, knowing the number of people in each demographic group is required to estimate when each group will be completely vaccinated according to a given prioritization scheme. For this demographic data, we use the 2011 Government of India census.

Economic Consumption Data In order to assess the economic benefits of vaccination of each group, historical data on consumption is required. We use estimated consumption data before and during the pandemic to project the economic trajectory along which societies will recover as people return to pre-pandemic levels of economic activity. We then map the epidemiological model’s projected case counts to estimated levels of resumed