Early guidance for Sars-Cov-2 health policies in India: Social Distancing amidst Vaccination and Virus Variants *

  1. Yi Zhang
  2. Sanjiv Kapoor

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Abstract

Policy decisions during the SARS-COV-2 pandemic were complicated due to virus variants and the impacts of societal restrictions. Accurate predictive models were required in this context. In this paper we report results from a model that helped in predicting the impact of SARS-CoV-2 virus transmission in India over a period of a number of months from June, 2021 to March 2022. These models were applied in the context of enabling policy decisions to tackle the impact of the pandemic in India culminating in early warning projections for the Omicron variant and used for advise on preemptive policy actions.

Methods

Our model utilizes a deterministic compartment models incorporating a dynamic transmission factor, dependent on the population’s behavior as a function of the reported confirmed cases of virus transmission as well as methods for estimation of the increase in susceptible population when social distancing mandates are relaxed. The model used to predict viral growth incorporates the state of vaccination and the virus variants that form part of the transmission dynamics as well the lockdown state of the population. NPI actions were used in India to contain the spread of infections during the period of study, especially during the surge of the Omicron variant of the virus. Further we present the impact of lockdown policies and illustrate the advantage of adopting partial lockdown policies in the early period of 2022. Based on the models, our predictive analysis, when applied to the Omicron variant, illustrated substantial improvement even when partial lockdown is planned.

Findings

This report presents models and results that incorporated the impact of vaccination rates and the Omicron variant and were used to establish projections on the growth of Sars-Cov2 infections in India for the period from July 2021 till March 2022. The growth rate of the Omicron virus was deduced from data that originated from South Africa in November 2021. These projections were submitted to a pivotal government organization involved in developing a national public health strategy to address the pandemic and, as per personal communication, were considered when formulating national policy. The pandemic had a subdued impact in India during the period from July 2021 till date as evident from the deaths reported by the government. The projections were made every month and cases were projected over the next 4-16 weeks. The projections of cumulative cases during the Omicron wave had low errors when measured using RMSE per capita and had a MAPE error of 17.8% when measured 15 days after start of the projection on December 5th, 2021.

Discussion

The composed model was found to be useful in providing predictive and data based analytic input to inform early warning approaches in the context of policy based interventions to control the pandemic in India. The model provided monthly early prediction of the spread and impact of the SARS-COV-2 virus in India, state-wise, during the phase of removal of government lockdown in the second half of 2021. The early warning system incorporated the impact of the Omicron variant to provide predictions for Indian states and the country.

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    SciScore for 10.1101/2022.02.02.22270353: (What is this?)

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  2. Version published to 10.1101/2022.02.02.22270353 on medRxiv