# Models for SARS-CoV-2 health policies: social distancing amid vaccination and virus variants

## TL;DR

This paper presents a model predicting SARS-CoV-2 transmission in India, incorporating vaccination, virus variants, and population behavior to guide public health policies.

## Contribution

A novel model integrating population behavior, vaccination rates, and virus variants to predict and inform health policies during the pandemic.

## Key findings

- The model projected cumulative cases during the Omicron wave with a mean absolute percentage error of 18.1%.
- The model incorporated dynamic transmission factors based on population behavior and reported confirmed cases.
- The model supported early warning projections for the Omicron variant to guide policy decisions.

## Abstract

Policy decisions during the SARS-CoV-2 pandemic were important to contain the spread of the virus, but complicated due to virus variants and the varying impact of societal restrictions. In this paper, we report results from a model that utilized population behaviour to predict the impact of SARS-CoV-2 transmission in India over a number of months from June 2021 to March 2022. The model utilizes deterministic population compartments, incorporating a dynamic transmission factor dependent on the population’s behaviour as a function of reported confirmed cases. The model also incorporates the state of vaccination and virus variants as part of the transmission dynamics. The model projections, used for formulating advice towards pre-emptive policy actions by pivotal government organizations involved in developing a national public health strategy, culminated in early warning projections for the Omicron variant. The projections of cumulative cases during the Omicron wave had a mean absolute percentage error of 18.1% when measured 15 days after the start of the projection on 1 December 2021.