Epidemic parameters for COVID-19 in several regions of India

TL;DR

This study uses Bayesian analysis to estimate COVID-19 epidemic parameters in various Indian regions, revealing that initial rapid case growth may be influenced by surveillance ramp-up, and that R0 and CFR increase together.

Contribution

It provides region-specific estimates of R0 and CFR for COVID-19 in India, highlighting the importance of surveillance effects and their implications for public health planning.

Findings

Initial rapid growth partly due to surveillance ramp-up

R0 and CFR increase together over time

Post-April 10 growth consistent with exponential model

Abstract

Bayesian analysis of publicly available time series of cases and fatalities in different geographical regions of India during April 2020 is reported. It is found that the initial apparent rapid growthin infections could be partly due to confounding factors such as initial rapid ramp-up of disease surveillance. A brief discussion is given of the fallacies which arise if this possibility is neglected. The growth after April 10 is consistent with a time independent but region dependent exponential. From this, R0 is extracted using both known cases and fatalities. The two estimates are seen to agree in many cases; for these CFR is reported. It is seen that CFR and R0 increase together. Some public health implications of this observation are discussed, including a target doubling interval if medical facilities are to remain adequate.