  A disease in a given population is termed endemic when it exhibits a steady
prevalence. We address the pertinent question as to what extent COVID-19 has
turned endemic in India. There are several existing models for studying endemic
behaviour, such as the extensions of the traditional temporal SIR model or the
spatio-temporal endemic-epidemic model of Held et al. (2005) and its
extensions. We propose a "spatio-temporal Gravity model" in a state of the art
generalised linear model set up that can be deployed at various spatial
resolutions. In absence of routine and quality covariates in the context of
COVID-19 at finer spatial scales, we make use of extraneous covariates like
air-traffic passenger count that enables us to capture the local mobility and
social interactions effectively. This makes the proposed model different from
the existing models. The proposed gravity model not only produces consistent
estimators, but also outperforms the other models when applied to Indian
COVID-19 data.
