Farhina Mozaffer

Summary Background The course of the COVID-19 pandemic has been driven by several dynamic behavioral, immunological, and viral factors. We used mathematical modeling to explore how the concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant affected the trajectory of the pandemic in India, using Andhra Pradesh (pop: 53 million) as an exemplar Indian state.

Modeling future parasite range expansion with warming climate We used a series of climate-based models to predict the change in parasite range with temperature. First, we extracted monthly values for minimum temperature and maximum temperature for the years 2021–2040 for four Himalayan sites. We used Madhmaheshwar (30°38′13″ N 79°12′58″ E), a nearby site, as an alternative to Shokharakh due to nonavailability of data.

Abstract Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021.