Mortality due to COVID-19 in different countries is associated with their demographic character and prevalence of autoimmunity

Abstract:

In the first few months of its deadly spread across the world, COVID-19 mortality has exhibited a wide range of variability across different nations. In order to explain this phenomenon empirically, we have taken into consideration all publicly available data for 106 countries on parameters like demography, prevalence of communicable and non-communicable diseases, BCG vaccination status, sanitation parameters, etc. We used multivariate linear regression models and found that the incidence of communicable diseases correlated negatively. Demography, improved hygiene and higher incidence of autoimmune disorders correlated positively with COVID-19 mortality and they were among the most plausible factors to explain COVID-19 mortality compared to GDP of the nations. (Joint work with Bithika Chatterjee and Rajeeva Karandikar.)

Sero-Survey in Karnataka State

Abstract:

The state of Karnataka conducted a survey to estimate the total COVID-19 burden in the state between September 03-16, 2020. The survey was unique in two main aspects, namely, it jointly estimated both current and past infection in the state and secondly the survey covered the entire geographical region of the state, a first in India. The survey provided a lot of useful information for the state and can be considered as a blue print for the rest of the country. (Joint work with Rajesh Sundaresan.)

Small ball probabilities and a support theorem for the stochastic heat equation

Abstract:

Small ball probabilities have a long history and vast literature. We shall begin with a formulation of the question and present key ideas in the proof for Brownian motion. We will have a brief discussion of known results for Gaussian processes beginning with the work of Bass-88 and Talagrand-94. We will conclude with our result on the small ball problem for the stochastic heat equation (SHE) \partial_t u(t,x) = \partial_x^2 u(t,x) + \sigma(t,x,u) \dot W(t,x) on a torus, where \dot W(t,x) is white noise. (Joint work with Carl Mueller and Mathew Joseph.)

Rajeeva L Karandikar: Glimpses of his work over four decades