Estimation of Optimal Lock-Down and Vaccination Rate of a Stochastic SIR Model: A Mathematical Approach

Abstract

This paper utilizes a stochastic Susceptible-Infected-recovered (SIR) model with a nonlinear incidence rate to estimate the optimal lock-down intensity and vaccination rate under the COVID-19 pandemic environment. We use a Feynman-type path integral control approach to determine a Fokker-Plank type equation of this system. Since we assume the availability of information on the COVID-19 pandemic is complete and perfect, we show the existence of a unique fixed point. A non-linear incidence rate is used because, it can be raised from saturation effects that if the proportion of infected agents is very high so that exposure to the pandemic is inevitable, then the transmission rate responds slower than linearity to the increase in the number of infections. The simulation study shows that with higher diffusion coefficients susceptible and recovery curves keep the downward trends while the infection curve becomes ergodic. Finally, we perform a data analysis using UK data at the beginning of 2021 and compare it with our theoretical results.

Measuring Asymmetric Tails Under Copula Distributions

“…In the domain of infectious disease modeling, the widely utilized susceptibility-infection-recovery (SIR) framework becomes relevant [34]. Our approach facilitates the assessment of exchangeability by analyzing various SIR datasets across two regions [27,28,30,32].…”