Time-dependent probability distribution for number of infection in a stochastic SIS model: case study COVID-19.

ABSTRACT: We derive the time-dependent probability distribution for the number of infected individuals at a given time in a stochastic Susceptible-Infected-Susceptible (SIS) epidemic model. The mean, variance, skewness, and kurtosis of the distribution are obtained as a function of time. We study the effect of noise intensity on the distribution and later derive and analyze the effect of changes in the transmission and recovery rates of the disease. Our analysis reveals that the time-dependent probability density function exists if the basic reproduction number is greater than one. It converges to the Dirac delta function on the long run (entirely concentrated on zero) as the basic reproduction number tends to one from above. The result is applied using published COVID-19 parameters and also applied

SUBMITTER: Otunuga OM 

PROVIDER: S-EPMC8112579 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature