Stochastic Model for the Spread of the COVID-19 Virus

Abstract

The COVID-19 pandemic has become a great challenge to scientific, biological and medical research as well as to economic and social sciences. Hence, the objective of infectious disease modeling-based data analysis is to recover these dynamics of infectious disease spread and to estimate parameters that govern these dynamics. The random aspect of epidemics leads to the development of stochastic epidemiological models. We establish a stochastic combined model using numerical scheme Euler, Markov chain and Susceptible-Exposed-Infected-Recovery (SEIR) model. The combined SEIR model was used to predict how epidemics will develop and then to act accordingly. These COVID-19 data were analyzed from several countries such as Italy, Russia, USA and Iran.

Broken-Edge Decision-Making Strategy for COVID-19 over Air Railway Composite Network

“…Similarly, the combination of complex network theory and propagation model is applicable to control the spread of biological viruses over the transportation network. References [ 31 , 32 ] improved the SEIR (susceptible-explored-infected-recovered) and SEI (susceptible-explored-infected) models based on the spreading characteristics of COVID-19 with system dynamics in epidemic areas. However, this study does not propose the strategy of network edge breaking so as to reduce the virus transmission rate and economic loss.…”