Research Article
Stochastic Modelling And Simulation Analysis Of Confirmed Cases Of Covid 19
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314570, author={Haridass M and Deepa V}, title={Stochastic Modelling And Simulation Analysis Of Confirmed Cases Of Covid 19}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={markov chain; steady state; confirmed; forecast; covid 19 simulation subject classification: 60j22 60j10 65c05 37a50}, doi={10.4108/eai.7-12-2021.2314570} }- Haridass M
Deepa V
Year: 2021
Stochastic Modelling And Simulation Analysis Of Confirmed Cases Of Covid 19
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314570
Abstract
Forecasting is essential to make reliable and accurate estimates of what will happen in the future in the face of uncertainty. The primary objective of forecasting is the monitoring of the continuing progress of action plans based on forecasts. COVID19 is an emerging viral infection and currently it is a major threat throughout the world. As on 7th May 2020, more than 3.5 million cases of COVID-19 and 2,50,000 deaths have been reported to WHO (World Health Organization). In this stringent situation, it is very important to forecast the cases, and it will be very much useful for healthcare department. Following this notion, we have been developing a stochastic model and then employed it for forecasting future COVID19 cases in two major countries Australia and Italy. This study explores the possibility of applying Stochastic Model to forecast the total number of new confirmed cases of COVID19, and to obtain their long run probabilities or steady state probabilities. In this article, the basic terminologies of stochastic processes and Markov Chain have been discussed. The real data were considered from WHO and analyzed the trend pertaining to the confirmed cases of the Novel Corona Virus COVID19. The Simulation study was made to justify the forecast analysis done by the stochastic model.