2018/03/05 by Kurnia Susvitasari, Susvitasari, Kurnia
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #Applications (stat.AP) #COVID-19 epidemiological studies #Computer science #Demography #Econometrics #Economics #Epidemic model #FOS: Biological sciences #FOS: Computer and information sciences #Geography #Mathematical and Theoretical Epidemiology and Ecology Models #Mathematics #Population #Populations and Evolution (q-bio.PE) #Sociology #Statistics #Stochastic modelling #Viral Infections and Vectors #q-bio.PE #stat.AP
paper · pdf · doi:10.48550/arxiv.1803.01496
published in arXiv (Cornell University) (Cornell University) · 9 pages, 14 figures
arxiv created 2018/03/05 · openalex publication_date 2018/03/05 · arxiv updated 2018/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Threshold theorem is probably the most important development of mathematical epidemic modelling. Unfortunately, some models may not behave according to the threshold. In this paper, we will focus on the final outcome of SIR model with demography. The behaviour of the model approached by deteministic and stochastic models will be introduced, mainly using simulations. Furthermore, we will also investigate the dynamic of susceptibles in population in absence of infective. We have successfully showed that both deterministic and stochastic models performed similar results when R0 ≤ 1. That is, the disease-free stage in the epidemic. But when R0 > 1, the deterministic and stochastic approaches had different interpretations.