Stochastic Model of SIR Epidemic Modelling

03/05/2018
by   Kurnia Susvitasari, et al.
0

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 R_0 ≤ 1. That is, the disease-free stage in the epidemic. But when R_0 > 1, the deterministic and stochastic approaches had different interpretations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/05/2018

Comparing the Behaviour of Deterministic and Stochastic Model of SIS Epidemic

Studies about epidemic modelling have been conducted since before 19th c...
research
07/01/2021

From Epidemic to Pandemic Modelling

We present a methodology for systematically extending epidemic models to...
research
06/15/2021

Epidemic modelling of multiple virus strains: a case study of SARS-CoV-2 B.1.1.7 in Moscow

During a long-running pandemic a pathogen can mutate, producing new stra...
research
06/30/2021

Switchover phenomenon induced by epidemic seeding on geometric networks

It is a fundamental question in disease modelling how the initial seedin...
research
10/26/2016

Location Aggregation of Spatial Population CTMC Models

In this paper we focus on spatial Markov population models, describing t...
research
06/10/2023

Epidemic spreading in wireless sensor networks with node sleep scheduling

Wireless Sensor Networks (WSNs) have become widely used in various field...

Please sign up or login with your details

Forgot password? Click here to reset