A Spatial Markov Chain Cellular Automata Model for the Spread of the COVID-19 virus

Including parameter estimation

Bachelor thesis (2020)

Authors

J. Lu Electrical Engineering, Mathematics and Computer Science

Contributors

F.H. van der Meulen Statistics - (mentor)

F.J. Vermolen Numerical Analysis - (mentor)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Mathematical model, COVID-19, Coronavirus, SARSCoV- 2, Pandemic, Numerical simulation, Parameter estimation, Markov chain cellular automata, epidemic model, maximum likelihood estimation, bayesian estimation.

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Published Date

20-09-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this bachelor thesis we propose a spatial Markov Chain Cellular Automata
model for the spread of the COVID-19 virus as well as two methods for parameter
estimation. Network topologies are used to model the progression of the epidemic
by considering each individual on a grid and using stochastic principles to determine the transition between different states. The model is able to predict the
time-evolution of outbreaks under different lockdown policies. Additionally, the
impact of variation in infection probability and recovery rates on the amount of
active cases, deaths as well as the length of the epidemic is investigated. These
results can provide us with insights and predictions of the spread of the virus under different scenarios. Parameter estimation is done by using both Maximum likelihood estimation and Bayesian estimation based on simulated data. The produced estimates were relatively accurate, suggesting that these methods can be applied in order to estimate the parameters of the proposed model based on actual data.

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