Print Email Facebook Twitter A Spatial Markov Chain Cellular Automata Model for the Spread of the COVID-19 virus Title A Spatial Markov Chain Cellular Automata Model for the Spread of the COVID-19 virus: Including parameter estimation Author Lu, Jenny (TU Delft Electrical Engineering, Mathematics and Computer Science; University of Hasselt) Contributor van der Meulen, F.H. (mentor) Vermolen, F.J. (mentor) Degree granting institution Delft University of TechnologyUniversity of Hasselt Programme Applied Mathematics Date 2020-09-20 Abstract In this bachelor thesis we propose a spatial Markov Chain Cellular Automatamodel for the spread of the COVID-19 virus as well as two methods for parameterestimation. Network topologies are used to model the progression of the epidemicby considering each individual on a grid and using stochastic principles to determine the transition between different states. The model is able to predict thetime-evolution of outbreaks under different lockdown policies. Additionally, theimpact of variation in infection probability and recovery rates on the amount ofactive cases, deaths as well as the length of the epidemic is investigated. Theseresults 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. Subject Mathematical model, COVID-19, Coronavirus, SARSCoV- 2, Pandemic, Numerical simulation, Parameter estimation, Markov chain cellular automata, epidemic model, maximum likelihood estimation, bayesian estimation. To reference this document use: http://resolver.tudelft.nl/uuid:83ab5472-2f15-447c-ac94-263a59ccd358 Part of collection Student theses Document type bachelor thesis Rights © 2020 Jenny Lu Files PDF BEPJennyFinal26sep.pdf 13.68 MB Close viewer /islandora/object/uuid:83ab5472-2f15-447c-ac94-263a59ccd358/datastream/OBJ/view