Evolving Weighted Networks to Simulate Epidemics and Lockdowns

Simulating epidemics is vital to understanding their effect on human populations, and developing models to provide insights into epidemic behaviour is a primary goal of this thesis. A generative evolutionary algorithm is used to evolve weighted personal contact networks that represent physical contact between individuals, and thus possible paths of infection during an epidemic. The evolutionary algorithm evolves a list of edge-editing operations applied to an initial graph. Two initial graphs are considered, a ring graph and a power-law graph. Different probabilities of infection and a wide range of weights are considered, which improve performance over other work. Modified edge operations are introduced, which also improve performance. When attempting to match a given epidemic profile, similar results are obtained when using either initial graph, but both improve performance over other work. The impact of different lockdown strategies upon the total number of infections in an epidemic are evaluated for two models of infection: one in which the disease confers permanent immunity, and one in which it does not. The strategies are based upon the proportion of the population infected at a time in order to trigger lockdown, combined with the proportion of interactions removed during lockdown. The population, its interactions, and the relative strengths of those interactions are stored in a weighted contact network, from which edges are removed during lockdown. These edges are selected using an evolutionary algorithm (EA) designed to minimize total infections. Using the EA to select edges significantly reduces total infections in comparison to random selection. In fact, the EA results for the least strict conditions were similar or better to the random results for the most strict conditions, showing that a judicious choice of restrictions during lockdown has the greatest effect on reducing infections. Further, when using the most strict rules a smaller proportion of interactions can be removed to obtain similar or better results in comparison to removing a higher proportion of interactions for less strict rules.

Name thesis:degree_name

M.Sc. Computer Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Computer Science

Grantor

Brock University

Year dc:date.issued

2023

Author dc:creator

• Sargant, James Robert

• Evolutionary Computation
• Epidemic Simulation
• Graph Theory

Statement dc:rights

• Attribution-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nd/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/18163

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/18163