Emerging Variants and Fading Immunity: Analyzing the Impact in Epidemic Modeling

Abstract

Outbreaks like the recent COVID-19 pandemic underscore the importance of quick and informed responses to control and mitigate the epidemic. This thesis aims to develop a model that can provide deeper insights into how epidemics progress and behave. Accurate epidemic simulation can provide valuable insights into how an epidemic affects the population. This thesis considers both epidemic spread and epidemic severity, and integrates fading immunity into the epidemic model, creating a more realistic representation of real-life scenarios. This research also extends the model by considering diverse population structures in the contact network where different age groups have different levels of immunity strength. An evolutionary algorithm was used to generate and evolve personal contact networks. We analyzed the epidemic dynamics within these networks, focusing on how different proportions of young and old individuals impact the spread and severity of the epidemic. Results reveal that older populations with weaker immunity experience more severe infections, while younger populations with stronger immunity mitigate both spread and severity. The thesis also explores the impact on variant generation, showing that when using the epidemic spread fitness function there is a tendency to produce more variants than when using the epidemic severity fitness function, highlighting the virus's need to mutate in response to existing immunity. When the population is dominated by younger individuals, even though fewer variants are being generated, successful variants tend to exhibit a higher mutation distance to overcome the robust immunity present in the community