The Impact of Past Epidemics on Future Disease Dynamics
arXiv:0910.2008 · doi:10.1016/j.jtbi.2012.06.012
Abstract
Many pathogens spread primarily via direct contact between infected and susceptible hosts. Thus, the patterns of contacts or contact network of a population fundamentally shapes the course of epidemics. While there is a robust and growing theory for the dynamics of single epidemics in networks, we know little about the impacts of network structure on long term epidemic or endemic transmission. For seasonal diseases like influenza, pathogens repeatedly return to populations with complex and changing patterns of susceptibility and immunity acquired through prior infection. Here, we develop two mathematical approaches for modeling consecutive seasonal outbreaks of a partially-immunizing infection in a population with contact heterogeneity. Using methods from percolation theory we consider both leaky immunity, where all previously infected individuals gain partial immunity, and perfect immunity, where a fraction of previously infected individuals are fully immune. By restructuring the epidemiologically active portion of their host population, such diseases limit the potential of future outbreaks. We speculate that these dynamics can result in evolutionary pressure to increase infectiousness.
References in corpus (2)
Cited by in corpus (10)
- Coevolution spreading in complex networks
- Modeling the dynamical interaction between epidemics on overlay networks
- Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity
- Outbreaks in susceptible-infected-removed epidemics with multiple seeds
- Human mobility networks and persistence of rapidly mutating pathogens
- Cooperative coinfection dynamics on clustered networks
- Strength and weakness of disease-induced herd immunity in networks
- Disease Transmission on Random Graphs Using Edge-Based Percolation
- An exact N-strain epidemic model using bond percolation
- One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories