Heterogeneity in susceptibility dictates the order of epidemiological models
arXiv:2005.04704 · doi:10.1016/j.jtbi.2021.110839
Abstract
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in infection susceptibility. We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.
References in corpus (5)
- The challenges of modeling and forecasting the spread of COVID-19
- Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola
- Key Questions for Modelling COVID-19 Exit Strategies
- On the spread of epidemics in a closed heterogeneous population
- Heterogeneous Susceptibles-Infectives model: Mechanistic derivation of the power law transmission function