Localized contacts between hosts reduce pathogen diversity

Abstract

We investigate the dynamics of a simple epidemiological model for the invasion by a pathogen strain of a population where another strain circulates. We assume that reinfection by the same strain is possible but occurs at a reduced rate due to acquired immunity. The rate of reinfection by a distinct strain is also reduced due to cross-immunity. Individual based simulations of this model on a 'small-world' network show that the proportion of local contacts in the host contact network structure significantly affects the outcome of such an invasion, and as a consequence will affect the patterns of pathogen evolution. In particular, hosts interacting through a 'small-world' network of contacts support lower prevalence of infection than well-mixed populations, and the region in parameter space for which an invading strain can become endemic and coexist with the circulating strain is smaller, reducing the potential to accommodate pathogen diversity. We discuss the underlying mechanisms for the reported effects, and we propose an effective mean-field model to account for the contact structure of the host population in 'small-world' networks.1

The reinfection threshold regulates pathogen diversity: the case of influenza

“…Above the RT, equilibrium prevalences are about two orders of magnitude higher and oscillations are no longer observed (see Gomes et al 2004). When two clusters are introduced, the long-term equilibrium is characterized by coexistence where both clusters maintain the same endemic level (Nunes et al 2006). This work is focused on invasion by new clusters and the dynamics on shorter time-scales.…”

The impact of past epidemics on future disease dynamics

“…The evolution of infectiousness, virulence and a pathogen's antigenic characteristics are in part driven by the epidemiological environment. Although the interactions between contact network structure and pathogen evolution and competition have been studied (Boots and Sasaki, 1999;Read and Keeling, 2003;van Baalen, 2002;Buckee et al, 2004;Nunes et al, 2006), we do not yet understand how prior outbreaks impact future disease dynamics by shaping the immunological structure of the underlying host contact network (Fig. 1).…”

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

“…To describe the individual-level characteristics, ABM and IBM were mainly used, followed by CA or micro-simulation (though with different spelling variations). Other terminology that covered our definitions included “individual-level model” [ 63 ], “individually based SIR model” [ 64 ], “small world network” [ 65 ], “large-scale stochastic simulation” [ 66 ], “equation free approach” [ 67 ] or other variants of “stochastic models” [ 36 , 68 ]. General keywords gave many false positive hits though still resulted in 124 papers that did not use the most common terminology in their abstract, title or keywords.…”