Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes

Abstract

Understanding host–pathogen dynamics requires realistic consideration of transmission events that, in the case of directly transmitted pathogens, result from contacts between susceptible and infected individuals. The corresponding contact rates are usually heterogeneous due to variation in individual movement patterns and the underlying landscape structure. However, in epidemiological models, the roles that explicit host movements and landscape structure play in shaping contact rates are often overlooked. We adapted an established agent‐based model of classical swine fever (CSF) in wild boar Sus scrofa to investigate how explicit representation of landscape heterogeneity and host movement between social groups affects invasion and persistence probabilities. We simulated individual movement both phenomenologically as a correlated random walk (CRW) and mechanistically by representing interactions of the moving individuals with the landscape and host population structure. The effect of landscape structure on the probability of invasion success and disease persistence depended remarkably on the way host movement is simulated and the case fatality ratio associated with the pathogen strain. The persistence probabilities were generally low with CRW which ignores feedbacks to external factors. Although the basic reproduction number R0, a measure of the contagiousness of an infectious disease, was kept constant, these probabilities were up to eight times higher under mechanistic movement rules, especially in heterogeneous landscapes. The increased persistence emerged due to important feedbacks of the directed movement on the spatial variation of host density, contact rates and transmission events to distant areas. Our findings underscore the importance of accounting for spatial context and group size structures in eco‐epidemiological models. Our study highlights that the simulation of explicit, mechanistic movement behaviour can reverse predictions of disease persistence in comparison to phenomenological rules such as random walk approaches. This can have severe consequences when predicting the probability of disease persistence and assessing control measures to prevent outbreaks.

Movement can mediate temporal mismatches between resource availability and biological events in host–pathogen interactions

“…It is based on earlier models only considering neighborhood infections developed by Kramer-Schadt et al, (2009) and Lange et al, (2012a, b). The model by Scherer et al, (2020) relies on individual movement decisions of host F I G U R E 1 Cascading effect from the resource landscape (a) through the dynamic resource level of each single habitat cell (b) and the host population dynamics (c) that can be synchronous, asynchronous (shifted by t lag ) (d), or random (e) in time, respectively, to each other. The resource level at specific points in time may influence host survival (f) and movement decisions (g) that may alter host population density distribution (h) and subsequently host-pathogen interactions through contact rates and transmission (i) before ultimately accentuating scenarios that allow for coexistence (j) individuals, that is, long-distance roaming movement of males (hereafter termed "movement"), a process important for disease transmission.…”

“…Pathogen transmission-All transmission processes remain unchanged from the model implementation by Scherer et al, (2020).…”

“…In case of wild boar, if the species continues to benefit from the effects of climate and land-use change, the single birth peak dynamic might continue to change toward multiple reproductive peaks per year, further offsetting adverse effects on host-pathogen coexistence. This could lead to an upsurge in persistent viral outbreaks throughout wild boar populations, which might exacerbate currently discussed processes like individual infection risk in piglets and movement (Scherer et al, ,2019(Scherer et al, , , 2020. A prominent example is the persistence of African Swine Fever (ASF) in wild boar in Europe, which affects animal health more severely and can cause profound economic damages (Halasa et al, 2016) when coming into contact with domestic pigs.…”

Infection Percolation: A Dynamic Network Model of Disease Spreading

“…We describe the intrinsic infectivity of a given disease throughout this network by the parameter P * , where a high value of P * characterizes a highly infectious disease. Crucially, each interaction between individuals ij is characterized by its own barrier to disease transmission P th,ij [58]; this threshold explicitly describes the propensity of individual i, if infected, to transmit the disease to the susceptible individual j, and depends on individual behaviors such as social distancing between i and j [13,23,32,42]. Furthermore, if individuals i and j are infected and susceptible, respectively, the duration of disease transmission from i to j is given by τ ij ; at the population scale, the mean of all the τ ij can be thought of as the inverse of a macroscopic disease transmission rate.…”

“…Nevertheless, models based on this approach also suffer from limitations: they typically either do not consider the dynamics of disease spreading and only treat the final stage of infection, or they also describe the dynamics of disease transmission using an ad hoc macroscopic parameter that aggregates the influence of random and uncorrelated individual interactions. However, transmission is known to depend sensitively on the full history of infection, on specific individual behaviors [40] including social distancing [13,23,24,32,41,42], and on interactions between different social networks [39]. Thus, the ability to accurately predict the temporal evolution of active infections in an overall population, as well as the full spatiotemporal features of disease spreading, remains limited.…”