Abstract
Epidemiological models often assume that individuals do not change their behaviour or that those aspects are implicitly incorporated in parameters in the models. Typically, these assumptions are included in the contact rate between infectious and susceptible individuals. However, adaptive behaviours are expected to emerge and play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioural dynamics due to infection awareness. We modelled the dynamics of social behaviour using a game theory framework, which is then coupled with an epidemiological model that captures the disease dynamics by assuming that individuals are aware of the actual epidemiological state to reduce their contacts. Results from the mechanistic model show that as individuals increase their awareness, the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also incorporate theoretical contact networks, having the awareness parameter dependent on global or local contacts. Results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state.
Highlights
Traditional infectious disease transmission models allocate the population into compartments that capture different disease royalsocietypublishing.org/journal/rsos R
Our results focus on understanding the role of behaviour modelled as the replicator dynamics present in the ordinary differential equations systems (ODEs) model and the imitation update rule in the network model
Social dynamics play an essential role in the evolution of epidemics and the unfolding of a disease across a population [6,8]
Summary
Traditional infectious disease transmission models allocate the population into compartments that capture different disease royalsocietypublishing.org/journal/rsos R. Numerous factors influence transmission and are important to consider or model directly in epidemiological models One of those is the understanding of the effect of an individual’s behaviour on the disease population dynamics, which has recently been highlighted as a response to reduce contact rates and pathogen transmission across populations [3]. Changes in individual contact rates driven by changes in the awareness on the epidemic or the state of the infectious disease have been discussed and explicitly modelled [8,9] These models do not incorporate an explicit mechanism by which individuals could modify their behaviour, they have demonstrated how behavioural aspects play an essential role in disease dynamics. A classic example is the Ebola outbreak in Sierra Leone, where risk communication played an essential role in controlling transmission due to high infection probability given contact with an infectious individual of this disease [10]
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