How disease risk awareness modulates transmission: coupling infectious disease models with behavioral dynamics

Jaime E. Cascante-Vega, Samuel Torres-Florez, Juan M. Cordovez and Mauricio Santos-Vega, Biomedical Engineering, Universidad de los Andes, Bogotá D.C., Colombia

Background/Question/Methods
We present a theoretical framework for coupling traditional epidemiological models with a behavioral dynamical model in the form of a game-theoretical setting. Individuals payoff is assumed to be coupled with the force of infection (FOI) and the transmission probability, which is proportional to the individuals behavior. First, we study the temporal dynamics with mechanistic susceptible-infected-susceptible individual (SIS) model, then we extended the results to SIS model where contacts (social network) are explicitly defined by using three type of networks: Scale-free, Watts-Strogatz and random networks.
Results/Conclusions
Our results shows that behavior change the final fraction of infected individuals and final fraction of cooperators or individuals who voluntarily take actions to reduce their transmission (epidemiological model and game theory model steady states respectively). However, when the dynamics were studied on a contact network we found that the topology of this network plays an essential role in controlling individuals behavior. Specifically, our results show that as the network is more connected (i.e. degree distribution is random or uniform (Watts-Strogatz or random networks respectively) disease burden is rapid and therefore individuals are not pushed to cooperate. However when the dynamics are studied in a scale free contact network, as degree distribution follow a power-law, we show that similarly as the mechanistic ODEs model individuals cooperate so their transmission probability is reduced and therefore, in the steady state, individuals cooperate.