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The spontaneous behavioral changes of the agents during an epidemic can havesignificant effects on the delay and the prevalence of its spread. In thiswork, we study a social distancing game among the agents of a population, whodetermine their social interactions during the spread of an epidemic. Theinterconnections between the agents are modeled by a network and localinteractions are considered. The payoffs of the agents depend on their benefitsfrom their social interactions, as well as on the costs to their health due totheir possible contamination. The information available to the agents duringthe decision making plays a crucial role in our model. We examine two extremecases. In the first case, the agents know exactly the health states of theirneighbors and in the second they have statistical information for the globalprevalence of the epidemic. The Nash equilibria of the games are studied and,interestingly, in the second case the equilibrium strategies for an agent areeither full isolation or no social distancing at all. Experimental studies arepresented through simulations, where we observe that in the first case ofperfect local information the agents can affect significantly the prevalence ofthe epidemic with low cost for their sociability, while in the second case theyhave to pay the burden of not being well informed. Moreover, the effects of theinformation quality (fake news), the health care system capacity and thenetwork structure are discussed and relevant simulations are provided, whichindicate that these parameters affect the size, the peak and the start of theoutbreak, as well as the possibility of a second outbreak.

Games of Social Distancing during an Epidemic: Local vs Statistical Information