| Abstract: |
| Details of human behavior are an important factor in the spread of infectious diseases. While in traditional epidemiological models most types of behavior (e.g., individuals` degree of compliance with social distancing recommendations) are pre-programmed, in Mean-Field Game (MFG) models individuals are assumed to make decisions (e.g., on their individual contact rates) rationally based on their current health status and the evolving epidemiological situation. But in most MFG epidemiological models, all players are assumed (a) to always have full information about their own health status, (b) to know in advance the planning horizon, (c) to be fully rational in planning their behavior, and (d) to be fully consistent in carrying out their plans. In this talk, we will show how each of these unrealistic assumptions can be relaxed, and how the resulting generalized MFG models can be treated numerically by solving a two-point boundary value problem for a system of approximating ODEs. Parts of this presentation will be based on joint papers with F. Buckley and C. Doebeli. |
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