Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases
Fig 16
a) The agent aims to minimize the loss function defined as the peak of the active cases. The optimization variables are the ratio of three roles that must be quarantined, and the ratios are constrained to be bounded from above and sum up to a constant value. The upper bound constraints are placed to take into account the cost of shutting down the economy and the trivial solution that is quarantining all individuals. The graph shows the result for 200 trials. The blue dashed line is the lower envelope of the cost produced by the discovered solution at every trial. Each point from A to H corresponds to the minimum cost up to that trial. The discovered policy associated with each of these points can be seen in Table 8. b) The curves that show the number of active cases versus time for each round of the optimization is plotted in this figure. These are actually the curves we need to flatten to protect the healthcare system against overloading. It can be seen that the discovered strategy with the least cost corresponds to the flattest curve. (The population size is reduced by a scale of 10 to boost the computation time.)