Searching vaccination strategy with surrogate-assisted evolutionary computing

Agent-based stochastic simulation is an established approach to study infectious diseases. Its advantage is the flexibility to incorporate important concepts. The effect of various mitigation strategies has been demonstrated using simulation models. Most of the previous studies compared a few options with a few selected scenarios. We propose to use genetic algorithms to search for the best vaccination strategy for a given scenario with the simulation program as fitness scorer. Vaccination efficacy varies significantly. Therefore, the real challenge is to find a good strategy without the knowledge of it. The simulation software is efficient, yet still takes three minutes to complete a simulation run with Taiwan population. We use surrogate to speed up the search about 1000 times. The surrogate has the average of the absolute value of error around 0.284 percent and the rank correlation coefficient is greater than 0.98 for all the scenarios except one. The optimal solution with surrogate has fitness value very close to use simulations. The difference is generally less than one percent. We envision that an autonomous software searches through the huge scenario space with the help of surrogate function and adaptively executes simulation program to revise the surrogate function to produce higher fidelity surrogate and better search results.