Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Background:Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations.Objective:Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems.Method:ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19.Results:Results of detailed calculations have led to new results and insights not previously obtained.Conclusion:These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.