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

Abstract

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 described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

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

“…There is not "one size fits all" in forecasting different systems. This was demonstrated for three systems (Ingber, 2020c), where the author has addressed multiple projects across multiple disciplines using these tools: 72 papers/reports/lectures in neuroscience, e.g. (Ingber, 2018a(Ingber, , 2020b, 31 papers/reports/lectures in finance, e.g.…”