Forecasting COVID-19 with Importance-Sampling and Path-Integrals
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 suggested as being useful to fit COVID-19 data and to help predict spread or control of this pandemic. Multiple variables are considered, e.g., potentially including ethnicity, population density, obesity, deprivation, pollution, race, environmental temperature. 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. 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.
