Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Forecasting COVID-19 with Importance-Sampling and Path-Integrals

Version 1 : Received: 25 December 2020 / Approved: 29 December 2020 / Online: 29 December 2020 (03:03:36 CET)
Version 2 : Received: 1 January 2021 / Approved: 4 January 2021 / Online: 4 January 2021 (13:29:01 CET)

How to cite: Ingber, L. Forecasting COVID-19 with Importance-Sampling and Path-Integrals. Preprints 2020, 2020120712 (doi: 10.20944/preprints202012.0712.v1). Ingber, L. Forecasting COVID-19 with Importance-Sampling and Path-Integrals. Preprints 2020, 2020120712 (doi: 10.20944/preprints202012.0712.v1).

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 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.

Subject Areas

path integral; importance sampling; financial options; combat analysis

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