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.org2020, 2020120712. https://doi.org/10.20944/preprints202012.0712.v1.
Ingber, L. Forecasting COVID-19 with Importance-Sampling and Path-Integrals. Preprints.org 2020, 2020120712. https://doi.org/10.20944/preprints202012.0712.v1.
Cite as:
Ingber, L. Forecasting COVID-19 with Importance-Sampling and Path-Integrals. Preprints.org2020, 2020120712. https://doi.org/10.20944/preprints202012.0712.v1.
Ingber, L. Forecasting COVID-19 with Importance-Sampling and Path-Integrals. Preprints.org 2020, 2020120712. https://doi.org/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.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
