Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI.

ABSTRACT: To quantificationally identify the optimal control measures for regulators to best minimize COVID-19's growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (countr

SUBMITTER: Chew AWZ 

PROVIDER: S-EPMC8832881 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature