Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia.

ABSTRACT: To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale,

SUBMITTER: Pan Y 

PROVIDER: S-EPMC8362659 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature