COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk.

ABSTRACT: This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected an

SUBMITTER: Yin X 

PROVIDER: S-EPMC8632406 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature