  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 and
deceased people. The Conditional Value at Risk (CVaR) is also incorporated into
the multi-stage mean-risk model to allow for a trade-off between the weighted
expected loss due to the outbreak and the expected risks associated with
experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk
epidemics-ventilator-logistics model to the case of controlling the COVID-19 in
highly-impacted counties of New York and New Jersey. We calibrate, validate,
and test our model using actual infection, population, and migration data. The
results indicate that short-term migration influences the transmission of the
disease significantly. The optimal number of ventilators allocated to each
region depends on various factors, including the number of initial infections,
disease transmission rates, initial ICU capacity, the population of a
geographical location, and the availability of ventilator supply. Our
data-driven modeling framework can be adapted to study the disease transmission
dynamics and logistics of other similar epidemics and pandemics.
