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.
翻译:本研究还预测了疾病传播情况,为此,根据各种非药物性干预措施,如戴面罩、社会失常和锁定等,在时间和时间上对传播速度进行了新的制定,从而在各种非药物性干预措施方面,如戴防疫面具、社会失常和锁定等,在应对减缓COVID-19的资源分配挑战的同时,对无症状者的人数进行新的多阶段诊断性模型审查,同时优化资源的分配,例如通风机,以最大限度地减少新感染者和死亡者的预期总数;风险传染值(CVaR)也被纳入多阶段平均风险模型,以便在各种非药物性干预措施中,如戴面罩、社会失常和闭合等,对传染性流行病的人数进行权衡;我们利用多阶段性风险传播地点的定位性模型,对无症状者人数进行不同的预测性评估;在控制CVID-19的地理分布时,对新感染和已死亡者进行优化的疾病传播能力分配;在高影响地区,对新感染和高影响地区,对新诊断性移徙结果进行大幅校正。