Department of Information Technology & Operations Management, College of Business, Florida Atlantic University, Boca Raton, FL, USA.
Department of Business Information Technology, Pamplin College of Business, Virginia Tech, Blacksburg, VA, USA.
Risk Anal. 2022 Jan;42(1):206-220. doi: 10.1111/risa.13707. Epub 2021 Feb 13.
The worldwide healthcare and economic crisis caused by the COVID-19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID-19.
由 COVID-19 大流行引发的全球医疗保健和经济危机突显了深入了解投资于减轻疫情风险的必要性。为了解决这个问题，我们构建了一个数学模型，以优化对两种减轻疫情传播风险措施的投资：预防/控制措施和治疗/恢复措施。新模型明确考虑了个体网络的特征，这是疫情传播的关键因素。后续分析表明，为了应对可能造成重大负面影响的疫情，对任何一类措施的最优投资都随着连接性和内在损失的增加而增加，但它被限制在总潜在损失的一小部分。然而，当必须在两种措施之间分配固定和有限的缓解投资时，当投资上限增加时，预防和控制投资的最佳比例增加，而当网络连接性降低时，预防和控制投资的最佳比例增加。我们的研究结果与现有研究一致，可以用来正确解释过去大流行中发生的情况，并为未来和正在发生的事件（如 COVID-19）提供启示。
