Department of Logistics, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
PLoS One. 2021 Mar 5;16(3):e0248053. doi: 10.1371/journal.pone.0248053. eCollection 2021.
The tumultuous inception of an epidemic is usually accompanied by difficulty in determining how to respond best. In developing nations, this can be compounded by logistical challenges, such as vaccine shortages and poor road infrastructure. To provide guidance towards improved epidemic response, various resource allocation models, in conjunction with a network-based SEIRVD epidemic model, are proposed in this article. Further, the feasibility of using drones for vaccine delivery is evaluated, and assorted relevant parameters are discussed. For the sake of generality, these results are presented for multiple network structures, representing interconnected populations-upon which repeated epidemic simulations are performed. The resource allocation models formulated maximise expected prevented exposures on each day of a simulated epidemic, by allocating response teams and vaccine deliveries according to the solutions of two respective integer programming problems-thereby influencing the simulated epidemic through the SEIRVD model. These models, when compared with a range of alternative resource allocation strategies, were found to reduce both the number of cases per epidemic, and the number of vaccines required. Consequently, the recommendation is made that such models be used as decision support tools in epidemic response. In the absence thereof, prioritizing locations for vaccinations according to susceptible population, rather than total population or number of infections, is most effective for the majority of network types. In other results, fixed-wing drones are demonstrated to be a viable delivery method for vaccines in the context of an epidemic, if sufficient drones can be promptly procured; the detrimental effect of intervention delay was discovered to be significant. In addition, the importance of well-documented routine vaccination activities is highlighted, due to the benefits of increased pre-epidemic immunity rates, and targeted vaccination.
传染病的爆发通常伴随着如何做出最佳应对的困难。在发展中国家，这可能会因疫苗短缺和道路基础设施差等后勤挑战而更加复杂。为了提供改善传染病应对的指导，本文提出了各种资源分配模型，以及基于网络的 SEIRVD 传染病模型。此外，还评估了使用无人机进行疫苗分发的可行性，并讨论了各种相关参数。为了通用性，针对多个代表相互关联的人群的网络结构呈现了这些结果，在这些网络结构上进行了多次重复的传染病模拟。所制定的资源分配模型通过根据两个各自的整数规划问题的解决方案分配应对小组和疫苗分发，使每个模拟传染病日的预期预防暴露最大化，从而通过 SEIRVD 模型影响模拟传染病。与一系列替代资源分配策略相比，这些模型被发现可以减少每个传染病的病例数和所需疫苗的数量。因此，建议将这些模型用作传染病应对的决策支持工具。在缺乏这些模型的情况下，根据易感人群而不是总人口或感染人数为疫苗接种地点确定优先级，对大多数网络类型最为有效。在其他结果中，如果能够迅速采购到足够数量的固定翼无人机，则证明其是一种可行的传染病疫苗分发方法；干预延迟的不利影响被发现非常显著。此外，还强调了记录良好的常规疫苗接种活动的重要性，因为这可以提高流行前免疫率和针对性疫苗接种的效益。
