School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom.
Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Palma de Mallorca, Spain.
PLoS One. 2020 Oct 21;15(10):e0241027. doi: 10.1371/journal.pone.0241027. eCollection 2020.
As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.
随着 COVID-19 病例数量的不断增加，当地的卫生服务机构面临着大量需要重症监护的患者的压力。我们开发并实施了一种算法，以提供最佳的重新路由策略，将需要重症监护病房（ICU）或呼吸机的患者进行转移，同时考虑到转移的可行性。我们使用来自英国和西班牙的真实数据验证了我们的方法。在英国，我们考虑了国民保健制度信托机构的水平，并定义了一个 4 正则几何图形，该图形表示任何给定信托机构的四个最近邻。在西班牙，我们对医疗保健系统进行了自治社区的粗粒度处理，并提取了类似的联系网络。通过随机搜索优化，我们确定了最佳的负载共享策略，其中最小化的成本函数基于超过容量的 ICU 单位的总数。我们的框架是通用和灵活的，可以考虑其他标准、替代成本函数，并可以扩展到 ICU 单位或呼吸机以外的其他资源。假设 ICU 的需求是均匀的，我们表明，在英国，通过算法的单个步骤，就有可能为多达 1000 例额外病例提供 ICU 服务。在更现实和异质的需求下，我们的方法仅通过本地共享就能够在西班牙系统中平衡大约 600 张床位，而通过全国共享则能够平衡超过 1300 张床位，这可能挽救了大量原本无法获得 ICU 服务的生命。
