Riccardi Annalisa, Gemignani Jessica, Fernandez-Navarro Francisco, Heffernan Anna
Department of Mechanical and Aerospace EngineeringUniversity of Strathclyde Glasgow G1 1XQ U.K.
Department of Developmental Psychology and SocialisationUniversità di Padova 35131 Padova Italy.
IEEE Trans Emerg Top Comput Intell. 2021 Jan 21;5(1):79-91. doi: 10.1109/TETCI.2020.3046012. eCollection 2021 Feb.
On [Formula: see text] March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understand the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in travelling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown, i.e. earlier government actions could have contained the growth to a degree that a widespread lockdown would have been avoided, or at least delayed. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.
3月[具体日期：见原文]，世界卫生组织宣布新冠疫情为大流行。在疫情全球蔓延过程中，许多国家见证了确诊病例呈指数级增长，后通过严格的大规模隔离或其他措施得以控制。然而，有些国家通过不同的行动时间线，阻止了这种指数级增长。目前，一些国家仍在应对病例增长，另一些国家则试图在避免疫情卷土重来的同时安全解除限制措施。本研究旨在通过一种新颖的软计算方法量化政府行动对减轻SARS-CoV-2病毒传播的影响，该方法同时使用神经网络模型来预测累计感染病例的每日斜率增长，并使用优化器对政府限制时间序列进行参数化，以了解最佳的缓解行动组合。收集了意大利和台湾两个地区的数据，以模拟政府在旅行、检测和社会距离措施执行方面的限制，以及人员流动和对政府行动的遵守情况。研究发现，规模更大、更早开展的检测活动以及更严格的入境限制对两个地区都有益，确诊病例显著减少。有趣的是，这种情况与意大利更早但更温和地实施全国性限制措施相契合，从而支持了台湾未实施全国性封锁的做法，即更早的政府行动本可在一定程度上控制疫情增长，从而避免或至少推迟全面封锁。通过纯粹的数据驱动方法得出的结果与数学流行病学模型的主要发现一致，证明了所提出的方法具有价值，且仅数据本身就包含有价值的知识，可为决策者提供参考。
