COVID-19增长中非药物措施的优化 神经网络

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摘要翻译：
3月19日，世界卫生组织宣布疫情爆发。通过这种全球传播，许多国家目睹了通过严格的大规模隔离或封锁措施得到控制的确诊病例呈指数级增长。然而，一些人通过不同的行动时间表阻止了这种指数增长。目前，随着一些人继续解决增长问题，另一些人试图安全地取消限制，同时避免死灰复燃。本研究试图通过一种新的软计算方法来量化政府措施在缓解新型冠状病毒病毒传播方面的影响，该方法同时使用神经网络模型来预测累积感染者的每日斜率增长，并通过政府限制时间序列的参数化来理解最佳缓解措施集。已经收集了意大利和台湾这两个地区的数据，以模拟政府在旅行、测试和执行社交距离措施以及人员连接和遵守政府行动方面的限制。研究发现，更大、更早的检测活动和更严格的入境限制对两个地区都有利，导致确诊病例明显减少。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相结合，从而支持台湾缺乏全国性封锁。纯粹以数据驱动的方法得出的结果与数学流行病学模型的主要结论一致，证明了拟议的方法具有价值，数据本身就包含了为决策者提供信息的宝贵知识。
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英文标题：
《Optimisation of non-pharmaceutical measures in COVID-19 growth via
  neural networks》
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作者：
Annalisa Riccardi, Jessica Gemignani, Francisco Fern\'andez-Navarro,
  Anna Heffernan
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
--

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英文摘要：
  On 19th 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 lockdown 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 understanding the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in traveling, 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. 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.
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PDF链接：
https://arxiv.org/pdf/2006.08867

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关键词：神经网络 OVID 神经网 Quantitative Restrictions exponential 方面 confirmed 模型