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科学家开发出COVID-19移动网络预测模型
作者:小柯机器人 发布时间:2020/11/11 22:37:39

美国斯坦福大学Jure Leskovec课题组开发出COVID-19移动网络预测模型。相关论文于2020年11月10日在线发表在《自然》杂志上。

研究人员引入了一个集成人口SEIR模型,该模型集成了精细的动态移动网络来模拟SARS-CoV-2在美国10个最大都市统计区域中的传播。这个移动网络从手机数据中提取,可以映射9800万人从社区(人口普查区块或CBG)到餐馆和宗教场所等兴趣点(POI)的每小时运动,并将54万个CBG与55.4亿个POI相连。
 
研究人员显示,通过集成这些网络,尽管人口行为随时间发生了重大变化,但相对简单的SEIR模型仍可以准确地拟合实际案例的轨迹。这个模型预测,少数“超级传播者” POI会导致大部分感染,并且限制每个POI的最大占用率比统一减少移动性更有效。
 
这个模型还仅根据流动性差异正确预测了处于不利地位的种族和社会经济群体中的较高感染率。研究人员发现,处于不利地位的群体无法大幅降低流动性,他们访问的POI更拥挤,因此,风险更高。通过捕获在哪些位置谁被感染,这个模型支持详细的分析,从而可为COVID-19提供更有效和公平的政策响应。
 
据悉,COVID-19大流行极大地改变了人类的迁徙方式,因此需要流行病学模型来捕捉迁徙变化对病毒传播的影响。
 
附:英文原文

Title: Mobility network models of COVID-19 explain inequities and inform reopening

Author: Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, Jure Leskovec

Issue&Volume: 2020-11-10

Abstract: The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

DOI: 10.1038/s41586-020-2923-3

Source: https://www.nature.com/articles/s41586-020-2923-3

期刊信息

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html