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数据丰富的世界中的数学流行病学

Mathematical epidemiology in a data-rich world.

作者信息

Arino Julien

机构信息

Department of Mathematics & Data Science NEXUS, University of Manitoba, Winnipeg, Manitoba, Canada.

出版信息

Infect Dis Model. 2020 Jan 7;5:161-188. doi: 10.1016/j.idm.2019.12.008. eCollection 2020.

Abstract

I discuss the acquisition and use of "background" data in mathematical epidemiology models, advocating a proactive approach to the incorporation of said data. I illustrate various mechanisms for acquiring data, mainly from open data sources. I also discuss incorporating this data into models.

摘要

我讨论了数学流行病学模型中“背景”数据的获取与使用,主张采取积极主动的方法纳入上述数据。我阐述了主要从开放数据源获取数据的各种机制。我还讨论了将这些数据纳入模型的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4a0/6962708/ddc9cdb99007/fx1.jpg

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