Suppr超能文献

新冠病毒传播建模和预测面临的挑战。

The challenges of modeling and forecasting the spread of COVID-19.

机构信息

Department of Mathematics, University of California, Los Angeles, CA 90095;

Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095.

出版信息

Proc Natl Acad Sci U S A. 2020 Jul 21;117(29):16732-16738. doi: 10.1073/pnas.2006520117. Epub 2020 Jul 2.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.

摘要

2019 年冠状病毒病(COVID-19)大流行使传染病建模成为全球公共政策制定的前沿。尽管如此,对 COVID-19 的传播进行建模和预测仍然是一项挑战。在这里,我们详细介绍了三种用于预测和评估大流行进程的区域规模模型。这项工作证明了简洁模型在早期数据中的实用性,并为深入了解其进程提供了一个可用于制定政策的框架。我们展示了如何将这些模型相互连接,并与特定地区的时间序列数据连接起来。这些模型能够衡量和预测社交距离隔离的影响,强调了在没有疫苗或抗病毒疗法的情况下放松非药物公共卫生干预措施的危险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d7/7382213/9bb5f0ba6b74/pnas.2006520117fig01.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验