Improving Pandemic Response: Employing Mathematical Modeling to Confront Coronavirus Disease 2019

Author:

Biggerstaff Matthew12,Slayton Rachel B12,Johansson Michael A12,Butler Jay C2

Affiliation:

1. COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA

2. Office of the Deputy Director for Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA

Abstract

Abstract Modeling complements surveillance data to inform coronavirus disease 2019 (COVID-19) public health decision making and policy development. This includes the use of modeling to improve situational awareness, assess epidemiological characteristics, and inform the evidence base for prevention strategies. To enhance modeling utility in future public health emergencies, the Centers for Disease Control and Prevention (CDC) launched the Infectious Disease Modeling and Analytics Initiative. The initiative objectives are to: (1) strengthen leadership in infectious disease modeling, epidemic forecasting, and advanced analytic work; (2) build and cultivate a community of skilled modeling and analytics practitioners and consumers across CDC; (3) strengthen and support internal and external applied modeling and analytic work; and (4) working with partners, coordinate government-wide advanced data modeling and analytics for infectious diseases. These efforts are critical to help prepare the CDC, the country, and the world to respond effectively to present and future infectious disease threats.

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Microbiology (medical)

Reference32 articles.

1. The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis;Presanis;PLoS Med,2009

2. Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States;Cauchemez;N Engl J Med,2009

3. Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA;White;Influenza Other Respir Viruses,2009

4. Estimating the future number of cases in the Ebola epidemic—Liberia and Sierra Leone, 2014-2015;Meltzer;MMWR Surveill Summ,2014

5. Modeling in real time during the ebola response;Meltzer;MMWR Suppl,2016

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3