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

被引:36
|
作者
Biggerstaff, Matthew [1 ,2 ]
Slayton, Rachel B. [1 ,2 ]
Johansson, Michael A. [1 ,2 ]
Butler, Jay C. [2 ]
机构
[1] US Ctr Dis Control & Prevent, COVID 19 Response, Atlanta, GA USA
[2] US Ctr Dis Control & Prevent, Off Deputy Director Infect Dis, Atlanta, GA USA
关键词
COVID-19; forecasting; modeling; pandemic; public health; UNITED-STATES;
D O I
10.1093/cid/ciab673
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Modeling has informed public health decision making and policy development throughout the COVID-19 response. CDC has launched the Infectious Disease Modeling and Analytics Initiative to continue to enhance the use of modeling during public health emergencies. 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.
引用
收藏
页码:913 / 917
页数:5
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