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Improving Pandemic Response: Employing Mathematical Modeling to Confront Coronavirus Disease 2019
Author(s) -
Matthew Biggerstaff,
Rachel B. Slayton,
Michael A. Johansson,
Jay C. Butler
Publication year - 2021
Publication title -
clinical infectious diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.44
H-Index - 336
eISSN - 1537-6591
pISSN - 1058-4838
DOI - 10.1093/cid/ciab673
Subject(s) - pandemic , public health , infectious disease (medical specialty) , government (linguistics) , analytics , medicine , situation awareness , disease , disease surveillance , work (physics) , disease control , covid-19 , data science , public relations , environmental health , computer science , political science , engineering , nursing , mechanical engineering , linguistics , philosophy , pathology , aerospace engineering
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.

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