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Improving Pandemic Response: Employing Mathematical Modeling to Confront Coronavirus Disease 2019.
Biggerstaff, Matthew; Slayton, Rachel B; Johansson, Michael A; Butler, Jay C.
  • Biggerstaff M; COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Slayton RB; Office of the Deputy Director for Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Johansson MA; COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Butler JC; Office of the Deputy Director for Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Clin Infect Dis ; 74(5): 913-917, 2022 03 09.
Article in English | MEDLINE | ID: covidwho-1708595
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Clin Infect Dis Journal subject: Communicable Diseases Year: 2022 Document Type: Article Affiliation country: Cid

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Clin Infect Dis Journal subject: Communicable Diseases Year: 2022 Document Type: Article Affiliation country: Cid