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
相关论文
共 32 条
  • [1] Estimating the numbers of pregnant women infected with Zika virus and infants with congenital microcephaly in Colombia, 2015-2017
    Adamski, Alys
    Bertolli, Jeanne
    Castaneda-Orjuela, Carlos
    Devine, Owen J.
    Johansson, Michael A.
    Gonzalez Duarte, Maritza Adegnis
    Farr, Sherry L.
    Tinker, Sarah C.
    Mercado Reyes, Marcela Maria
    Tong, Van T.
    Pacheco Garcia, Oscar Eduardo
    Valencia, Diana
    Cuellar Ortiz, Diego Alberto
    Honein, Margaret A.
    Jamieson, Denise J.
    Ospina Martinez, Martha Lucia
    Gilboa, Suzanne M.
    [J]. JOURNAL OF INFECTION, 2018, 76 (06) : 529 - 535
  • [2] Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19
    Biggerstaff, Matthew
    Cowling, Benjamin J.
    Cucunuba, Zulma M.
    Dinh, Linh
    Ferguson, Neil M.
    Gao, Huizhi
    Hill, Verity
    Imai, Natsuko
    Johansson, Michael A.
    Kada, Sarah
    Morgan, Oliver
    Piontti, Ana Pastore Y.
    Polonsky, Jonathan A.
    Prasad, Pragati Venkata
    Quandelacy, Talia M.
    Rambaut, Andrew
    Tappero, Jordan W.
    Vandemaele, Katelijn A.
    Vespignani, Alessandro
    Warmbrod, K. Lane
    Wong, Jessica Y.
    [J]. EMERGING INFECTIOUS DISEASES, 2020, 26 (11) : E1 - E14
  • [3] BORCHERING R, 2021, MMWR MORB MORTAL WKL
  • [4] Brooks LC, COMP ENSEMBLE APPROA
  • [5] Household Transmission of 2009 Pandemic Influenza A (H1N1) Virus in the United States
    Cauchemez, Simon
    Donnelly, Christl A.
    Reed, Carrie
    Ghani, Azra C.
    Fraser, Christophe
    Kent, Charlotte K.
    Finelli, Lyn
    Ferguson, Neil M.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2009, 361 (27) : 2619 - 2627
  • [6] Centers for Disease Control and Prevention, 2021, SCI BRIEF BACKGR RAT
  • [7] Centers for Disease Control and Prevention, COVID 19 MATH MOD CO
  • [8] Centers for Disease Control and Prevention, PRIOR COVID 19 CONT
  • [9] Centers for Disease Control and Prevention, ACIP OCT 2020 PRES S
  • [10] Centers for Disease Control and Prevention, OPT RED QUAR CONT PE