Infectious disease surveillance needs for the United States

Lessons from Covid-19

Bibliographic Data

ID22070908
AuthorsMarc Lipsitch (0000-0003-1504-9213, Centers for Disease Control and Prevention, corresponding author), Mary T Bassett (0000-0002-7810-4163), John S Brownstein (0000-0001-8568-5317, Boston Children's Hospital), Peter Elliott (0000-0002-7511-5684, Imperial College London), Paul Elliott, David W Eyre (0000-0001-5095-6367, University of Oxford), David Eyre, M Kate Grabowski (0000-0002-4451-3436, Johns Hopkins University), James A Hay (0000-0002-1998-1844, University of Oxford), Michael A Johansson (0000-0002-5090-7722, Centers for Disease Control and Prevention), Stephen M Kissler (0000-0001-6000-8387, University of Colorado Boulder), Daniel B Larremore (0000-0001-5273-5234, University of Colorado Boulder), Jennifer E Layden (0000-0002-1264-2348, Centers for Disease Control and Prevention), Justin Lessler (0000-0002-9741-8109), Ruth Lynfield (0000-0002-7741-1284, Minnesota Department of Health), Duncan MacCannell (0000-0002-8869-1840, Centers for Disease Control and Prevention), Lawrence C Madoff (0000-0003-2589-7777, Massachusetts Department of Public Health), C Jessica E Metcalf (0000-0003-3166-7521, Princeton University), Lauren Ancel Meyers (0000-0002-5828-8874, The University of Texas at Austin), Sylvia K Ofori (0000-0002-9961-9975, Center for Disease Dynamics, Economics & Policy), Celia Quinn (New York City Department of Health and Mental Hygiene), Ana I Bento (0000-0001-7728-007X, Cornell University), Nicholas G Reich (0000-0003-3503-9899, University of Massachusetts Amherst), Steven Riley (0000-0001-7904-4804), Roni Rosenfeld (0000-0002-3274-5862), Matthew H Samore (0000-0002-4862-9196, University of Utah), Rangarajan Sampath (0000-0002-2951-6372, Siemens Healthcare (United States)), Rachel B Slayton (0000-0003-4699-8040, Centers for Disease Control and Prevention), David L Swerdlow (0000-0003-1653-6646, Center for Disease Dynamics, Economics & Policy), Shaun Truelove (0000-0003-0538-0607), Jay K Varma (0000-0003-4764-0294, Siga Technologies (United States)), Yonatan H Grad (0000-0001-5646-1314, Harvard University)
Year2024
Volume12
Pages1408193-1408193
Publication date2024-07-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1408193
PMID39076420
OpenAlexW4400651088
LanguageEN
Citations received1
References cited91

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity

2019-20 coronavirus outbreak · Business · Data science · Disease · Disease surveillance · Pandemic · Political science · Public health · Upgrade · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Viral Infections and Outbreaks Research · Virology

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