Infectious disease surveillance needs for the United States: lessons from Covid-19

Marc Lipsitch, Mary T. Bassett, John S. Brownstein, Paul Elliott, David Eyre, M. Kate Grabowski, James A. Hay, Michael A. Johansson, Stephen M. Kissler, Daniel B. Larremore, Jennifer E. Layden, Justin Lessler, Ruth Lynfield, Duncan MacCannell, Lawrence C. Madoff, C. Jessica E. Metcalf, Lauren A. Meyers, Sylvia K. Ofori, Celia Quinn, Ana I. BentoNicholas G. Reich, Steven Riley, Roni Rosenfeld, Matthew H. Samore, Rangarajan Sampath, Rachel B. Slayton, David L. Swerdlow, Shaun Truelove, Jay K. Varma, Yonatan H. Grad

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1408193
JournalFrontiers in Public Health
Volume12
DOIs
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health

Keywords

  • COVID-19
  • infectious diseases
  • mathematical model
  • pandemic
  • public health
  • surveillance and forecast system

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