Infectious disease surveillance needs for the United States: lessons from COVID-19.
CoRR(2023)
摘要
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.
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