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