Technology to advance infectious disease forecasting for outbreak management

Dylan B George; Wendy Taylor; Jeffrey Shaman; Caitlin Rivers; Brooke Paul; Tara O'Toole; Michael A Johansson; Lynette Hirschman; Matthew Biggerstaff; Jason Asher; Nicholas G Reich

doi:10.1038/s41467-019-11901-7

Technology to advance infectious disease forecasting for outbreak management

Nat Commun. 2019 Sep 2;10(1):3932. doi: 10.1038/s41467-019-11901-7.

Authors

Affiliations

¹ BNext, IQT Labs, Arlington, VA, USA. dylangeorge@gmail.com.
² Rockefeller Foundation, New York, NY, USA.
³ Climate and Health Program, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
⁴ Center for Health Security, Johns Hopkins University, Baltimore, MD, USA.
⁵ Taivara Ltd, Columbus, OH, USA.
⁶ BNext, IQT Labs, Arlington, VA, USA.
⁷ Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, PR, USA.
⁸ The MITRE Corporation, Bedford, MA, USA.
⁹ Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
¹⁰ Leidos, Reston, VA, USA.
¹¹ Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, USA.

Abstract

Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks.

MeSH terms

Communicable Diseases / epidemiology*
Computational Biology / methods
Disease Outbreaks / prevention & control*
Forecasting / methods*
Humans
Information Dissemination / methods
Public Health / methods*

Abstract

MeSH terms

Grants and funding