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Our publications keep professionals working across the public, private, and academic sectors informed on the most important developments and issues in health security and biosecurity.

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Title:

Technology to advance infectious disease forecasting for outbreak management

Authors:
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
Date posted:
September 02, 2019
Publication type:
Article
Publication:
Nature Communications
DOI:
https://doi.org/10.1038/s41467-019-11901-7
Availability:
Open access
See also:
Introduction:
Present capacity to develop, evaluate, manufacture, distribute and administer effective medical countermeasures (e.g., vaccines, diagnostics, therapeutics) is inadequate to meet the burden of both recurrent and emerging outbreaks of infectious diseases. When such interventions are unavailable, public health measures (e.g., contact tracing, outbreak investigations, social distancing) and supportive clinical care remain the only feasible tools to slow an emerging outbreak. Decision-making under such circumstances can be greatly improved by the use of appropriate data and advanced analytics such as infectious disease modeling or machine learning. Furthermore, these analyses can guide decision-making when medical countermeasures become available, allowing them to be used in more effective ways. Data analyses already underpin public health actions such as anticipating resource requirements, refining situational awareness and monitoring control efforts2,3,4,5. New applications of data science and statistical analyses to disease outbreaks could provide support to decision-makers during public health crises.

 

 

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