Predicting social response to infectious disease outbreaks from internet-based news streams

Predicting social response to infectious disease outbreaks from internet-based news streams Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

Predicting social response to infectious disease outbreaks from internet-based news streams

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Business and Management; Operations Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
D.O.I.
10.1007/s10479-017-2480-9
Publisher site
See Article on Publisher Site

Abstract

Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread.

Journal

Annals of Operations ResearchSpringer Journals

Published: Mar 29, 2017

References

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