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

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. T

SUBMITTER: Fast SM 

PROVIDER: S-EPMC7088430 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature