Big Data for Infectious Disease Surveillance and Modeling

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Abstract
We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.
Funding Information
  • RAPIDD (Research And Policy for Infectious Diseases Dynamics)
  • National Institutes of Health
  • European Commission
  • Lundbeck Foundation
  • in-house research program of Fogarty International Center
  • Defense Threat Reduction Agency
  • National Institutes of Health (MIDAS-U54-GM111274)

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