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Researchers Changing the Way We Respond to Epidemics with Wikipedia and Twitter

“A global disease-forecasting system will change the way we respond to epidemics.”  Dr. Sara Del Valle, Los Alamos National Laboratory

The media and broad scientific community have taken note of a fast-growing segment of research known as digital epidemiology. Examples:

  • A system to forecast 28 days in advance where influenza will strike hardest based on localized Wikipedia searches
  • A basis for predicting which communities will see more cases of flu resulting from vaccination decisions as revealed by geographically-based Twitter sentiments.
map
Figure 1. Map generated by more than 250 million public tweets with high-resolution location information, March 2011 – January 2012. Inset shows greater Los Angeles area. Brightness of color corresponds to geographic density of tweets. doi:10.1371/journal.pcbi.1002616.g001

Described by PLOS Computational Biology Associate Editor Marcel Salathé as a “mix of exciting science, modern everyday technology and public health,” this interdisciplinary approach is developing just in time to meet increased demand for improved forecasting of infectious disease outbreaks before they reach epidemic or pandemic stages.

A significant driver for the quantitative and qualitative breakthroughs setting these papers apart from previous work in the field was the openness of the raw data underlying their findings and the source codes underlying their models, as well as the openness of the research processes and final publications.

PLOS journals and blogs actively cover this transformational research:

  • “Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world,” write PLOS Computational Biology Editors in Editors Outlook: Digital Epidemiology, published 26 Jul 2012
  • “In the same way we check the weather each morning, individuals and public health officials can monitor disease incidence and plan for the future based on today’s forecast,” says Sara Del Valle, coauthor of the PLOS Computational Biology research article, Global Disease Monitoring and Forecasting with Wikipedia, published November 13, 2014

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