“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.
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
- PLOS Biologue blog, Media Response: Forecasting Diseases Using Wikipedia, posted December 31, 2014
- “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
- “Wikipedia usage accurately estimated the week of peak influenza-like illness activity 17% more often than Google Flu Trends data,” according to the PLOS Computational Biology research article, Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time, published April 17, 2014
- “People infect each other with opinions about vaccinations,” says Marcel Salathé in the PLOS Public Health Perspectives blog, Twitter Study of Vaccine Messages Study Show Opinions are Contagious But in Unexpected Ways, posted April 5, 2013
- “If clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased,” conclude the authors of the PLOS Computational Biology research article, Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control, published October 13, 2011
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