Glkobal disease-forecasting

“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 is 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. Read the articles here.