TY - JOUR
T1 - Big Data for Infectious Disease Surveillance and Modeling
AU - Bansal, Shweta
AU - Chowell, Gerardo
AU - Simonsen, Lone
AU - Vespignani, Alessandro
AU - Viboud, Cecile
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
KW - big data
KW - infectious diseases
KW - surveillance
KW - disease models
KW - transmission
KW - social media
KW - Internet search queries
KW - electronic health records
KW - mobility
KW - adverse events
KW - outbreaks
U2 - 10.1093/infdis/jiw400
DO - 10.1093/infdis/jiw400
M3 - Journal article
C2 - 28830113
SN - 0022-1899
VL - 214
SP - S375-S379
JO - Journal of Infectious Diseases
JF - Journal of Infectious Diseases
IS - Supplement 4
ER -
