Big Data for Infectious Disease Surveillance and Modeling

Shweta Bansal, Gerardo Chowell, Lone Simonsen, Alessandro Vespignani, Cécile Viboud

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftJournal of Infectious Diseases
Vol/bind214
Udgave nummersuppl 4
Sider (fra-til)S375-S379
ISSN0022-1899
DOI
StatusUdgivet - 1 nov. 2016
Udgivet eksterntJa

Citer dette

Bansal, Shweta ; Chowell, Gerardo ; Simonsen, Lone ; Vespignani, Alessandro ; Viboud, Cécile. / Big Data for Infectious Disease Surveillance and Modeling. I: Journal of Infectious Diseases. 2016 ; Bind 214, Nr. suppl 4. s. S375-S379.
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Bansal, S, Chowell, G, Simonsen, L, Vespignani, A & Viboud, C 2016, 'Big Data for Infectious Disease Surveillance and Modeling', Journal of Infectious Diseases, bind 214, nr. suppl 4, s. S375-S379. https://doi.org/10.1093/infdis/jiw400

Big Data for Infectious Disease Surveillance and Modeling. / Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile.

I: Journal of Infectious Diseases, Bind 214, Nr. suppl 4, 01.11.2016, s. S375-S379.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Big Data for Infectious Disease Surveillance and Modeling

AU - Bansal, Shweta

AU - Chowell, Gerardo

AU - Simonsen, Lone

AU - Vespignani, Alessandro

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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.

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