TY - JOUR
T1 - Using big data to monitor the introduction and spread of Chikungunya, Europe, 2017
AU - Rocklöv, Joacim
AU - Tozan, Yesim
AU - Ramadona, Aditya
AU - Sewe, Maquines O.
AU - Sudre, Bertrand
AU - Garrido, Jon
AU - De Saint Lary, Chiara Bellegarde
AU - Lohr, Wolfgang
AU - Semenza, Jan C.
N1 - Funding Information:
J.R. received partial funding from the Swedish Research Council for Sustainable Development (FORMAS) (no. 2017-01300). The
Publisher Copyright:
© 2019, Centers for Disease Control and Prevention (CDC). All rights reserved.
PY - 2019/6
Y1 - 2019/6
N2 - With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.
AB - With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.
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U2 - 10.3201/eid2506.180138
DO - 10.3201/eid2506.180138
M3 - Article
C2 - 31107221
AN - SCOPUS:85066038092
SN - 1080-6040
VL - 25
SP - 1041
EP - 1049
JO - Emerging Infectious Diseases
JF - Emerging Infectious Diseases
IS - 6
ER -