Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.
Aedes
/ virology
Animals
Big Data
Chikungunya Fever
/ epidemiology
Chikungunya virus
Climate
Communicable Diseases, Emerging
/ epidemiology
Data Mining
Disease Outbreaks
Europe
/ epidemiology
Geography, Medical
History, 21st Century
Humans
Mosquito Vectors
/ virology
Population Dynamics
Public Health Surveillance
Seasons
Aedes albopictus
Europe
arbovirus
big data
chikungunya
data science
human mobility
social media
vector-borne infections
vectorial capacity
viruses
Journal
Emerging infectious diseases
ISSN: 1080-6059
Titre abrégé: Emerg Infect Dis
Pays: United States
ID NLM: 9508155
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
entrez:
21
5
2019
pubmed:
21
5
2019
medline:
24
12
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31107221
doi: 10.3201/eid2506.180138
pmc: PMC6537727
doi:
Types de publication
Historical Article
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1041-1049Références
PLoS One. 2011 May 04;6(5):e19467
pubmed: 21573238
Euro Surveill. 2014 Apr 03;19(13):
pubmed: 24721535
Health Aff (Millwood). 2014 Sep;33(9):1523-30
pubmed: 25201656
Emerg Infect Dis. 2003 Jan;9(1):86-9
pubmed: 12533286
Emerg Infect Dis. 2016 Apr;22(4):581-9
pubmed: 26982104
Sci Rep. 2015 Mar 09;5:8923
pubmed: 25747871
PLoS Negl Trop Dis. 2014 Dec 04;8(12):e3278
pubmed: 25474491
Nature. 2009 Feb 19;457(7232):1012-4
pubmed: 19020500
Genome Announc. 2017 Dec 7;5(49):
pubmed: 29217791
Ann N Y Acad Sci. 2016 Oct;1382(1):73-83
pubmed: 27434370
PLoS Comput Biol. 2014 Apr 17;10(4):e1003581
pubmed: 24743682
J Infect Dis. 2016 Dec 1;214(suppl_4):S380-S385
pubmed: 28830112
PLoS Negl Trop Dis. 2009 Jul 21;3(7):e481
pubmed: 19621090
Proc Natl Acad Sci U S A. 2015 Sep 1;112(35):11114-9
pubmed: 26283349
PLoS Negl Trop Dis. 2019 Apr 15;13(4):e0007298
pubmed: 30986218
Lancet. 2014 Feb 8;383(9916):514
pubmed: 24506907
EBioMedicine. 2016 Jul;9:250-256
pubmed: 27344225
PLoS One. 2010 Nov 29;5(11):e14118
pubmed: 21124761
Science. 2016 Apr 15;352(6283):345-349
pubmed: 27013429
PLoS One. 2015 Jul 08;10(7):e0131469
pubmed: 26154597
Proc Natl Acad Sci U S A. 2016 Jun 7;113(23):6421-6
pubmed: 27217564
Nature. 2013 Feb 14;494(7436):155-6
pubmed: 23407515
J Infect Dis. 2016 Dec 1;214(suppl_4):S375-S379
pubmed: 28830113
JMIR Public Health Surveill. 2015 Jan-Jun;1(1):e5
pubmed: 27014744
PLoS One. 2013 Jul 24;8(7):e69305
pubmed: 23894447
FEMS Microbiol Lett. 2018 Feb 1;365(2):
pubmed: 29149298