Monitoring Pertussis Infections Using Internet Search Queries

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Zhang, Yuzhou ^a   Milinovich, Gabriel ^a   Xu, Zhiwei ^a   Bambrick, Hilary ^a   Mengersen, Kerrie ^a   Tong, Shilu ^a,b,c   Hu, Wenbiao ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b ANHUI MEDICAL UNIVERSITY   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDEMIC; EPIDEMIOLOGICAL MONITORING; HEALTH SURVEY; HUMAN; INCIDENCE; INTERNET; PERTUSSIS; SEARCH ENGINE; SEASON;

DISEASE OUTBREAKS; EPIDEMIOLOGICAL MONITORING; HUMANS; INCIDENCE; INTERNET; PUBLIC HEALTH SURVEILLANCE; SEARCH ENGINE; SEASONS; WHOOPING COUGH;

EID: 85028841413     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-11195-z     Document Type: Article

References (34)

1. Marzouqi, I., Richmond, P., Fry, S., Wetherall, J. & Mukkur, T. Development of improved vaccines against whooping cough: current status. Human vaccines 6, 543-553 (2010).
2. Fennelly, N. K. et al. Bordetella pertussis expresses a functional type III secretion system that subverts protective innate and adaptive immune responses. Infection and immunity 76, 1257-1266 (2008).
3. Australian Government. Australia's notifiable diseases status: Annual report of the National Notifiable Diseases Surveillance System - Vaccine preventable diseases, http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-cdi3502a9.htm (2011) (Date of access: 05/05/2016).
4. O'Connell, E. K., Zhang, G., Leguen, F., Llau, A. & Rico, E. Innovative Uses for Syndromic Surveillance. Emerging infectious diseases 16, 669 (2010).
5. Chan, E. H. et al. Global capacity for emerging infectious disease detection. Proceedings of the National Academy of Sciences 107, 21701-21706 (2010).
6. Triple, S. Assessment of syndromic surveillance in Europe. The Lancet 378, 1833-1834 (2011).
7. Milinovich, G. J., Williams, G. M., Clements, A. C. & Hu, W. Internet-based surveillance systems for monitoring emerging infectious diseases. The Lancet infectious diseases 14, 160-168 (2014).
8. Pervaiz, F., Pervaiz, M., Rehman, N. A. & Saif, U. FluBreaks: early epidemic detection from Google flu trends. Journal of medical Internet research 14, e125 (2012).
9. Nuti, S. V. et al. The use of google trends in health care research: a systematic review. PloS one 9, e109583 (2014).
10. Milinovich, G. J. et al. Using internet search queries for infectious disease surveillance: screening diseases for suitability. BMC infectious diseases 14, 690 (2014).
11. McGough, S. F., Brownstein, J. S., Hawkins, J. B. & Santillana, M. Forecasting Zika incidence in the 2016 Latin America outbreak combining traditional disease surveillance with search, social media, and news report data. PLoS neglected tropical diseases 11, e0005295 (2017).
12. Scarpino, S. V., Dimitrov, N. B. & Meyers, L. A. Optimizing provider recruitment for influenza surveillance networks. PLoS Comput Biol 8, e1002472 (2012).
13. The World Bank. Internet users (per 100 people), http://data.worldbank.org/indicator/IT.NET.USER.P2/countries/AU?display=default (2015) (Date of access: 25/05/2016).
14. The Nielsen Company. The Australian Online Consumer Landscape, http://www.ad-tech.com/sydney-uploads/nielsen-WhitePaper2011.pdf (Date of access: 14/05/2016) (2011).
15. StatCounter Global Stats. Top 5 seach engines in Australia from 2008 to 2013, http://gs.statcounter.com/#search-engine-AUyearly-2008-2013 (Date of access: 01/06/2016) (2013).
16. Postels-Multani, S., W von Koenig, C., Schmitt, H., Bock, H. & Bogaerts, H. Symptoms and complications of pertussis in adults. Infection 23, 139-142 (1995).
17. Huang, X. et al. Assessing the social and environmental determinants of pertussis epidemics in Queensland, Australia: a Bayesian spatio-temporal analysis. Epidemiology & Infection 145, 1221-1230 (2017).
18. Gambhir, M. et al. A change in vaccine efficacy and duration of protection explains recent rises in pertussis incidence in the United States. PLoS Comput Biol 11, e1004138 (2015).
19. Spokes, P. J., Quinn, H. E. & McAnulty, J. M. Review of the 2008-2009 pertussis epidemic in NSW: notifications and hospitalisations. New South Wales public health bulletin 21, 167-173 (2010).
20. Busselle, R., Reagan, J., Pinkleton, B. & Jackson, K. Factors affecting Internet use in a saturated-access population. Telematics and Informatics 16, 45-58 (1999).
21. Australian Bureau of Statistics. The National Regional Profile (NRP), http://stat.data.abs.gov.au/Index.aspx?DataSetCode=SEIFA-SSC (Date of access: 30/05/2016) (2011).
22. Ortiz, J. R. et al. Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends. PloS one 6, e18687 (2011).
23. Choi, H. & Varian, H. Predicting the present with Google Trends. Economic Record 88, 2-9 (2012).
24. Wongkoon, S., Jaroensutasinee, M. & Jaroensutasinee, K. Assessing the temporal modelling for prediction of dengue infection in northern and northeastern, Thailand. Tropical biomedicine 29, 339-348 (2012).
25. Midekisa, A., Senay, G., Henebry, G. M., Semuniguse, P. & Wimberly, M. C. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria journal 11, 165 (2012).
26. Ren, H. et al. The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China. BMC infectious diseases 13, 421 (2013).
27. Australian Bureau of Statistics. Household Use of Information Technology, Australia, 2012-13, http://www.abs.gov.au/ausstats/abs@ nsf/Lookup/8146.0Chapter12012-13 (2014) (Date of access: 11/05/2016).
28. Althouse, B. M., Ng, Y. Y. & Cummings, D. A. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis 5, e1258 (2011).
29. Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in big data analysis. Science 343, 1203-1205 (2014).
30. Butler, D. When Google got flu wrong. Nature 494, 155 (2013).
31. Yang, S., Santillana, M. & Kou, S. C. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences 112, 14473-14478, doi:10.1073/pnas.1515373112 (2015).
32. Boyle, J. R. et al. Prediction and surveillance of influenza epidemics. Medical journal of Australia 194, S28 (2011).
33. Pollett, S. et al. Validating the use of Google trends to enhance pertussis surveillance in California. PLoS currents 7 (2015).
34. Box, G. E., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. Time series analysis: forecasting and control. (John Wiley & Sons, 2015).