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Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence

Abstract

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

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References

  • Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatol 33(1):121–131

    Article  Google Scholar 

  • Basile L, de la Fuente MO, Torner N, Martínez A, Jané M (2018) Real-time predictive seasonal influenza model in Catalonia, Spain. PLoS One 13(3):e0193651

    Article  CAS  Google Scholar 

  • Butler D (2013) When Google got flu wrong. Nature 494(7436):155–156

    CAS  Article  Google Scholar 

  • Carlson SJ, Dalton CB, Tuyl FA, Durrheim DN, Fejsa J, Muscatello DJ, Francis J, d’Espaignet ET (2009) Flutracking surveillance: comparing 2007 New South Wales results with laboratory confirmed influenza notifications. Communicable diseases intelligence quarterly report 33(3):323–327

    Google Scholar 

  • Chan EH, Brewer TF, Madoff LC, Pollack MP, Sonricker AL, Keller M, Freifeld CC, Blench M, Mawudeku A, Brownstein JS (2010) Global capacity for emerging infectious disease detection. Proc Natl Acad Sci 107(50):21701–21706

    CAS  Article  Google Scholar 

  • Choi J, Cho Y, Shim E, Woo H (2016) Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health 16(1):1238

    Article  Google Scholar 

  • Choisy M, Rohani P (2012) Changing spatial epidemiology of pertussis in continental USA. Proc R Soc Lond B Biol Sci 279(1747):4574–4581

    Google Scholar 

  • Chunara R, Freifeld CC, Brownstein JS (2012) New technologies for reporting real-time emergent infections. Parasitology 139(14):1843–1851

    Article  Google Scholar 

  • Cordeiro R, Donalisio MR, Andrade VR, Mafra AC, Nucci LB, Brown JC, Stephan C (2011) Spatial distribution of the risk of dengue fever in southeast Brazil, 2006-2007. BMC Public Health 11(1):355

    Article  Google Scholar 

  • Corley CD, Cook DJ, Mikler AR, Singh KP (2010) Using Web and social media for influenza surveillance. Advances in computational biology, Springer, pp 559–564

    Google Scholar 

  • Ebi KL, Nealon J (2016) Dengue in a changing climate. Environ Res 151:115–123

    CAS  Article  Google Scholar 

  • Eisen L, Eisen RJ (2011) Using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. Annu Rev Entomol 56:41–61

    CAS  Article  Google Scholar 

  • Eitan O, Barchana M, Dubnov J, Linn S, Carmel Y, Broday DM (2010) Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel. Sci Total Environ 408(20):4429–4439

    CAS  Article  Google Scholar 

  • Epstein PR (2000) Is global warming harmful to health? Sci Am 283(2):50–57

    CAS  Article  Google Scholar 

  • Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144

    Article  Google Scholar 

  • Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014

    CAS  Article  Google Scholar 

  • Grassly NC, Fraser C (2006) Seasonal infectious disease epidemiology. Proc R Soc Lond B Biol Sci 273(1600):2541–2550

    Google Scholar 

  • Gryn JM, Wildes RP, Tsotsos JK (2009) Detecting motion patterns via direction maps with application to surveillance. Comput Vis Image Underst 113(2):291–307

    Article  Google Scholar 

  • Guy S, Ratzki-Leewing A, Bahati R, Gwadry-Sridhar F (2012) Social media: a systematic review to understand the evidence and application in infodemiology. Lect Notes Inst Comput Sci Soc Inform Telecomm Eng 91:1–8

    Google Scholar 

  • He G, Chen Y, Chen B, Wang H, Shen L, Liu L, Suolang D, Zhang B, Ju G, Zhang L (2018) Using the Baidu Search Index to predict the incidence of HIV/AIDS in China. Sci Rep 8(1):9038

    Article  CAS  Google Scholar 

  • Internet World Stats (2017) Internet users in the world by regions-2017 Q2. http://www.internetworldstats.com/stats.htm.

  • Jiang W, Han SW, Tsui KL, Woodall WH (2011) Spatiotemporal surveillance methods in the presence of spatial correlation. Stat Med 30(5):569–583

    Article  Google Scholar 

  • Knope KE, Muller M, Kurucz N, Doggett SL, Feldman R, Johansen CA, Hobby M (2016) 2013–14: Annual report of the National Arbovirus And Malaria Advisory Committee. Commun Dis Intell 40(3):E401–E436

    Google Scholar 

  • Koelle K, Cobey S, Grenfell B, Pascual M (2006) Epochal evolution shapes the phylodynamics of interpandemic influenza A (H3N2) in humans. Science 314(5807):1898–1903

    CAS  Article  Google Scholar 

  • Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of Google Flu: traps in big data analysis. Science 343(6176):1203–1205

    CAS  Article  Google Scholar 

  • Li Z, Liu T, Zhu G, Lin H, Zhang Y, He J, Deng A, Peng Z, Xiao J, Rutherford S (2017) Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: a case study in Guangzhou, China. PLoS Negl Trop Dis 11(3):e0005354

    Article  Google Scholar 

  • Liu K, Huang S, Miao Z-P, Chen B, Jiang T, Cai G, Jiang Z, Chen Y, Wang Z, Gu H (2017) Identifying potential norovirus epidemics in China via Internet surveillance. J Med Internet Res 19(8):e282

    Article  Google Scholar 

  • McIver DJ, Brownstein JS (2014) Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol 10(4):e1003581

    Article  CAS  Google Scholar 

  • Milinovich GJ, Magalhães RJS, Hu W (2015) Role of big data in the early detection of Ebola and other emerging infectious diseases. Lancet Glob Health 3(1):e20–e21

    Article  Google Scholar 

  • Moher D, Liberati A, Tetzlaff J, Altman DG, Group P (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097

    Article  Google Scholar 

  • Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, Brownstein JS (2014) A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res 16(10):e236

    Article  Google Scholar 

  • O’Shea J (2017) Digital disease detection: a systematic review of event-based internet biosurveillance systems. Int J Med Inform 101:15–22

    Article  Google Scholar 

  • Özkan Ş, Vitali A, Lacetera N, Amon B, Bannink A, Bartley DJ, Blanco-Penedo I, De Haas Y, Dufrasne I, Elliott J (2016) Challenges and priorities for modelling livestock health and pathogens in the context of climate change. Environ Res 151:130–144

    Article  CAS  Google Scholar 

  • Paz S, Bisharat N, Paz E, Kidar O, Cohen D (2007) Climate change and the emergence of Vibrio vulnificus disease in Israel. Environ Res 103(3):390–396

    CAS  Article  Google Scholar 

  • Perrotta D, Bella A, Rizzo C, Paolotti D (2017) Participatory online surveillance as a supplementary tool to sentinel doctors for influenza-like illness surveillance in Italy. PLoS One 12(1):e0169801

    Article  CAS  Google Scholar 

  • Pervaiz F, Pervaiz M, Rehman NA, Saif U (2012) FluBreaks: early epidemic detection from Google flu trends. J Med Internet Res 14(5):e125

    Article  Google Scholar 

  • Project TS (2011) Assessment of syndromic surveillance in Europe. Lancet 378(9806):1833–1834

    Article  Google Scholar 

  • Racloz V, Griot C, Stärk K (2006) Sentinel surveillance systems with special focus on vector-borne diseases. Anim Health Res Rev 7(1-2):71–79

    CAS  Article  Google Scholar 

  • Rushton G, Elmes G, McMaster R (2000) Considerations for improving geographic information system research in public health. URISA-WASHINGTON DC 12(2):31–50

    Google Scholar 

  • Shaman J, Kandula S, Yang W, Karspeck A (2017) The use of ambient humidity conditions to improve influenza forecast. PLoS Comput Biol 13(11):e1005844

    Article  CAS  Google Scholar 

  • Varga C, Pearl DL, McEwen SA, Sargeant JM, Pollari F, Guerin MT (2013) Evaluating area-level spatial clustering of Salmonella Enteritidis infections and their socioeconomic determinants in the greater Toronto area, Ontario, Canada (2007–2009): a retrospective population-based ecological study. BMC Public Health 13(1):1078

    Article  Google Scholar 

  • Vayena E, Salathé M, Madoff LC, Brownstein JS (2015) Ethical challenges of big data in public health. PLoS Comput Biol 11(2):e1003904

    Article  CAS  Google Scholar 

  • Watson RT, Zinyowera MC, Moss RH, Dokken DJ (2001) IPCC Special Report on the regional impacts of climate change: an assessment of vulnerability. IPCC Secretariat,

  • Weng TC, Chan TC, Lin HT, Chang CKJ, Wang WW, Li ZRT, Cheng H-Y, Chu Y-R, Chiu AW-H, Yen M-Y (2015) Early Detection for cases of enterovirus-and influenza-like illness through a newly established school-based syndromic surveillance system in Taipei, January 2010~ August 2011. PLoS One 10(4):e0122865

    Article  CAS  Google Scholar 

  • Wu X, Tian H, Zhou S, Chen L, Xu B (2014) Impact of global change on transmission of human infectious diseases. Sci China Earth Sci 57(2):189–203

    Article  Google Scholar 

  • Yang W, Li Z, Lan Y, Wang J, Ma J, Jin L, Sun Q, Lv W, Lai S, Liao Y (2011) A nationwide web-based automated system for early outbreak detection and rapid response in China. Western Pacific Surveillance and Response 2(1):10–15

    Article  Google Scholar 

  • Zhang H, Li Z, Lai S, Clements AC, Wang L, Yin W, Zhou H, Yu H, Hu W, Yang W (2014) Evaluation of the performance of a dengue outbreak detection tool for China. PLoS One 9(8):e106144

    Article  Google Scholar 

  • Zhang Y, Milinovich G, Xu Z, Bambrick H, Mengersen K, Tong S, Hu W (2017) Monitoring pertussis infections using internet search queries. Sci Rep 7(1):10437

    Article  CAS  Google Scholar 

  • Zhang Y, Bambrick H, Mengersen K, Tong S, Hu W (2018) Using Google Trends and ambient temperature to predict seasonal influenza outbreaks. Environ Int 117:284–291

    Article  Google Scholar 

Download references

Funding

Y.Z. was supported by the China Scholarship Council Postgraduate Scholarship and the Queensland University of Technology Higher Degree Research Tuition Fee Sponsorship. W. H. was supported by an Australian Research Council (ARC) Future Fellowship (award number FT140101216). K. M. was supported by an ARC Laureate Fellowship (award number FL150100150) and an ARC Centre of Excellence in Mathematical and Statistical Frontiers (award number CE140100049).

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Contributions

W. H. designed this study. Y. Z. collected and analysed the data and drafted this manuscript with W.H. assistance. W. H., H. B., K. M., and S. T. interpreted the results and revised the manuscript.

Corresponding author

Correspondence to Wenbiao Hu.

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The authors declare no competing interests.

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Zhang, Y., Bambrick, H., Mengersen, K. et al. Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence. Int J Biometeorol 65, 2203–2214 (2021). https://doi.org/10.1007/s00484-021-02155-4

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  • DOI: https://doi.org/10.1007/s00484-021-02155-4

Keywords

  • Internet-based data
  • Climatic factors
  • Infectious diseases
  • Prediction
  • Spatiotemporal epidemiology