Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior

JMIR Public Health and Surveillance

Volumn 5, Issue 1, 2019, Pages

  Watad, Abdulla ^a,b,c   Watad, Samaa ^b   Mahroum, Naim ^a,b   Sharif, Kassem ^a,b   Amital, Howard ^a,b   Bragazzi, Nicola Luigi ^d   Adawi, Mohammad ^e,f,g  

^a SHEBA MEDICAL CENTER   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

^c UNIVERSITY OF LEEDS   (United Kingdom)

^d UNIVERSITY OF GENOA   (Italy)

^e BARUCH PADEH MEDICAL CENTER   (Israel)

^f BAR ILAN UNIVERSITY   (Israel)

^g Rebecca Sieff Government Hospital   (Israel)

Author keywords

Forecasting model; Google trends; Infodemiology; Infoveillance; Seasonal autoregressive integrated moving average model with explicative variable (SARIMAX); West nile virus

Indexed keywords

EID: 85097133228     PISSN: None     EISSN: 23692960     Source Type: Journal    
DOI: 10.2196/publichealth.9176     Document Type: Article

References (31)

1. Saxena V, Bolling BG, Wang T. West Nile virus. Clin Lab Med 2017 Jun;37(2):243-252. [doi: 10.1016/j.cll.2017.01.001] [Medline: 28457348]
2. Kleinschmidt-DeMasters BK, Beckham JD. West Nile virus encephalitis 16 years later. Brain Pathol 2015 Sep;25(5):625-633. [doi: 10.1111/bpa.12280] [Medline: 26276026]
3. Calisher CH. West Nile virus in the New World: appearance, persistence, and adaptation to a new econiche-an opportunity taken. Viral Immunol 2000;13(4):411-444. [doi: 10.1089/vim.2000.13.411] [Medline: 11192287]
4. Williamson PC, Custer B, Biggerstaff BJ, Lanciotti RS, Sayers MH, Eason SJ, et al. Incidence of West Nile virus infection in the Dallas-Fort Worth metropolitan area during the 2012 epidemic. Epidemiol Infect 2017 Sep;145(12):2536-2544. [Medline: 26829991]
5. Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron AJ. More diseases tracked by using Google Trends. Emerg Infect Dis 2009 Aug;15(8):1327-1328 [FREE Full text] [doi: 10.3201/eid1508.090299] [Medline: 19751610]
6. Alicino C, Bragazzi NL, Faccio V, Amicizia D, Panatto D, Gasparini R, et al. Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes. Infect Dis Poverty 2015 Dec 10;4:54 [FREE Full text] [doi: 10.1186/s40249-015-0090-9] [Medline: 26654247]
7. Bragazzi NL, Barberis I, Rosselli R, Gianfredi V, Nucci D, Moretti M, et al. How often people google for vaccination: qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends. Hum Vaccin Immunother 2017 Dec;13(2):464-469 [FREE Full text] [doi: 10.1080/21645515.2017.1264742] [Medline: 27983896]
8. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009 Mar 27;11(1):e11 [FREE Full text] [doi: 10.2196/jmir.1157] [Medline: 19329408]
9. Eysenbach G. Infodemiology: the epidemiology of (mis)information. Am J Med 2002 Dec 15;113(9):763-765. [Medline: 12517369]
10. Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009 Nov 15;49(10):1557-1564. [doi: 10.1086/630200] [Medline: 19845471]
11. Bragazzi NL, Bacigaluppi S, Robba C, Siri A, Canepa G, Brigo F. Infodemiological data of West-Nile virus disease in Italy in the study period 2004-2015. Data Brief 2016 Dec;9:839-845 [FREE Full text] [doi: 10.1016/j.dib.2016.10.022] [Medline: 27872881]
12. Bragazzi NL, Dini G, Toletone A, Brigo F, Durando P. Leveraging big data for exploring occupational diseases-related interest at the level of scientific community, media coverage and novel data streams: the example of silicosis as a pilot study. PLoS One 2016;11(11):e0166051 [FREE Full text] [doi: 10.1371/journal.pone.0166051] [Medline: 27806115]
13. Gould E, Pettersson J, Higgs S, Charrel R, de Lamballerie X. Emerging arboviruses: why today? One Health 2017 Dec;4:1-13 [FREE Full text] [doi: 10.1016/j.onehlt.2017.06.001] [Medline: 28785601]
14. Kala AK, Tiwari C, Mikler AR, Atkinson SF. A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ 2017 Mar 28;5:e3070 [FREE Full text] [doi: 10.7717/peerj.3070] [Medline: 28367364]
15. Shand L, Brown WM, Chaves LF, Goldberg TL, Hamer GL, Haramis L, et al. Predicting West Nile virus infection risk from the synergistic effects of rainfall and temperature. J Med Entomol 2016 Jul;53(4):935-944. [doi: 10.1093/jme/tjw042] [Medline: 27113111]
16. Rochlin I, Turbow D, Gomez F, Ninivaggi DV, Campbell SR. Predictive mapping of human risk for West Nile virus (WNV) based on environmental and socioeconomic factors. PLoS One 2011;6(8):e23280 [FREE Full text] [doi: 10.1371/journal.pone.0023280] [Medline: 21853103]
17. Trawinski PR, Mackay DS. Meteorologically conditioned time-series predictions of West Nile virus vector mosquitoes. Vector Borne Zoonotic Dis 2008 Aug;8(4):505-521. [doi: 10.1089/vbz.2007.0202] [Medline: 18279008]
18. Day JF, Shaman J. Using hydrologic conditions to forecast the risk of focal and epidemic arboviral transmission in peninsular Florida. J Med Entomol 2008 May;45(3):458-465. [Medline: 18533440]
19. Shaman J, Day JF, Komar N. Hydrologic conditions describe West Nile virus risk in Colorado. Int J Environ Res Public Health 2010 Dec;7(2):494-508 [FREE Full text] [doi: 10.3390/ijerph7020494] [Medline: 20616987]
20. Ghosh D, Guha R. Using a neural network for mining interpretable relationships of West Nile risk factors. Soc Sci Med 2011 Feb;72(3):418-429. [doi: 10.1016/j.socscimed.2010.09.014] [Medline: 20950908]
21. Konrad SK, Zou L, Miller SN. A geographical information system-based web model of arbovirus transmission risk in the continental United States of America. Geospat Health 2012 Nov;7(1):157-159. [doi: 10.4081/gh.2012.114] [Medline: 23242689]
22. Carney R, Ahearn SC, McConchie A, Glasner C, Jean C, Barker C, et al. Early warning system for West Nile virus risk areas, California, USA. Emerg Infect Dis 2011 Aug;17(8):1445-1454 [FREE Full text] [doi: 10.3201/eid1708.100411] [Medline: 21801622]
23. Birnbrauer K, Frohlich DO, Treise D. Inconsistencies in reporting risk information: a pilot analysis of online news coverage of West Nile Virus. Glob Health Promot 2017 Sep;24(3):14-22. [doi: 10.1177/1757975915594603] [Medline: 26265595]
24. Dubey D, Amritphale A, Sawhney A, Dubey D, Srivastav N. Analysis of YouTube as a source of information for West Nile Virus infection. Clin Med Res 2014 Dec;12(3-4):129-132 [FREE Full text] [doi: 10.3121/cmr.2013.1194] [Medline: 24573700]
25. Bragazzi NL, Alicino C, Trucchi C, Paganino C, Barberis I, Martini M, et al. Global reaction to the recent outbreaks of Zika virus: insights from a Big Data analysis. PLoS One 2017 Sep 21;12(9):e0185263 [FREE Full text] [doi: 10.1371/journal.pone.0185263] [Medline: 28934352]
26. Adawi M, Bragazzi NL, Watad A, Sharif K, Amital H, Mahroum N. Discrepancies between classic and digital epidemiology in searching for the Mayaro virus: preliminary qualitative and quantitative analysis of Google Trends. JMIR Public Health Surveill 2017 Dec 01;3(4):e93 [FREE Full text] [doi: 10.2196/publichealth.9136] [Medline: 29196278]
27. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science 2014 Mar 14;343(6176):1203-1205. [doi: 10.1126/science.1248506] [Medline: 24626916]
28. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol 2013;9(10):e1003256 [FREE Full text] [doi: 10.1371/journal.pcbi.1003256] [Medline: 24146603]
29. Santillana M, Zhang DW, Althouse BM, Ayers JW. What can digital disease detection learn from (an external revision to) Google Flu Trends? Am J Prev Med 2014 Sep;47(3):341-347. [doi: 10.1016/j.amepre.2014.05.020] [Medline: 24997572]
30. Ayers JW, Althouse BM, Dredze M. Could behavioral medicine lead the web data revolution? JAMA 2014 Apr 09;311(14):1399-1400 [FREE Full text] [doi: 10.1001/jama.2014.1505] [Medline: 24577162]
31. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of google trends in health care research: a systematic review. PLoS One 2014;9(10):e109583 [FREE Full text] [doi: 10.1371/journal.pone.0109583] [Medline: 25337815]