Correction: Public reaction to Chikungunya outbreaks in Italy—Insights from an extensive novel data streams-based structural equation modeling analysis(PLoS ONE (2018)13:5 (e0197337) DOI: 10.1371/journal.pone.0197337);Public reaction to Chikungunya outbreaks in Italy—Insights from an extensive novel data streams-based structural equation modeling analysis

PLoS ONE

Volumn 13, Issue 5, 2018, Pages

  Mahroum, Naim ^a,b   Adawi, Mohammad ^c,d   Sharif, Kassem ^a,b   Waknin, Roy ^b   Mahagna, Hussein ^a,b   Bisharat, Bishara ^e   Mahamid, Mahmud ^c,f   Abu Much, Arsalan ^a,b   Amital, Howard ^a,b   Bragazzi, Nicola Luigi ^e,g   Watad, Abdulla ^a,b  

^a SHEBA MEDICAL CENTER   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

^c BAR ILAN UNIVERSITY   (Israel)

^d Rebecca Sieff Government Hospital   (Israel)

^e Society for Health Promotion of the Arab Community   (Israel)

^f Nazareth Hospital   (Israel)

^g UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AWARENESS; CHIKUNGUNYA; CONTROLLED STUDY; EPIDEMIC; HUMAN; INTERNET; ITALY; PUBLIC HEALTH; SOCIAL MEDIA; STRUCTURAL EQUATION MODELING; ATTITUDE TO HEALTH; INFORMATION SEEKING; MASS MEDIUM; MEDICAL INFORMATION; PSYCHOLOGY; SCHOLARLY COMMUNICATION; STATISTICAL MODEL;

CHIKUNGUNYA FEVER; DISEASE OUTBREAKS; HEALTH COMMUNICATION; HEALTH KNOWLEDGE, ATTITUDES, PRACTICE; HUMANS; INFORMATION SEEKING BEHAVIOR; INTERNET; ITALY; MASS MEDIA; MODELS, STATISTICAL; SCHOLARLY COMMUNICATION;

EID: 85047447096     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0222865     Document Type: Erratum

References (40)

1. Lo Presti A, Cella E, Angeletti S, Ciccozzi M. Molecular epidemiology, evolution and phylogeny of Chikungunya virus: An updating review. Infect Genet Evol. 2016; 41:270–8. Epub 2016/04/18. https://doi.org/10.1016/j.meegid.2016.04.006 PMID: 27085290.
2. Cunha RVD, Trinta KS. Chikungunya virus: clinical aspects and treatment—A Review. Mem Inst Oswaldo Cruz. 2017; 112(8):523–31. Epub 2017/08/03. https://doi.org/10.1590/0074-02760170044PMID: 28767976.
3. Amraoui F, Failloux AB. Chikungunya: an unexpected emergence in Europe. Curr Opin Virol. 2016; 21:146–50. https://doi.org/10.1016/j.coviro.2016.09.014 PMID: 27771517.
4. Angelini R, Finarelli AC, Angelini P, Po C, Petropulacos K, Macini P, et al. An outbreak of chikungunya fever in the province of Ravenna, Italy. Euro Surveill. 2007; 12(9):E070906.1. PMID: 17900424.
5. Grandadam M, Caro V, Plumet S, Thiberge JM, Souares Y, Failloux AB, et al. Chikungunya virus, southeastern France. Emerg Infect Dis. 2011; 17(5):910–3. https://doi.org/10.3201/eid1705.101873PMID: 21529410.
6. Rezza G, Nicoletti L, Angelini R, Romi R, Finarelli AC, Panning M, et al. Infection with chikungunya virus in Italy: an outbreak in a temperate region. Lancet. 2007; 370(9602):1840–6. https://doi.org/10.1016/ S0140-6736(07)61779-6 PMID: 18061059.
7. Medlock JM, Hansford KM, Schaffner F, Versteirt V, Hendrickx G, Zeller H, et al. A review of the invasive mosquitoes in Europe: ecology, public health risks, and control options. Vector Borne Zoonotic Dis. 2012; 12(6):435–47. https://doi.org/10.1089/vbz.2011.0814 PMID: 22448724.
8. Venturi G, Di Luca M, Fortuna C, Remoli ME, Riccardo F, Severini F, et al. Detection of a chikungunya outbreak in Central Italy, August to September 2017. Euro Surveill. 2017; 22(39). https://doi.org/10.2807/1560-7917.ES.2017.22.39.17-00646 PMID: 29019306.
9. Castillo-Ortiz JD, de Jesus Valdivia-Nuno J, Ramirez-Gomez A, Garagarza-Mariscal H, Gallegos-Rios C, Flores-Hernandez G, et al. Fifteen-year trend in information on the World Wide Web for patients with rheumatoid arthritis: evolving, but opportunities for improvement remain. Rheumatology International. 2016; 36(9):1281–9. https://doi.org/10.1007/s00296-016-3507-2 PMID: 27295190
10. Castillo-Ortiz JD, Valdivia-Nuno JdJ, Ramirez-Gomez A, Garagarza-Mariscal H, Gallegos-Rios C, Flores-Hernandez G, et al. Readability, Relevance and Quality of the Information in Spanish on the Web for Patients With Rheumatoid Arthritis. Reumatología Clínica (English Edition). 2017; 13(3):139–44. https://doi.org/10.1016/j.reumae.2016.04.010
11. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection—harnessing the Web for public health surveillance. N Engl J Med. 2009; 360(21):2153–5, 7. Epub 2009/05/09. https://doi.org/10.1056/ NEJMp0900702 PMID: 19423867.
12. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244–8. Epub 2007/01/24. PMID: 17238340.
13. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009; 457(7232):1012–4. Epub 2008/11/21. https://doi.org/10.1038/nature07634 PMID: 19020500.
14. Polgreen PM, Chen Y, Pennock DM, Nelson FD. Using internet searches for influenza surveillance. Clin Infect Dis. 2008; 47(11):1443–8. Epub 2008/10/29. https://doi.org/10.1086/593098 PMID: 18954267.
15. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One. 2010; 5(11):e14118. Epub 2010/12/03. https://doi.org/10.1371/journal.pone.0014118 PMID: 21124761.
16. Odlum M, Yoon S. What can we learn about the Ebola outbreak from tweets? Am J Infect Control. 2015; 43(6):563–71. Epub 2015/06/05. https://doi.org/10.1016/j.ajic.2015.02.023 PMID: 26042846.
17. Kagashe I, Yan Z, Suheryani I. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data. J Med Internet Res. 2017; 19(9):e315. Epub 2017/09/14. https://doi.org/10.2196/jmir.7393 PMID: 28899847.
18. Manogaran G, Vijayakumar V, Varatharajan R, Malarvizhi Kumar P, Sundarasekar R, Hsu C-H. Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering. Wireless Personal Communications. 2017. https://doi.org/10.1007/s11277-017-5044-z
19. Varatharajan R, Manogaran G, Priyan MK. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications. 2017. https://doi.org/10.1007/s11042-017-5318-1
20. Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C. A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems. 2018; 82:375–87. https://doi.org/10.1016/j.future.2017.10.045.
21. Manogaran G, Lopez D, Thota C, Abbas KM, Pyne S, Sundarasekar R. Big Data Analytics in Healthcare Internet of Things. In: Qudrat-Ullah H, Tsasis P, editors. Innovative Healthcare Systems for the 21st Century. Cham: Springer International Publishing; 2017. p. 263–84.
22. 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. Epub 2014/10/23. https://doi.org/10.1371/journal.pone.0109583 PMID: 25337815.
23. Pageviews Analysis: Wikimedia Foundation; 2017 [cited 2017]. https://tools.wmflabs.org/.
24. MacKinnon DP, Krull JL, Lockwood CM. Equivalence of the mediation, confounding and suppression effect. Prev Sci. 2000; 1(4):173–81. PMID: 11523746.
25. Rucker DD, Preacher KJ, Tormala ZL, Petty RE. Mediation Analysis in Social Psychology: Current Practices and New Recommendations. Social and Personality Psychology Compass. 2011; 5/6:359–71.
26. Wong R, Harris JK. Geospatial Distribution of Local Health Department Tweets and Online Searches About Ebola During the 2014 Ebola Outbreak. Disaster Med Public Health Prep. 2017:1–4. Epub 2017/ 08/25. https://doi.org/10.1017/dmp.2017.69 PMID: 28835312.
27. Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic Forecasting of Zika Epidemics Using Google Trends. PLoS One. 2017; 12(1):e0165085. Epub 2017/01/07. https://doi.org/10.1371/journal.pone. 0165085 PMID: 28060809.
28. Rossini G, Gaibani P, Vocale C, Finarelli AC, Landini MP. Increased number of cases of Chikungunya virus (CHIKV) infection imported from the Caribbean and Central America to northern Italy, 2014. Epide-miol Infect. 2016; 144(9):1912–6. Epub 2016/01/12. https://doi.org/10.1017/S0950268815002940PMID: 26751121.
29. 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; 12(9):e0185263. Epub 2017/09/22. https://doi.org/10.1371/journal.pone.0185263 PMID: 28934352.
30. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013–2014 influenza season using Wikipedia. PLoS Comput Biol. 2015; 11(5):e1004239. Epub 2015/05/15. https://doi.org/10.1371/journal.pcbi.1004239 PMID: 25974758.
31. Fairchild G, Del Valle SY, De Silva L, Segre AM. Eliciting Disease Data from Wikipedia Articles. Proc Int AAAI Conf Weblogs Soc Media. 2015; 2015:26–33. Epub 2015/05/01. PMID: 28721308.
32. Tausczik Y, Faasse K, Pennebaker JW, Petrie KJ. Public anxiety and information seeking following the H1N1 outbreak: blogs, newspaper articles, and Wikipedia visits. Health Commun. 2012; 27(2):179–85. Epub 2011/08/11. https://doi.org/10.1080/10410236.2011.571759 PMID: 21827326.
33. Moore RJ. Twitter Data Analysis: An Investor’s Perspective: TechCrunch; 2009. https://techcrunch.com/2009/10/05/twitter-data-analysis-an-investors-perspective-2/.
34. Russell J. Twitter admits it overestimated its user numbers: TechCrunch; 2017. https://techcrunch.com/2017/10/26/twitter-admits-it-overestimated-its-user-numbers/.
35. Aylward B, Barboza P, Bawo L, Bertherat E, Bilivogui P, Blake I, et al. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Engl J Med. 2014; 371(16):1481–95. Epub 2014/09/23. https://doi.org/10.1056/NEJMoa1411100 PMID: 25244186.
36. Fasina FO, Shittu A, Lazarus D, Tomori O, Simonsen L, Viboud C, et al. Transmission dynamics and control of Ebola virus disease outbreak in Nigeria, July to September 2014. Euro Surveill. 2014; 19 (40):20920. Epub 2014/10/18. PMID: 25323076.
37. Guzzetta G, Montarsi F, Baldacchino FA, Metz M, Capelli G, Rizzoli A, et al. Potential Risk of Dengue and Chikungunya Outbreaks in Northern Italy Based on a Population Model of Aedes albopictus (Dip-tera: Culicidae). PLoS Negl Trop Dis. 2016; 10(6):e0004762. Epub 2016/06/16. https://doi.org/10.1371/journal.pntd.0004762 PMID: 27304211.
38. Rodgers S, Chen Q, Duffy M, Fleming K. Media usage as health segmentation variables. J Health Commun. 2007; 12(2):105–19. Epub 2007/03/17. https://doi.org/10.1080/10810730601150064 PMID: 17365354.
39. Geiß S, Leidecker M, Roessing T. The interplay between media-for-monitoring and media-for-searching: How news media trigger searches and edits in Wikipedia. New Media & Society. 2016; 18 (11):2740–59. https://doi.org/10.1177/1461444815600281
40. 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. Journal of Medical Internet Research. 2009; 11(1):e11. https://doi.org/10.2196/jmir.1157 PMID: 19329408