Published on 01.12.17 in Vol 3, No 4 (2017): Oct-Dec
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/9136, first published Oct 07, 2017.
Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends
Background: Mayaro virus (MAYV), first discovered in Trinidad in 1954, is spread by the Haemagogus mosquito. Small outbreaks have been described in the past in the Amazon jungles of Brazil and other parts of South America. Recently, a case was reported in rural Haiti.
Objective: Given the emerging importance of MAYV, we aimed to explore the feasibility of exploiting a Web-based tool for monitoring and tracking MAYV cases.
Methods: Google Trends is an online tracking system. A Google-based approach is particularly useful to monitor especially infectious diseases epidemics. We searched Google Trends from its inception (from January 2004 through to May 2017) for MAYV-related Web searches worldwide.
Results: We noted a burst in search volumes in the period from July 2016 (relative search volume [RSV]=13%) to December 2016 (RSV=18%), with a peak in September 2016 (RSV=100%). Before this burst, the average search activity related to MAYV was very low (median 1%). MAYV-related queries were concentrated in the Caribbean. Scientific interest from the research community and media coverage affected digital seeking behavior.
Conclusions: MAYV has always circulated in South America. Its recent appearance in the Caribbean has been a source of concern, which resulted in a burst of Internet queries. While Google Trends cannot be used to perform real-time epidemiological surveillance of MAYV, it can be exploited to capture the public’s reaction to outbreaks. Public health workers should be aware of this, in that information and communication technologies could be used to communicate with users, reassure them about their concerns, and to empower them in making decisions affecting their health.
JMIR Public Health Surveill 2017;3(4):e93
- digital health;
- digital epidemiology;
- emerging viruses;
- Mayaro virus;
- epidemiological monitoring
Mayaro virus (MAYV) was first discovered in Trinidad in 1954 and isolated by Charles Anderson and collaborators from the blood of 5 febrile forest workers [- ]. MAYV is similar to the chikungunya and Semliki Forest viruses, being a linear, positive-sense, single-stranded, enveloped RNA virus, more specifically an arbovirus of the family Togaviridae and of the genus Alphavirus) [ ]. MAYV is generally spread by the Haemagogus mosquito [ ], but it can also be spread by Aedes aegypti and Aedes albopticus mosquitoes, which appear to be competent vectors as well [ , ]. Rubber workers are particularly at risk of developing MAYV infection.
In the past, small and occasional outbreaks have been described mainly in the Amazon basin of Brazil and a few other parts of South America [- ]. The first epidemics were reported in 1955 in Brazil and Bolivia [ ]. The reemergence of MAYV is cause for great concern for both public health organizations and communities. Anthropogenic changes in ecosystems and environments, due to a variety of phenomena, including urbanization, globalization and migration, agricultural intensification, and deforestation, together with displacement of populations and invasion of wildlife habitats by humans and domestic animals, are playing a major role in MAYV reemergence. Climate changes, economic downturns, and poverty are further drivers of the reemergence of neglected tropical diseases [ ]. Therefore, eliminating mosquito breeding sites constitutes an important preventive measure. Efforts to strengthen and improve pathogen surveillance technologies are also fundamental in programs to control disease.
MAYV infection is a nonfatal and generally self-limiting disease causing arthritis in the knee, ankle, and small joints of the extremities, generalized myalgia, frontal headaches and photophobia, vertigo, nausea and epigastric pain, and chills, followed in two-thirds of patients by a fine maculopapular rash affecting the trunk and the extremities. In some cases, MAYV infection can persist up to 2 months.
In May 2007, an outbreak occurred in Chuquisaca Department, Bolivia, and involved 12 persons . In January 2010, a French tourist, after a 15-day trip in the Amazon forest, Brazil, reported MAYV infection [ ]. In 2011, MAYV was diagnosed in a 27-year-old male Swiss tourist returning from Peru [ ]. MAYV disease was also imported into the United States by 2 infected people who had visited eastern Peru [ ] and, more recently, into the Netherlands by a couple infected during their holidays in Suriname [ ].
In June 2010, an outbreak occurred in Venezuela, with 69 cases in Ospino, Portuguesa state, and 2 additional cases in San Fernando de Apure, Apure state, on June 7, for a total of 71 confirmed cases as of June 8 (out of the initially reported 77 cumulative cases) . A single case of MAYV infection in an 8-year-old child with fever and abdominal pain was described in rural Haiti (in the Gressier-Léogâne area, 20 miles west of Port-au-Prince) in 2015 [ ].
In conclusion, MAYV has been so far isolated in humans, wild animals, and mosquitoes in Bolivia, Brazil [, ], Colombia, Costa Rica, French Guiana [ ], Guatemala, Guyana, Panama, Peru [ ], Suriname, Trinidad, and Venezuela [ ].
Given the emerging importance of MAYV [- ], we here aimed to explore the feasibility of exploiting a Web-based tool for monitoring and tracking cases of MAYV infection.
Google Trends (Google Inc) is a freely available, online tracking system that, properly using keyword(s), enables a visualization of hit-search volumes in terms of relative search volumes (RSVs). In more detail, for each keyword or string of keywords, searches can be performed using search term or search topic strategies. With the first option, Google Trends tracks and monitors the exact text typed by the user. The second strategy, instead, consists of an exhaustive and systematic collection of all searches semantically related to the given query. Generally, the second search option results in broader findings.
A Google-based approach seems to be particularly useful to monitor infectious diseases epidemics . Pelat and collaborators [ ], as well as Valdivia and Monge-Corella [ ], documented the usefulness of using Google Trends in capturing influenza and chickenpox outbreaks. The nowcasting or forecasting approach has been used also for other tropical diseases, such as malaria [ ], Ebola [ ], West Nile virus [ ], and dengue [ ].
In this study, we systematically searched Google Trends from its inception (January 2004 through to May 2017), using as keywords “virus Mayaro,” “Mayaro virus,” “virus de Mayaro,” and “virus del Mayaro.” We carried out this investigation according to the guidelines and recommendations put forth by Nuti and coworkers .
We built an ad hoc database of cases of MAYV by extensively mining Google, Google Scholar, the scholarly literature (PubMed or MEDLINE, Scopus, Scientific Electronic Library Online, and Latin American and Caribbean Health Sciences Literature), epidemiological alerts (from the US Centers for Disease Control and Prevention, European Centre for Disease Prevention and Control, World Health Organization, and Pan American Health Organization), HealthMap, and ProMED-mail reports.
Since Web searches can be prompted by different external or environmental cues (media coverage, education system, etc), we carried out a multivariate regression analysis according to the following predictive model:
RSV(%) = α × scientific interest + β × epidemiology + γ × media impact + ε.
We measured “scientific interest” by counting the number of MAYV-related articles indexed in PubMed or MEDLINE in the study period, using scientific production as a proxy of the interest of the scientific community toward MAYV; “epidemiology” was the number of confirmed MAYV cases; we assessed “media impact” as the number of MAYV-related news items released in the openly available news aggregator Google News as a proxy of the media coverage and influence over public opinion; and ε is the intercept of the model.
This model was theoretically inspired by the extant scholarly literature on infodemiology and infoveillance [- ] and, in particular, on Google Trends, as well as by a study by Segev and Baram-Tsabari [ ], which systematically investigated different search patterns in terms of the roles of the media and the education system.
We chose the best model according to the goodness-of-fit statistical model.
All statistical analyses were performed with IBM SPSS (version 24.0; IBM Corporation). Figures with a P value less than .05 were considered statistically significant.
shows the MAYV-related RSV trend. A burst in search volumes can be noticed in the period from July 2016 (RSV=13%) to December 2016 (RSV=18%), with a peak in September 2016 (RSV=100%). Before this burst, the average search activity related to MAYV was very low (median 1%).
and show the countries with major search volumes. MAYV-related queries were concentrated in the Caribbean.
According to the best multivariate regression model, both scientific interest and media coverage had an impact on seeking behavior, with negative and positive effects, respectively (, , ).
lists the top MAYV-related and rising queries. These mainly related to MAYV infection symptoms (eg, fever, influenza-like symptoms), its carriers and vectors of transmission (eg, mosquitoes of the Aedes genus), and other similar tropical diseases (eg, yellow fever, malaria, dengue, chikungunya).
reports the different models and their goodness-of-fit statistics.
|Trinidad and Tobago||24|
aRSV: relative search volume.
|Source||Value||SE||T||P value||95% CI|
|Intercept||27.313||22.046||1.239||.24||–20.720 to 75.347|
|Cases||0.352||0.434||0.811||.43||–0.593 to 1.297|
|Epidemiology + media impact|
|Intercept||14.684||4.313||3.405||.006||5.192 to 24.176|
|Cases||0.028||0.086||0.328||.75||–0.160 to 0.217|
|Google News||0.718||0.041||17.648||<.001||0.628 to 0.807|
|Epidemiology + scientific interest|
|Intercept||–12.942||31.445||–0.412||.69||–82.152 to 56.267|
|PubMed||5.628||3.331||1.689||.12||–1.704 to 12.960|
|Cases||0.341||0.404||0.846||.42||–0.547 to 1.230|
|Epidemiology + media impact + scientific interest|
|Intercept||27.358||5.288||5.173||<.001||15.575 to 39.141|
|Confirmed cases||0.002||0.065||0.038||.970||–0.143 to 0.148|
|Google News||0.783||0.037||20.936||<.001||0.699 to 0.866|
|PubMed||–1.931||0.635||–3.043||.01||–3.345 to –0.517|
aThe epidemiological predictor is given by the number of confirmed Mayaro virus cases; the bibliometric predictor is given by the number of articles published in PubMed or MEDLINE; the media predictor is given by the number of news items concerning the Mayaro virus.
|Fitting parameter||Epidemiology||Epidemiology + media impact||Epidemiology + scientific interest||Epidemiology + scientific interest + media impact|
|Mean square of errors||3898.689||145.088||3376.945||82.855|
|Root mean square of errors||62.439||12.045||58.111||9.102|
|Mean absolute percentage error||119.495||36.418||122.541||29.780|
|Mallows Cp coefficient||2.000||3.000||3.000||4.000|
|Akaike information criterion||117.599||72.306||116.370||65.129|
|Schwarz Bayesian criterion||118.878||74.224||118.287||67.685|
|Amemiya prediction criterion||1.264||0.050||1.163||0.030|
The geospatial and temporal epidemiology of MAYV as captured by Google Trends did not reflect the real-world epidemiology of MAYV. For example, Google Trends did not capture several epidemic outbreaks (briefly overviewed in the introduction), including one of the largest MAYV outbreaks, which occurred in northwestern Venezuela in 2010, in a rural village, with 77 cases and 19 individuals confirmed seropositive .
Moreover, areas in which MAYV is known to circulate and spread were scarcely represented in terms of search volumes, while areas in which MAYV has been isolated only recently and has never been seen before, such as in the Caribbean, were overrepresented. In the case of MAYV, Google Trends seemed to capture more of the public reaction to MAYV reemergence in terms of worries and concerns, rather than the real-world epidemiological figures. In the last years, there have been concerns about the ability of MAYV to mutate and adapt to new environments, spreading from South America to North America and other countries, and emerging as a “new Ebola,” a “new Zika,” or the “next chikungunya,” thus giving rise to a new public health emergency . The last decades have been characterized by the reemergence of several arboviruses and, above all, by unexpected changes in their clinical history, such as that Zika virus infection can result in neurological disorders and fetal microcephaly [ ]. This has led to public concerns and worries, amplified, in their turn, by imbalanced and distorted media coverage. In the case of MAYV, as can be seen using Google News, a freely available aggregator of media news, the report of a single case of an infected child attracted more media attention than all other MAYV cases in South America, as well as receiving more tweets and videos. On the other hand, this single case report suggested that MAYV-related scenarios are changing or could be further changing in the near future, making the possibility of finding MAYV in urban locations carried by anthropophilic insect vectors more concrete [ ].
MAYV-related queries concerned above all the symptoms of the infectious disease. No query was related to preventive measures (either environmental or personal hygiene) that could be taken to reduce and mitigate its spreading. This undoubtedly constitutes a major gap in knowledge that public health workers and officials should fill, by providing and disseminating adequate information.
Specifically concerning Google Trends, its validity in complementing classic epidemiological and surveillance techniques and approaches has recently been questioned by some scholars. While there is a relatively huge body of literature reporting the feasibility of exploiting Google Trends in the field of digital epidemiology [, ], some scholars have criticized Google Trends, showing that it may be inaccurate in some cases, such as that of influenza surveillance [ , ]. Google Flu Trends, based on Google Trends for the epidemiological monitoring of influenza, has been publicly withdrawn, following different criticisms (Google Dengue Trends met a similar fate). On the other hand, Santillana and collaborators [ ] demonstrated that some techniques, inspired by data assimilation techniques, supervised machine learning, and artificial intelligence, could be applied to improve the reliability of Google Flu Trends.
Cervellin and colleagues , on the contrary, found that Google Trends was a scarcely reliable epidemiological tool in a variety of clinical settings, ranging from renal colic or epistaxis to mushroom poisoning, meningitis, Legionella pneumophila pneumonia, and Ebola fever.
Similarly, Tran and coworkers , searching Google Trends for “suicide” from 2004 to 2010 in the United States and Switzerland, and from 2004 to 2012 in Germany and Austria, found that Google Trends was not able to forecast national suicide rates.
Our study showed that media coverage resulted in seeking behavior and that this impact can be quantified using a multivariate regression model. This is in line with the findings of Segev and Baram-Tsabari , who found that ad hoc events or current concerns correlated better with media coverage than did general or well-established scientific terms. Indeed, MAYV, being an emerging virus, represents a relatively overlooked research field and only recently has caused severe public health problems and concerns, which have resulted both in increased media attention and coverage (as shown by Google News) and increased interest from the scientific community (as shown by the bibliometric data), as well as in higher search volumes from the public (as shown by Google Trends).
Strengths and Limitations
This study has some strengths, including the systematic search of MAYV-related queries and the novelty of the investigation, being the first, to the best of our knowledge, to address the topic of the relationship between MAYV and information and communication technologies (ICTs).
However, our investigation is not without limitations. The major drawback is that Google Trends returns relative, normalized values instead of providing scholars with absolute, raw figures that can be further handled, refined, and statistically processed. A second shortcoming is the digital divide, in that Google Trends captures only that segment of the population that actively uses the new ICTs and devices. However, this segment is constantly growing and increasing. A third drawback is that, when using Google Trends, only Google-based searches and queries can be tracked and monitored. On the other hand, Google is the most commonly used search engine worldwide.
MAYV, an arthropod-borne virus, has always circulated in South America. Its recent appearance in the Caribbean has been a source of concern, which has resulted in a burst of Internet queries. While Google Trends cannot be used to perform real-time epidemiological surveillance of MAYV, it can be exploited to capture the public reaction to outbreaks, in terms of worries, and knowledge needs and gaps . Public health workers and officials should be aware that they can use Google Trends to easily track and monitor public reaction and popular perceptions, and use ICTs to communicate with users, reassure them about their concerns, and empower them in making decisions affecting their health [ - ].
Further studies in the field are needed, especially using other ICTs and social media or networks, such as Twitter, Facebook, or Instagram, as well as carrying out a content analysis of MAYV-related digital material. Moreover, techniques for correcting and revising Google Trends should be systematically explored, for example, correlating Web searches with environmental parameters (such as rainfall, temperature, or weather), which are well known to have an impact on the epidemiology of neglected tropical infectious diseases.
The authors wish to thank Mr Abed El Rahman Wattad for help in preparing the Table of Contents image.
Conflicts of Interest
- Casals J, Whitman L. Mayaro virus: a new human disease agent. I. Relationship to other arbor viruses. Am J Trop Med Hyg 1957 Nov;6(6):1004-1011. [Medline]
- Anderson CR, Downs WG, Wattley GH, Ahin NW, Reese AA. Mayaro virus: a new human disease agent. II. Isolation from blood of patients in Trinidad, B.W.I. Am J Trop Med Hyg 1957 Nov;6(6):1012-1016. [Medline]
- Causey OR, Maroja OM. Mayaro virus: a new human disease agent. III. Investigation of an epidemic of acute febrile illness on the river Guama in Pará, Brazil, and isolation of Mayaro virus as causative agent. Am J Trop Med Hyg 1957 Nov;6(6):1017-1023. [Medline]
- Buckley SM, Clarke DH. Differentiation of group A arboviruses chikungunya, Mayaro, and Semliki Forest by the fluorescent antibody technique. Proc Soc Exp Biol Med 1970 Nov;135(2):533-539. [Medline]
- Lemos PD, Monteiro HA, Castro FC, Lima CP, Silva DE, Vasconcelos JM, et al. Characterization of mitochondrial genome of Haemagogus janthinomys (Diptera: Culicidae). Mitochondrial DNA A DNA Mapp Seq Anal 2017 Jan;28(1):50-51. [CrossRef] [Medline]
- Long KC, Ziegler SA, Thangamani S, Hausser NL, Kochel TJ, Higgs S, et al. Experimental transmission of Mayaro virus by Aedes aegypti. Am J Trop Med Hyg 2011 Oct;85(4):750-757 [FREE Full text] [CrossRef] [Medline]
- Smith GC, Francy DB. Laboratory studies of a Brazilian strain of Aedes albopictus as a potential vector of Mayaro and Oropouche viruses. J Am Mosq Control Assoc 1991 Mar;7(1):89-93. [Medline]
- Pinheiro FP, Freitas RB, Travassos da Rosa JF, Gabbay YB, Mello WA, LeDuc JW. An outbreak of Mayaro virus disease in Belterra, Brazil. I. Clinical and virological findings. Am J Trop Med Hyg 1981 May;30(3):674-681. [Medline]
- LeDuc JW, Pinheiro FP, Travassos da Rosa AP. An outbreak of Mayaro virus disease in Belterra, Brazil. II. Epidemiology. Am J Trop Med Hyg 1981 May;30(3):682-688. [Medline]
- Hoch AL, Peterson NE, LeDuc JW, Pinheiro FP. An outbreak of Mayaro virus disease in Belterra, Brazil. III. Entomological and ecological studies. Am J Trop Med Hyg 1981 May;30(3):689-698. [Medline]
- Vieira CJ, Silva DJ, Barreto ES, Siqueira CE, Colombo TE, Ozanic K, et al. Detection of Mayaro virus infections during a dengue outbreak in Mato Grosso, Brazil. Acta Trop 2015 Jul;147:12-16. [CrossRef] [Medline]
- Taylor SF, Patel PR, Herold TJ. Recurrent arthralgias in a patient with previous Mayaro fever infection. South Med J 2005 Apr;98(4):484-485. [CrossRef] [Medline]
- Hotez PJ. Neglected tropical diseases in the anthropocene: the cases of Zika, Ebola, and other infections. PLoS Negl Trop Dis 2016 Apr;10(4):e0004648 [FREE Full text] [CrossRef] [Medline]
- Terra. Seis regiones de Bolivia afectadas por brote de epidemias tras las lluvias [Six regions of Bolivia affected by outbreak of epidemics after rains]. Sao Paulo, Brazil: Terra Networks Brasil SA; 2007 May 13. URL: http://noticias.terra.com/noticias/articulo/html/act834981.htm [accessed 2017-11-21] [WebCite Cache]
- Receveur MC, Grandadam M, Pistone T, Malvy D. Infection with Mayaro virus in a French traveller returning from the Amazon region, Brazil, January, 2010. Euro Surveill 2010 May 06;15(18) [FREE Full text] [Medline]
- Tesh RB, Watts DM, Russell KL, Damodaran C, Calampa C, Cabezas C, et al. Mayaro virus disease: an emerging mosquito-borne zoonosis in tropical South America. Clin Infect Dis 1999 Jan;28(1):67-73. [CrossRef] [Medline]
- Neumayr A, Gabriel M, Fritz J, Günther S, Hatz C, Schmidt-Chanasit J, et al. Mayaro virus infection in traveler returning from Amazon Basin, northern Peru. Emerg Infect Dis 2012 Apr;18(4):695-696 [FREE Full text] [CrossRef] [Medline]
- Hassing RJ, Leparc-Goffart I, Blank SN, Thevarayan S, Tolou H, van Doornum G, et al. Imported Mayaro virus infection in the Netherlands. J Infect 2010 Oct;61(4):343-345. [CrossRef] [Medline]
- Auguste AJ, Liria J, Forrester NL, Giambalvo D, Moncada M, Long KC, et al. Evolutionary and ecological characterization of Mayaro virus strains isolated during an outbreak, Venezuela, 2010. Emerg Infect Dis 2015 Oct;21(10):1742-1750 [FREE Full text] [CrossRef] [Medline]
- Lednicky J, De Rochars VM, Elbadry M, Loeb J, Telisma T, Chavannes S, et al. Mayaro virus in child with acute febrile illness, Haiti, 2015. Emerg Infect Dis 2016 Dec;22(11):2000-2002 [FREE Full text] [CrossRef] [Medline]
- Coimbra TL, Santos CL, Suzuki A, Petrella SM, Bisordi I, Nagamori AH, et al. Mayaro virus: imported cases of human infection in São Paulo State, Brazil. Rev Inst Med Trop Sao Paulo 2007;49(4):221-224 [FREE Full text] [Medline]
- da Costa VG, de Rezende Féres VC, Saivish MV, de Lima Gimaque JB, Moreli ML. Silent emergence of Mayaro and Oropouche viruses in humans in Central Brazil. Int J Infect Dis 2017 Sep;62:84-85 [FREE Full text] [CrossRef] [Medline]
- de Thoisy B, Gardon J, Salas RA, Morvan J, Kazanji M. Mayaro virus in wild mammals, French Guiana. Emerg Infect Dis 2003 Oct;9(10):1326-1329 [FREE Full text] [CrossRef] [Medline]
- Halsey ES, Siles C, Guevara C, Vilcarromero S, Jhonston EJ, Ramal C, et al. Mayaro virus infection, Amazon Basin region, Peru, 2010-2013. Emerg Infect Dis 2013 Nov;19(11):1839-1842 [FREE Full text] [CrossRef] [Medline]
- Torres JR, Russell KL, Vasquez C, Barrera R, Tesh RB, Salas R, et al. Family cluster of Mayaro fever, Venezuela. Emerg Infect Dis 2004 Jul;10(7):1304-1306 [FREE Full text] [CrossRef] [Medline]
- Hotez PJ, Murray KO. Dengue, West Nile virus, chikungunya, Zika-and now Mayaro? PLoS Negl Trop Dis 2017 Aug;11(8):e0005462 [FREE Full text] [CrossRef] [Medline]
- Muñoz M, Navarro JC. Mayaro: a re-emerging Arbovirus in Venezuela and Latin America. Biomedica 2012 Jun;32(2):286-302 [FREE Full text] [CrossRef] [Medline]
- Pinheiro FP, LeDuc JW. Mayaro virus disease. In: Monath TP, editor. The Arboviruses: Epidemiology and Ecology. Boca Raton, FL: CRC Press; 1988:137-150.
- 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 [FREE Full text] [CrossRef] [Medline]
- Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron A. More diseases tracked by using Google Trends. Emerg Infect Dis 2009 Aug;15(8):1327-1328 [FREE Full text] [CrossRef] [Medline]
- Valdivia A, Monge-Corella S. Diseases tracked by using Google trends, Spain. Emerg Infect Dis 2010 Jan;16(1):168 [FREE Full text] [CrossRef] [Medline]
- Ocampo AJ, Chunara R, Brownstein JS. Using search queries for malaria surveillance, Thailand. Malar J 2013 Nov 04;12:390 [FREE Full text] [CrossRef] [Medline]
- 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 (forthcoming)(forthcoming). [CrossRef] [Medline]
- 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] [CrossRef] [Medline]
- Marques-Toledo CA, Degener CM, Vinhal L, Coelho G, Meira W, Codeço CT, et al. Dengue prediction by the web: tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis 2017 Jul;11(7):e0005729 [FREE Full text] [CrossRef] [Medline]
- 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] [CrossRef] [Medline]
- Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med 2011 May;40(5 Suppl 2):S154-S158. [CrossRef] [Medline]
- 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;11(1):e11 [FREE Full text] [CrossRef] [Medline]
- Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc 2006:244-248 [FREE Full text] [Medline]
- Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. J Med Internet Res 2013;15(7):e147 [FREE Full text] [CrossRef] [Medline]
- Segev E, Baram-Tsabari A. Seeking science information online: data mining Google to better understand the roles of the media and the education system. Public Underst Sci 2012 Oct;21(7):813-829. [CrossRef] [Medline]
- Adibi JJ, Marques ETJ, Cartus A, Beigi RH. Teratogenic effects of the Zika virus and the role of the placenta. Lancet 2016 Apr 09;387(10027):1587-1590. [CrossRef] [Medline]
- Mackay IM, Arden KE. Mayaro virus: a forest virus primed for a trip to the city? Microbes Infect 2016 Dec;18(12):724-734. [CrossRef] [Medline]
- 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] [CrossRef] [Medline]
- Bragazzi NL, Dini G, Toletone A, Brigo F, Durando P. Infodemiological data concerning silicosis in the USA in the period 2004-2010 correlating with real-world statistical data. Data Brief 2017 Feb;10:457-464 [FREE Full text] [CrossRef] [Medline]
- Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google Flu Trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One 2011;6(8):e23610 [FREE Full text] [CrossRef] [Medline]
- Butler D. When Google got flu wrong. Nature 2013 Feb 14;494(7436):155-156. [CrossRef] [Medline]
- 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. [CrossRef] [Medline]
- Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. J Epidemiol Glob Health 2017 Sep;7(3):185-189 [FREE Full text] [CrossRef] [Medline]
- Tran US, Andel R, Niederkrotenthaler T, Till B, Ajdacic-Gross V, Voracek M. Low validity of Google Trends for behavioral forecasting of national suicide rates. PLoS One 2017;12(8):e0183149 [FREE Full text] [CrossRef] [Medline]
- 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] [CrossRef] [Medline]
- Roberts H, Seymour B, Fish SA, Robinson E, Zuckerman E. Digital health communication and global public influence: a study of the Ebola epidemic. J Health Commun 2017;22(sup1):51-58. [CrossRef] [Medline]
- Rabarison KM, Croston MA, Englar NK, Bish CL, Flynn SM, Johnson CC. Measuring audience engagement for public health Twitter chats: insights from #LiveFitNOLA. JMIR Public Health Surveill 2017 Jun 08;3(2):e34 [FREE Full text] [CrossRef] [Medline]
- Glowacki EM, Lazard AJ, Wilcox GB, Mackert M, Bernhardt JM. Identifying the public's concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: an analysis of a Zika live Twitter chat. Am J Infect Control 2016 Dec 01;44(12):1709-1711. [CrossRef] [Medline]
|ICTs: information and communication technologies|
|MAYV: Mayaro virus|
|RSV: relative search volume|
Edited by G Eysenbach; submitted 07.10.17; peer-reviewed by M Martini, G Dini, I Barberis, M Behzadifar; comments to author 10.10.17; revised version received 12.10.17; accepted 12.10.17; published 01.12.17
©Mohammad Adawi, Nicola Luigi Bragazzi, Abdulla Watad, Kassem Sharif, Howard Amital, Naim Mahroum. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 01.12.2017.
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