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dc.contributor.authorGluskin, Rebecca T.en_US
dc.contributor.authorSantillana, Mauricioen_US
dc.contributor.authorBrownstein, John S.en_US
dc.date.accessioned2014-02-13T19:00:50Z
dc.date.issued2013en_US
dc.identifier.citationGluskin, Rebecca T., Mauricio Santillana, and John S. Brownstein. 2013. “Using Google Dengue Trends to Estimate Climate Effects in Mexico.” Online Journal of Public Health Informatics 5 (1): e94.en
dc.identifier.issn1947-2579en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11708543
dc.description.abstractObjective: To evaluate the association between Dengue Fever (DF) and climate in Mexico with real-time data from Google Dengue Trends (GDT) and climate data from NASA Earth observing systems. Introduction: The incidence of dengue fever (DF) has increased 30 fold between 1960 and 2010 [1]. The literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of DF outbreaks [2]. We use real-time data from GDT and real-time temperature estimates from NASA Earth observing systems to examine the relationship between dengue and climate in 17 Mexican states from 2003–2011. For the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue. Methods: The GDT estimates are derived from internet search queries and use similar methods as those developed for Google Flu Trends [3]. To validate GDT data, we ran a correlation between GDT and dengue data from the Mexican Secretariat of Health (2003–2010). To analyze the relationship between GDT and varying lags of temperature, we constructed a time series meta-analysis. The mean, max and min of temperature were tested at lags 0 –12 weeks using data from the Modern Era Retrospective-Analysis for Research and Applications. Finally, we built a binomial model to identify the minimum 5° C temperature range associated with a 50% or higher Dengue activity threshold as predicted by GDT. Results: The time series plot of GDT data and the Mexican Secretariat of Health data (2003– 2010) (Figure 1) produced a correlation coefficient of 0.87. The time series meta-analysis results for 17 states showed an increase in minimum temperature at lag week 8 had the greatest odds of dengue incidence, 1.12 Odds Ratio (1.09–1.16, 95% Confidence Interval). The comparison of dengue activity above 50% in each state to the minimum temperature at lag week 8 showed 14/17 states had an association with warmest 5 degrees of the minimum temperature range. The state of Sonora was the only state to show an association between dengue and the coldest 5 degrees of the minimum temperature range. Conclusions: Overall, the incidence data from the Mexican Secretariat of Health showed a close correlation with the GDT data. The meta-analysis indicates that an increase in the minimum temperature at lag week 8 is associated with an increased dengue risk. This is consistent with the Colon-Gonzales et al. Mexico study which also found a strong association with the 8 week lag of increasing minimum temperature [4]. The results from this binomial regression show, for the majority of states, the warmest 5 degree range for the minimum temperature had the greatest association with dengue activity 8 weeks later. Inevitably, several other factors contribute to dengue risk which we are unable to include in this model [5]. IPCC climate change predictions suggest a 4° C increase in Mexico. Under such scenario, we predict an increase in the number of days the minimum temperature falls within the range associated with DF risk.en
dc.language.isoen_USen
dc.publisherUniversity of Illinois at Chicago Libraryen
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692944/pdf/en
dash.licenseLAAen_US
dc.subjectTime Seriesen
dc.subjectMexicoen
dc.subjectGoogle Dengue Trendsen
dc.subjectClimate Changeen
dc.subjectMeta-analysisen
dc.titleUsing Google Dengue Trends to Estimate Climate Effects in Mexicoen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalOnline Journal of Public Health Informaticsen
dash.depositing.authorSantillana, Mauricioen_US
dc.date.available2014-02-13T19:00:50Z
dash.contributor.affiliatedSantillana, Mauricio
dash.contributor.affiliatedBrownstein, John


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