OJPHI: Vol. 5
Journal Information
Journal ID (publisher-id): OJPHI
ISSN: 1947-2579
Publisher: University of Illinois at Chicago Library
Article Information
©2013 the author(s)
open-access: This is an Open Access article. Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes.
Electronic publication date: Day: 4 Month: 4 Year: 2013
collection publication date: Year: 2013
Volume: 5E-location ID: e94
Publisher Id: ojphi-05-94

Using Google Dengue Trends to Estimate Climate Effects in Mexico
Rebecca T. Gluskin*1
Mauricio Santillana2
John S. Brownstein13
1Boston Children’s Hospital, Boston, MA, USA;
2Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA;
3Department of Pediatrics, Harvard Medical School, Boston, MA, USA
*Rebecca T. Gluskin, E-mail: rgluskin@health.nyc.gov

Abstract
Objective

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.


Acknowledgments

Funded by the NIH Grant # 1R01LM010812-01 and the Applied Public Health Informatics Fellowship Program administered by CSTE and funded by the Centers for Disease Control and Prevention (CDC) Cooperative Agreement 3U38HM000414-04W1.


References
1.. WHOW.H.O., Dengue2010
2.. Yang HM, et al. Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengueEpidemiol Infect 2009;137(8):1188–202.
3.. Chan EH, et al. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillancePLoS Negl Trop Dis 2011;5(5):e1206.
4.. Colon-Gonzalez FJ, Lake IR, Bentham G. Climate variability and dengue fever in warm and humid MexicoAm J Trop Med Hyg 2011;84(5):757–63.
5.. Thai KT, Anders KL. The role of climate variability and change in the transmission dynamics and geographic distribution of dengueExp Biol Med (Maywood) 2011;236(8):944–54.

Figures
ojphi-05-94f1.tif
[Figure ID: f1-ojphi-05-94]
Figure 1 

Time Series Correlation: Google Dengue Trends vs. Secretariat of Health, Mexico 2003–2010



Article Categories:
  • ISDS 2012 Conference Abstracts

Keywords: Time Series, Mexico, Google Dengue Trends, Climate Change, Meta-analysis.




Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org