Relationship Between Climate and the Dengue Fever Vector, Aedes Aegypti, in Machala, Ecuador

Author/​Artist
Mulligan, Kathleen [Browse]
Format
Senior thesis
Language
English

Details

Advisor(s)
Dobson, Andrew P. [Browse]
Department
Princeton University. Department of Ecology and Evolutionary Biology [Browse]
Certificate
Princeton University. Program in Global Health and Health Policy [Browse]
Class year
2017
Summary note
AbstractBackground Dengue fever is an increasingly widespread disease of which up to one-half of the world’s population is at risk. The disease is caused by the dengue virus, which is spread to humans through the bite of the Aedes aegpyti mosquito. Understanding the climate patterns and socio-ecological factors that may influence this mosquito’s feeding and reproductive timing may help inform practices that curb incidence of the disease. Machala, Ecuador is a city with high dengue fever incidence, so finding local trends that may allow epidemiologists to predict when an outbreak may occur in the future would allow for more time to prepare for a potential outbreak and minimize the burden of disease.MethodsUsing data collected from 2013-2015, I analyzed three variables collected from households of dengue fever patients that may predict increased rates of dengue fever incidence: mosquito abundance, sex ratio, and proportion of females of the mosquito population. I analyzed these experimentally-collected variables against climate values with a lag of 0, 15, 30, or 45 days, and created a best-fit model of a significant relationship based on past data. ResultsAcross all of mosquito and climate variables that were analyzed, temperature could not be reliably correlated with any mosquito-related variable. Precipitation with a 15-day lag period, however, had a clear positive correlation total mosquito abundance. A model was created based on this relationship that could predict expected mosquito abundance fifteen days after recording a precipitation value. When tested against the past data, this model had an R-squared value of .192, meaning that the model could successfully predict 19.2% of the data’s variance and could be incorporated into a broader system of predictive models dengue fever incidence in Machala. ConclusionsThis model can be incorporated into an Early Warning System (EWS) for public health workers in Machala to help predict when A. aegypti abundance will increase. As such, this model would be able to predict when increased incidence of A. aegypti-carried diseases, particularly dengue fever, may occur. The significance of precipitation may be because of Machala’s inadequate garbage collection system, which leads to more oviposition sites for female mosquitoes. Aiming public efforts towards improving public waste management would likely lower the burden of disease in the area.
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