Predicting Dengue Transmission in a Changing Climate to Improve Mosquito Control

Mosquito presence, abundance, and ability to transmit disease are affected by temperature, rainfall, and humidity, and our research objective is to use these known relationships to predict where and when dengue and chikungunya epidemics are most likely to occur. Dengue and chikungunya affect millions of people every year, and without an effective vaccine, mosquito control is the most effective option for reducing disease risk.

Our research seeks to promote more effective allocation of costly mosquito control resources by predicting where and when mosquito control will be most effective. Using data from laboratory experiments and theoretical models of how temperature affects mosquito and virus development, we developed climate-driven dynamic models of Aedes aegypti-transmitted diseases. We are validating these models with mosquito abundance and human disease cases documented by parallel research programs in Kenya (led by Desiree LaBeaud) and Ecuador (led by Anna Stewart-Ibarra). We will be adapting these models to use satellite-derived climate data to predict disease risk and inform mosquito control programs across broader and more poorly-studied areas and to assess different mosquito control strategies.

Funding provided by the Stanford University Woods Institute for Environment Environmental Ventures Project Grant. 7/2016 – 7/2019.

Investigative Team: Erin Mordecai (PI), Desiree LaBeaud (co-PI), Eric Lambin (co-PI)

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