Predictive modeling of mosquito abundance and dengue transmission in Kenya
Abstract
Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMGC24A..04C
- Keywords:
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- 1630 Impacts of global change;
- GLOBAL CHANGE;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS;
- 4322 Health impact;
- NATURAL HAZARDS;
- 4323 Human impact;
- NATURAL HAZARDS