Predicting population of disease-carrying mosquitoes
Researchers at University of Adelaide in Australia create a model predicting population peaks of disease-carrying mosquitoes; model will help in developing cost-effective mosquito control policies
Researchers working at the University of Adelaide have built a model to predict the population peaks of disease-carrying mosquitoes. This should help the fight against outbreaks of serious mosquito-borne disease such as dengue and Ross River fever by allowing efficient and cost-effective mosquito control, said ecologist associate professor Corey Bradshaw.
“The risk of disease transmission is highest when mosquitoes are at their most abundant,” said Bradshaw, who is from the university’s School of Earth and Environmental Sciences and also employed as a senior scientist by the South Australian Research and Development Institute (SARDI). “This model is a tool that helps predict when there is going to be a higher-than-average outbreak so that population control efforts can be implemented when they are going to be most effective and are most needed.”
The University of Adelaide researchers analyzed fifteen years of population data of Aedes vigilax, the northern Australian mosquito that transmits the Ross River and Barmah Forest viruses, and compared it with environmental factors affecting populations including tides and rainfall. “We found that basic environmental monitoring data can be coupled with relatively simple population models to assist in predicting the timing and magnitude of mosquito peaks, which lead to disease outbreaks in human populations,” Bradshaw added.
In salt-loving species such as the Aedes vigilax mosquito, populations tend to peak after very high tides. The frequency of high tides, however, and the amount of rainfall in the preceding months when mosquito numbers are low are the critical elements dictating the magnitude of eventual peaks.
“Previously, we didn’t know how big that peak would be,” said Bradshaw. “With this model, mosquito control efforts can be scaled according to the expected size of a future peak.” Bradshaw said the same model could be applied to other mosquito species, for instance dengue- or malaria-transmitting species, and others in tropical regions worldwide.
