Using Sensors to Understand Insect-Vectored Neglected Infectious Diseases
Yanping Chen of the University of California, Riverside in the U.S. will develop an inexpensive and robust sensor to directly measure the real-time density of insect vectors that transmit parasitic diseases to help plan intervention and treatment programs. Preliminary results indicate that insects can be classified based on the frequency of their wingbeats, which also varies depending on the time of day. Chen will develop an accurate detection system by investigating combining wingbeat frequency with circadian rhythms and other behaviors. A software system will also be produced that can translate the data into real-time counts of insect numbers and produce density maps of their distribution. The sensors will be field tested in Cameroon and Cambodia/Thailand.