Research Project
6587123
DESCRIPTION (provided by applicant): Mosquito-borne diseases are spreading worldwide. The monitoring of mosquito populations is an essential element in risk assessment of disease transmission. When monitoring, valuable time is often lost and accuracy compromised due to a series of procedural bottlenecks. The problem lies in the labor-intensive task of processing the trap-catch (mosquito identification and sexing) tabulating the data, and reporting the information to the proper individuals. Through the development of an automated arthropod identification device (AID), ISCA Technologies proposes to overcome these bottlenecks in information flow and issues of accuracy to support on-time decision-making. Our preliminary data indicate that it is feasible to positively identify flying mosquitoes to species and gender in a fractions of seconds. ISCA will develop a device that records and stores trap-catch data (quantity and identification of mosquitoes caught in monitoring traps with the time and date stamp), which will through a series of steps, be processed in one central location to generate concise reports for decision-makers. Automation will not only provide instantaneous estimates of mosquito population dynamics, allowing for timely decision-making, it will allow for a more frequent sampling of the population to discern whether the population is growing or dwindling in size. This type of information will ultimately increase the efficacy of mosquito abatement programs both in time and space. The successful development of a reliable, yet affordable, field device which automatically identifies and quantifies mosquito trap-catch data will make great strides in advancing the techniques currently used to monitor mosquito populations. Combine this proposed technology together with capabilities already provided by our company (wirelessly integrating trap-catch data to an automated internet support system) and the practice of vector control will be transformed to a new level of understanding providing a solid basis for the development of new, more effective, vector control standards. The merging of "instantaneous" trap-catch data with an artificial neural network and GIS capabilities provided by our servers will yield up-to-date concise easy-to-read reports designed for the decision makers. This type of "up to the minute reporting" will serve decision makers like a "Doppler radar', creating a new niche in the worldwide field of vector control.
