Monitoring mosquito eco-systems and vector-control strategies using a stand-off optical sensor.

Monitoring mosquito ecosystems and vector-control strategies using a stand-off optical sensor. PI: B. Thomas â€Â" NIH R21 Project Summary: Vector control strategies remain one of the most effective ways to protect human populations from the large number of mosquito borne diseases such as malaria, dengue fever, zika virus, or West Nile virus. Mosquito populations are generally monitored using physical traps, however this method suffers from many disadvantages. It requires long and expensive laboratory analysis by qualified personnel which drastically reduces the number of observed insects as well as time of trap deployment. Traps also provide a poor estimate of the actual population size or population density because the attractive range of traps is generally unknown and may change with weather conditions. These limitations are strong drawbacks in our ability to evaluate the effectiveness of various types of vector-control strategies (chemicals, biological, environmental modifications etc.). Inferior methods are not necessarily identified which ultimately contributes to the spread of infectious diseases. In this context, we argue that new methodologies to monitor insect population dynamics is key in the necessary effort to improve control program performance. A team from the New Jersey Institute of Technology in collaboration with the Hudson Mosquito Program seeks support to carry out a series of field experiments using a new optical sensor capable of identifying in real-time the family, species, and gender of mosquitoes in its field of view. The laser-based instrument is a dual-wavelength polarization-sensitive stand-off sensor. For each flying insect transiting through the infrared laser beams, the sensor can retrieve the optical properties of the wings and body of the insect as well as its wing beat frequency. Preliminary data from a laboratory prototype and numerical simulations indicate that the instrument, using a supervised machine learning classifier, can identify the species, gender, and gravidity of mosquitoes up to 300 m away. The instrument will be deployed in a high mosquito density area in New Jersey to continuously monitor the mosquito population over the whole season from April to October 2021. Continuous measurements will allow to identify a number of insects that is orders a magnitude higher than physical traps. As the probed volume of air is known, data analysis will provide the population density for each class of insects from which the population dynamics will be derived. In addition, the time and date of each insect transit allow to study the circadian rhythm, peak activities, and behavior as a function of atmospheric conditions measured by a weather station. In 2022, a similar experiment will be conducted at the same location while the Hudson Mosquito Program will conduct a vector control campaign targeting Culex and Aedes mosquitoes, both responsible for the spread of various infectious diseases. The impact of multiple applications of airborne pyrethroid insecticide on targeted and non-targeted insects will be evaluated by studying the mortality rates and population dynamics for each species. Both years, the data will be compared to physical traps on site, the current gold standard method, for further analysis and validation.