SBIR-STTR Award

Mosquitoes are the deadliest animal in the world infecting over 350 million people each year with a range of diseases. Driven by climate change and insecticide resistance this burden is expected to grow. Due to a lack of effective vaccines for mosquito-borne viruses integrated control of mosquito populations remains the primary strategy for disease mitigation. Mosquito surveillance -monitoring an area to understand mosquito species composition abundance and spatial distribution- is critical to informing decisions about what control strategies will be most effective in specific locations and is necessary to determine if interventions are effectively decreasing mosquito populations. Conventional surveillance practice relies on manual distribution of mosquito traps and routine visits to collect the specimens. Due to the resource-intensive nature of vector surveillance many county and municipal departments of health particularly those in rural communities do not have the capacity and capability to conduct routine surveillance. Here we propose to develop the first computer vision driven automated counting trap for mosquito surveillance. Building on prior work to develop a computer vision system for automated mosquito species classification we will build an optical system for distinguishing between mosquitoes and non-mosquito arthropods. Image recognition techniques will focus on object detection in context of the new optical design and hardware specifications will be considered with the aim of transitioning development into a low- cost system to remotely transmit abundance information to community public health agencies and support actionable biosurveillance.

___(NOTE: Note: no official Abstract exists of this Phase II projects. Abstract is modified by idi from relevant Phase I data. The specific Phase II work statement and objectives may differ)___ Mosquitoes are the deadliest animal in the world infecting over 350 million people each year with a range of diseases. Driven by climate change and insecticide resistance this burden is expected to grow. Due to a lack of effective vaccines for mosquito-borne viruses integrated control of mosquito populations remains the primary strategy for disease mitigation. Mosquito surveillance -monitoring an area to understand mosquito species composition abundance and spatial distribution- is critical to informing decisions about what control strategies will be most effective in specific locations and is necessary to determine if interventions are effectively decreasing mosquito populations. Conventional surveillance practice relies on manual distribution of mosquito traps and routine visits to collect the specimens. Due to the resource-intensive nature of vector surveillance many county and municipal departments of health particularly those in rural communities do not have the capacity and capability to conduct routine surveillance. Here we propose to develop the first computer vision driven automated counting trap for mosquito surveillance. Building on prior work to develop a computer vision system for automated mosquito species classification we will build an optical system for distinguishing between mosquitoes and non-mosquito arthropods. Image recognition techniques will focus on object detection in context of the new optical design and hardware specifications will be considered with the aim of transitioning development into a low- cost system to remotely transmit abundance information to community public health agencies and support actionable biosurveillance.