Learning to Count Mosquitoes for the Sterile Insect Technique

Yaniv Ovadia (Google);Yoni Halpern (Google);Dilip Krishnan (Google);Josh Livni (Verily);Daniel Newburger (Verily);Ryan Poplin (Google, Inc.);Tiantian Zha (Verily (Google Life Sciences));D. Sculley (Google, Inc.)

Abstract

Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice.