Vector in the machine: how accurately can mosquito transmission of viruses be predicted by machine learning?

Through climate change and human travel, many mosquito-borne viruses are rapidly emerging and having substantial impacts on global health. Zika and West Nile have emerged over the last 2-3 decades, and Usutu virus has emerged across Europe, including incursion into the UK in 2020. For each of these viruses, previously naive mosquito species and populations were the drivers of transmission in new areas.
The ability to predict whether a novel mosquito is capable of virus transmission computationally and before incursion will greatly bolster estimation of risk, preparedness, and mitigation of new outbreaks.
Our group has developed machine learning frameworks to predict virus/host associations, broadly across all mammals, as well as specifically for coronaviruses. Ongoing work is adapting those pipelines to mosquito-borne viruses, and this project will experimentally validate and refine those machine learning pipelines.

This interdisciplinary project will be the first to experimentally validate machine learning-produced mosquito/virus competence predictions, and the first to make refinements based on experimental validation.
Using machine learning predictions of competent mosquito/virus combinations, and experimentally validate the pipeline by live virus infection of mosquitoes in the CL3 lab. Colony mosquitoes will be fed on a virus-spiked blood-meals, incubated, and have their saliva extracted. Presence of virus in the saliva (plaque assay or qRT-PCR) demonstrates competence. Using these results, strengths/weaknesses in the machine learning pipeline will be identified i.e. what was it failing to predict? Identifying patterns and refine the pipeline accordingly.