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

通过气候变化和人类旅行，许多蚊媒病毒正在迅速出现，并对全球健康产生重大影响。寨卡病毒和西尼罗河病毒在过去20 - 30年中出现，而西尼罗河病毒已经在欧洲出现，包括2020年入侵英国。对于这些病毒中的每一种，先前未感染的蚊子物种和种群都是新地区传播的驱动力。在入侵之前通过计算预测新蚊子是否能够传播病毒的能力将大大加强对新疫情的风险估计、准备和缓解。我们的团队已经开发了机器学习框架来预测病毒/宿主的关联，广泛适用于所有哺乳动物，特别是冠状病毒。正在进行的工作是使这些管道适应蚊子传播的病毒，该项目将通过实验验证和完善这些机器学习管道。这个跨学科项目将是第一个通过实验验证机器学习产生的蚊子/病毒能力预测的项目，也是第一个基于实验验证进行改进的项目。使用机器学习预测蚊子/病毒组合，并在CL 3实验室通过蚊子的活病毒感染实验验证管道。蜂群蚊子将被喂食一种掺有病毒的血餐，孵化，并提取它们的唾液。唾液中存在病毒（空斑试验或qRT-PCR）证明了能力。使用这些结果，将确定机器学习管道中的优势/劣势，即它未能预测什么？识别模式并相应地优化管道。

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.