Mitigating bat viruses: from forecasting spillover to control at the source
In plain English
AI plain-English summaryBats carry viruses that cause Ebola, SARS, and Nipah, but we only act after people get sick—this fellowship aims to shift that response to prevention. The problem is reactive: vaccines and treatments are deployed after a bat virus spills over into humans or livestock, not before. For vampire bat-transmitted rabies in the Americas, this costs lives and millions in economic losses. The researcher will use routine surveillance data to build models that forecast where and when outbreaks will occur, so vaccines can be sent ahead of the virus. Field studies and viral genomics will then map the transmission chain from individual bats to whole landscapes, identifying weak points where interventions could break the cycle. If successful, this could change how we handle bat viruses globally. The most immediate impact is on public health and livestock industries in Latin America, where rabies kills thousands of cattle and some people each year. Longer-term, the project will create a toolbox—statistical models, genomic methods, and potentially self-spreading vaccines—to manage other bat-borne threats like Ebola or Nipah before they become human epidemics. This is applied science with a clear translational goal, not fundamental curiosity-driven work.
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