EAGER: Evaluating the feasibility of a transmissible vaccine within bat populations.

当一种通常在野生动物种群中传播的病毒进入人类种群时，就会出现像新冠肺炎这样的新发传染病。不幸的是，正如我们在新冠肺炎上非常清楚地看到的那样，一旦发生这种跳入人类人口的情况，可能就太晚了，无法阻止这种传染病的迅速传播。这个问题的一个新的解决方案是在动物种群中传播的高风险病原体进入人类种群之前将其消灭。实现这一目标的一种方法是为动物群体开发一种疫苗，这种疫苗能够将自己从一只动物传播到另一只动物，在病原体传播时为每只动物接种疫苗。基因工程的最新进展使这种自我传播疫苗的开发成为可能。然而，我们尚不清楚的是，这样的疫苗是否会成为从野生蝙蝠种群中消除冠状病毒(比如导致新冠肺炎的病毒)的有效工具。这项研究将使用数学和计算模型来评估这种方法的可行性，并为开发降低新发传染病风险的新型疫苗奠定基础。这项研究项目还将支持第一代女研究生在数学和传染病方面的培训。拟议的工作将开发数学和计算模型，评估使用可传播疫苗来降低野生蝙蝠种群中类似SARS冠状病毒的流行率的可行性。这些模型将使用已发表的关于野生蝙蝠种群中冠状病毒流行率和血清阳性率的数据以及蝙蝠种群结构、人口学和测距行为的数据进行参数化。参数化模型将预测使用具有不同传播性的疫苗载体的干预结果，从而为可用于设计有效的可传播疫苗的载体设定边界。除了提高我们减少新出现的传染病威胁的能力外，拟议的工作还将为研究生提供数学和传染病方面的培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Emerging infectious diseases like COVID-19 can arise when a virus normally circulating within a population of wild animals jumps into the human population. Unfortunately, as we have seen very clearly with COVID-19, once this jump into the human population occurs, it may be too late to stop the infectious disease from spreading rapidly. A novel solution to this problem is to extinguish high-risk pathogens circulating within animal populations before they can jump into the human population. One way this can be accomplished is by developing a vaccine for the animal population that is capable of transmitting itself from one animal to the next, vaccinating each animal against the pathogen as it spreads. Recent advances in genetic engineering have made the development of this sort of self-disseminating vaccine possible. What we do not yet know, however, is if such a vaccine would be an effective tool for eliminating coronaviruses (like the virus that causes COVID-19) from the wild bat populations within which they normally circulate. This research will use mathematical and computational models to evaluate the feasibility of this approach and lay the groundwork for developing new types of vaccines that reduce the risk of emerging infectious disease. This research project will also support the training of a first generation female graduate student at the interface of mathematics and infectious diseases.The proposed work will develop mathematical and computational models evaluating the feasibility of using a transmissible vaccine to reduce the prevalence of SARS-like coronaviruses within wild Rhinolophus bat populations. These models will be parameterized using published data on coronavirus prevalence and seroprevalence within wild bat populations and data on bat population structure, demography, and ranging behavior. Parameterized models will predict the outcome of interventions employing vaccine vectors with different degrees of transmissibility and thus set boundaries on the vectors that could be used to engineer an effective transmissible vaccine. In addition to advancing our ability to reduce the threat of emerging infectious disease, the proposed work will provide training at the interface of mathematics and infectious disease for a graduate student.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.