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.
