Predicting the spread and impact of transmissible vaccines

Infectious diseases that normally thrive in wild animals occasionally make the leap into the human population. For instance, rabies virus infects and kills tens of thousands of people each year when wild animals carrying the virus bite humans and transmit the virus to them. Other viruses that occasionally leap from animals to humans are even more dangerous because they can also transmit from human to human and thus potentially seed epidemics or pandemics. Unfortunately, we do not yet have effective solutions in place to stop these infectious diseases from spilling over into the human population. Instead, our current approach to these animal diseases is reactive, and focuses on medical treatment of humans who have become infected and corralling human outbreaks before they can spread and become full blown epidemics or pandemics. A promising solution to this challenging problem is the development of wildlife vaccines that can spread themselves from one animal to the next. By self-disseminating, these vaccines magnify the spread of immunity within the wild animal population and reduce or eliminate the risk of spillover into the human population. Although multiple self-disseminating animal vaccines are being developed, we do not yet have the mathematical, statistical, and computational tools we need to critically evaluate their performance and thus make informed decisions about their possible use. Work on this project will develop these quantitative tools and enable candidate self-disseminating vaccines to be critically evaluated before they are used. In addition, this project will train first-generation college students from rural backgrounds to use mathematical and computational models to evaluate and optimize emerging biotechnologies critical to the future of the US economy. Student recruitment will be facilitated by offering competitive financial support that relieves pressure to abandon research experiences in favor of traditional employment. Finally, this project will continue development of a website that explains self-disseminating vaccines to the public, disseminates relevant research results, and examines the state of this emerging technology. Before making the decision to conduct even small-scale field trials, the likelihood that a self-disseminating vaccine will improve human health should be quantified. This requirement poses a formidable technical challenge because data on the behavior of the vaccine within the target animal population cannot be collected prior to release. This project will overcome this technical challenge using mathematical models of recombinant vector transmissible vaccines that can be parameterized using a combination of field and laboratory data. Specifically, mathematical models will be developed that integrate the age structure of the reservoir population and the explicit pattern of vaccine shedding from animals infected with vaccine. These models will take the form of a system of partial differential equations. Field data will come from trapping studies of the reservoir animal that record the age of each captured animal and whether it was infected by the vector virus used to construct the candidate vaccine. Laboratory data will describe the temporal pattern of vaccine shedding from reservoir animals experimentally infected with the vaccine. Approximate Bayesian computation will be used to parameterize the models and a stochastic simulation framework developed for predicting the outcome of a proposed vaccine release. By repeatedly simulating a vaccine release for models parameterized by drawing randomly from the posterior distribution, this framework faithfully integrates reservoir ecology, randomness in biological processes, and uncertainty in parameter estimates. The methodology developed by this project will be applied to a prototype self-disseminating vaccine for Lassa virus but will be broadly applicable to self-disseminating vaccines developing for a range of animal reservoirs. This project is jointly funded by the Population and Community Ecology (PCE) Cluster in the Division of Environmental Biology, the Established Program to Stimulate Competitive Research (EPSCoR), and the Mathematical Biology Program in the Division of Mathematical and Physical Sciences. 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.