The danger posed by infectious agents necessitates the development of new tools to both predict and manage emerging diseases. One promising approach is the development of recombinant viral vaccines that are themselves infectious. Transmissible vaccines have been shown to greatly reduce the effort required to control the spread of zoonotic pathogens in their animal reservoirs, thereby limiting the chances of human infection. While this approach can be immensely useful in combating emerging diseases, the ability to self-replicate exposes vaccines to evolutionary change. As a recombinant transmissible vaccine mutates, selection is expected to favor variants with reduced efficacy against the pathogen and increased transmission rates. This creates a trade-off in vaccine design priorities between efficacy, reduction in transmission, and evolutionary stability. Here we ask how such trade-offs influence the overall performance of transmissible vaccines. We find that evolutionary instability can dramatically reduce performance, even for vaccine candidates with ideal efficacy and transmission dynamics. One method to increase functional stability is through the inclusion of multiple redundant antigens. We show that the inclusion of a second antigen can increase functional stability in the face of evolutionary pressures. However, the benefit of genetic redundancy plus any gain in efficacy must outweigh the reduction in transmission for dual-gene designs to be effective. Our results suggest that the successful application of recombinant transmissible vaccines will require consideration of evolutionary dynamics and epistatic effects, as well as basic measurements of epidemiological features.

The data in this study was generated using a combination of custom c++ code and Mathematica notebooks. Data were analyzed and plotted using R scripts.

Uploaded data includes all files and scripts as well as the folder structure used to generate results zipped into a single archive. All effort was made to utilize relative path structures to document code in as much detail as possible to ease replication of results.