Abstract
Spillover of viruses into novel host species occurs frequently. Often, spillover results in dead-end infections in novel hosts, sometimes, in stuttering transmission chains that die out, and rarely, in large epidemics with sustained transmission. If we could identify early which outcome will occur following a spillover event, we could more appropriately invest in efforts to surveil, respond to, or prevent disease emergence. Our goal was to identify early epidemiological characteristics that correlate with these outcomes, including those predictive of population-level virus persistence in novel hosts. To identify these characteristics, we experimentally induced spillover in the Caenorhabditis nematode-Orsay virus system and measured infection prevalence in exposed populations and virus shedding and infection intensity from infected hosts in replicate populations of eight strains belonging to seven non-native host species. We then passaged 20 adult nematodes from exposed populations to virus-free plates where they reproduced, initiating new populations to which they had the potential to transmit virus. We used quantitative PCR to track virus presence in passaged host populations for 10 passages or until virus was undetectable, indicating its loss. We then used a correlative modeling and a mechanistic modeling approach to understand which epidemiological characteristics were associated with population-level viral persistence. In our correlative models, we found that the number of passages until virus loss was associated with early epidemiological characteristics in the spillover host populations, including infection prevalence in the initially exposed population, the ability of hosts to detectably shed the virus, and the relative susceptibility of the host species, but not infection intensity. When all these characteristics were included simultaneously in a correlative model, only infection prevalence and shedding were significantly associated with virus maintenance, and the model explained over half of the variation in the data. We then developed a mechanistic model that attempts to explain virus passage success by using our epidemiological characteristics data to calculate the probability that at least one worm infectious enough to infect a conspecific is transferred during passage. This mechanistic model explained 38% of the variation in the data on its own. With the goal of understanding how our mechanistic model falls short, we used model selection to test a suite of larger models that included or excluded each epidemiological characteristic and included random effects of strain, experimental line, passage number, and block while the mechanistic prediction was included as an offset. We found that 66% of the variation in our data could be explained by a model that included our mechanistic prediction in addition to infection prevalence, infection intensity, and random effects. Altogether, our study demonstrates that early epidemiological characteristics can play a substantial role in explaining the ultimate outcome of a spillover event.