Explanation of parameters in mechanistic model.

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.

病毒向新宿主物种的溢出事件频繁发生。通常情况下，溢出会导致新宿主出现终末感染（dead-end infections）；有时会形成逐渐消亡的顿挫式传播链；极少数情况下，则会引发具备持续传播能力的大规模流行。若能在溢出事件发生后早期预判其最终结局，便可更精准地投入资源开展疾病出现的监测、应对或防控工作。本研究旨在识别与上述结局相关的早期流行病学特征，包括可预测病毒在新宿主中种群水平持续存在的特征。为识别此类特征，我们在秀丽隐杆线虫（Caenorhabditis nematode）-奥尔塞病毒（Orsay virus）模型体系中通过实验手段诱导病毒溢出，并针对7个非本地宿主物种的8个菌株的重复种群，测定了暴露种群的感染率，以及感染宿主的病毒脱落量与感染强度。随后，我们从暴露种群中选取20只成年线虫，转移至无病毒的培养板中进行繁殖，以此建立新的种群——线虫在此种群中具备传播病毒的潜在可能。我们通过定量PCR（quantitative PCR）对传代宿主种群的病毒存在情况进行追踪，共进行10次传代，或直至病毒检测呈阴性（即病毒消失）。随后，我们分别采用相关性建模与机制建模方法，探究哪些流行病学特征与病毒的种群水平持续存在相关。在相关性模型中，我们发现病毒消失前的传代次数与溢出宿主种群的早期流行病学特征存在关联，包括初始暴露种群的感染率、宿主可检测到的病毒脱落能力，以及宿主物种的相对易感性，但与感染强度无关。当将上述所有特征同时纳入相关性模型时，仅感染率与病毒脱落能力与病毒维持存在显著关联，且该模型可解释数据中超过一半的变异。随后我们构建了一个机制模型，尝试通过利用我们的流行病学特征数据，计算传代过程中至少有1只具备感染同种个体能力的线虫被转移的概率，以此解释病毒传代成功的原因。该机制模型单独可解释38%的数据变异。为探究该机制模型的不足，我们通过模型选择测试了一系列更大的模型：这些模型可纳入或排除各项流行病学特征，同时纳入菌株、实验谱系、传代次数与区组的随机效应，并将机制预测值作为偏移项。我们发现，将机制预测值与感染率、感染强度及随机效应一同纳入模型时，可解释66%的数据变异。综上，本研究证实，早期流行病学特征可在很大程度上解释病毒溢出事件的最终结局。