Biology subjects

Wearing, H. J.

Publications and source records attributed to Wearing, H. J..

3 recordsLinked to original sources

bioRxiv2026

Eco-evolutionary Dynamics of Colonization: When Does Source Selection Matter?

Ancestral balancing selection may facilitate evolutionary rescue following migration into a new habitat by providing founder populations with adaptive alleles, even when those alleles were not the fittest in the ancestral habitat. However, recent studies emphasize the roles of immigration history and admixture on colonization success. The extent to which balanced polymorphism may contribute to colonization success and the conditions under which it matters remain unclear. We use computer simulations to investigate how the mode of selection in the source population influences colonization success in new habitats under eco-evolutionary population dynamics, different invasion scenarios, and different selection regimes in the new habitat. We model source populations under balancing selection, directional selection, and neutrality, and allow founder groups of different sizes to invade a novel stable or perturbed habitat. We find that balancing selection in ancestral populations leads to higher rates of establishment in new habitats than are observed under source neutrality or directional selection when founder populations are relatively small, which would occur with a population bottleneck. This trend is observed regardless of whether invaders experience stable or variable conditions in a new habitat and persists under increasingly harsh conditions or greater environmental perturbations. When the colonizing populations are large, selection in the source population has little effect on invasion success. This study elucidates the interaction between genetic diversity and propagule pressure on colonization success, highlighting the number of adaptive alleles as a unique metric.

genetics↗

bioRxiv2026

Quantifying Uncertainty in the Re-Emergence of Yellow Fever Virus

Emerging infectious diseases are a persistent public health threat that challenge deterministic, mechanistic modeling approaches. Because outbreaks initially start with a low number of infected hosts, their dynamics are highly stochastic, making traditional deterministic methods, e.g. ordinary differential equations, unable to qualitatively or quantitatively capture the transmission dynamics. In place, stochastic models are used, such as Markov chain models, but these models present their own challenges. Often, to infer a quantity of interest with stochastic models, we need to sample the models distribution many times over, introducing an additional source of noise to our analysis. This additional noise makes the inference of our quantity of interest more difficult and computationally expensive. Here, we show how novel tools from the field of uncertainty quantification can be used to efficiently separate these two channels of noise, allowing us to make rigorous statistical inferences about processes important for disease emergence. We illustrate these techniques with a model of yellow fever virus spillover in the Americas, a virus that has seen rapid re-emergence amongst multiple hosts and vectors in South America over the last decade. We show that only a handful of parameters uncertainties greatly affect variation in cumulative disease incidence. In particular, uncertainty in patch connectivity and non-human primate latency has the greatest impact on the variation of cumulative disease incidence of yellow fever virus across patches. Author ContributionsConceptualization: Nate Kornetzke, Helen J. Wearing. Methodology: Nate Kornetzke, Helen J. Wearing. Formal Analysis: Nate Kornetzke. Software: Nate Kornetzke. Visualization: Nate Kornetzke. Writing, Original Draft Preparation: Nate Kornetzke. Writing, Review and Editing: Helen J. Wearing. Supervision: Helen J. Wearing. Author SummaryEmerging infectious diseases are a growing threat to public health. Computational models allow researchers to forecast future disease burdens and to investigate counterfactual scenarios, and often, these models are stochastic, i.e. contain randomness, to capture the qualitative behavior of emergence. A type of statistical analysis known as global sensitivity analysis allows modelers to rigorously analyze how varying the input to a model affects variation in its outputs. This type of analysis helps us infer what mechanisms are important for disease mitigation and control, especially when an infectious disease has a complex ecology consisting of multiple hosts and vectors. Until recently, stochastic models of emerging infectious diseases often proved too computationally expensive to perform a global sensitivity analysis on. Here, we demonstrate how new mathematical tools allow us to streamline this process for complex models of emerging infectious diseases. We analyze the ecology of re-emerging yellow fever in South America, a public health threat occurring in many hosts and vectors across vastly different ecosystems.

ecology↗

bioRxiv2021

How do I bite thee? Let me count the ways: Heterogeneity and temperature dependence in Ae. aegypti biting habits drive individual mosquito arbovirus transmission potential

In models of mosquito-borne transmission, the mosquito biting rate is an influential parameter, and understanding the heterogeneity of the process of biting is important, as biting is usually assumed to be relatively homogeneous across individuals, with time-between-bites described by an exponentially distributed process. However, these assumptions have not been addressed through laboratory experimentation. We experimentally investigated the daily biting habits of Ae. aegypti at three temperatures (24{degrees}C, 28{degrees}C, and 32{degrees}C) and determined that there was individual heterogeneity in biting habits (number of bites, timing of bites, etc.). We further explored the consequences of biting heterogeneity using an individual-based model designed to examine whether a particular biting profile determines whether a mosquito is more or less likely to 1) become exposed given a single index case of dengue (DENV) and 2) transmit to a susceptible human individual. Our experimental results indicate that there is heterogeneity among individuals and among temperature treatments. We further show that this results in altered probabilities of transmission of DENV to and from individual mosquitoes based on biting profiles. While current model representation of biting may work under some conditions, it might not uniformly be the best fit for this process. Our data also confirm that biting is a non-monotonic process with temperatures around 28{degrees}C being optimum. Author SummaryMosquito biting is a necessary and critical part of arbovirus transmission. The mosquito must bite once to acquire a virus and again to transmit, and these two bites must be separated by sufficient time for the virus to get to the salivary glands of the mosquito. Thus, both the number and timing of bites is important. We experimentally investigated how these bite characteristics might be different among individuals and further explored how temperature affected the overall heterogeneity of biting in Aedes aegypti mosquitoes, which carry many arboviruses like dengue virus (DENV). We found that the biting profiles - including number and timing thereof - did vary within temperature groups among individuals and compared outcomes associated with each individual in an individual based model of household DENV transmission. Our results further confirmed that temperatures around 28{degrees}C are optimal for mosquito biting (and transmission), that correlations between biting characteristics and transmission were not uniform across temperature, which represents another layer of heterogeneity, and that - at least at 28{degrees}C - the null assumption of an exponential or an exponential like (geometric) distribution of biting in mathematical models of transmission is not the best and offer an alternative.

ecology↗