Posters

The unprecedented mpox outbreak in non-endemic regions during 2022-2023, which has seen a recent resurgence in late 2023-2024, poses a significant public health threat. Despite its global spread, the viral dynamics of mpox infection and the specific characteristics driving these outbreaks remain insufficiently explored. We develop mathematical models to examine the interactions between host immune responses and the virus across three distinct infection routes (intravenous, intradermal, and intrarectal). The models are calibrated using viral load data from macaques infected through each of these three infection routes. Subsequently, we calculate the infectiousness of each infected macaque, finding that the proportion of presymptomatic infectiousness is highest in those infected via sexual contact, followed by skin-to-skin contact. These observations demonstrate that close contact during sexual activity is a significant route of viral transmission, with presymptomatic spread playing a crucial role in the 2022-2023 multi-country outbreak and potentially also in the 2023-2024 multi-source outbreak. Leveraging model predictions and infectiousness data, we assess the impact of antiviral drugs on interventions against mpox infection. Model simulations suggest that early administration of antiviral drugs can reduce peak viral loads, even in individuals with compromised immunity, particularly in cases of infection through skin-to-skin and sexual contact. These results underscore the importance of initiating antiviral treatment as early as possible for mpox-infected patients with compromised immune systems, such as those who are HIV-positive.

Reassortment is the exchange of genomic segments between viruses. It is a significant feature of the evolution of influenza A viruses (IAV), where it can yield new combinations between highly divergent lineages (antigenic shift). In this study, we evaluated the reliability of standard comparative methods for reconstructing reassortment events in the evolutionary history of H5Nx IAV genomes. We retrieved a total of 11,765 complete H5Nx genome sequences from NCBI GenBank and GISAID. These data were screened for duplicate records, and segment-specific alignments were generated with MAFFT. We used IQ-TREE to reconstruct a maximum-likelihood (ML) tree for the HA alignment and progressively removed terminals with branch lengths shorter than 0.05. Using this reduced set of divergent genomes, we reconstructed ML trees for each segment.

Separately, we generated five random subsets from the full dataset to examine phylogenetic discordance across independent samples. For each subset, we reconstructed ML trees for all genomic segments and quantified topological incongruence using maximum agreement forests, counting subtree-prune-regraft (SPR) events between the HA tree and each segment tree.

To test whether these signals could arise in the absence of reassortment, we simulated sequence evolution along the HA tree using Pyvolve, matching observed levels of divergence per segment. Surprisingly, real datasets exhibited higher SPR distances than simulated ones, and also showed a greater number of small subtrees often reduced to a single branch and shorter sibling distances between matched nodes. These patterns were not present in the simulated data and are more consistent with reconstruction errors than with true reassortment events.

These results imply that there are no simple quantitative criteria that can reliably identify true reassortment events from phylogenetic discordance alone, and suggest that previous studies employing similar tree-based methods may have overestimated the extent of reassortment in IAV genomes.

This poster will present some preliminary results from a structured literature review of statistical methods that are used for handling scale in species distribution models; an aim will be to summarize the methods used to handle scale, and distance (both spatial and phylogenetic). Spatial distances can be challenging to deal with in species distribution models. A simple way to account for influence at a distance is to use nearest distance to a landscape feature as a predictor variable; a potential downside to this approach is that it ignores potentially valuable information regarding the local density of the feature. A second approach could be to quantify total feature density within a fixed radius; a drawback of this approach is needing to estimate that radius. A third approach might be to rasterize data at an appropriate scale; this leaves the challenge of choosing that scale. Given the potential of multiscale approaches to statistical inference, and a growing interest in metacommunity approaches that incorporate phylogeny and species traits, it would be valuable to obtain an overview of the relative frequency with which various methods are currently in use.

In this work, we propose a coupled system of disease transmission dynamics and a behavioural renewal equation to explain nonlinear oscillations post the acute phase of a pandemic. This extends the Zhang-Scarabel-Murty-Wu model, which captured multi-wave patterns during the early acute phase of the COVID-19 pandemic. Our study explores how susceptible depletion affects the coupled dynamics of disease spread and behaviour. Using risk aversion functions and delayed adaptation, we also show how these factors contribute to sustained oscillatory patterns.

Authors: Cecily Costain ^1,2, Yichao Shi ¹, Guoqi Wen ¹, Baoluo Ma ¹, Weikai Yan ¹, Wubishet Bekele, Wen Chen ^1,2*

Affiliations:

¹ Ottawa Research & Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada

² Department of Biology, University of Ottawa, Ottawa, Canada

Correspondence: wen.chen@agr.gc.ca

Resolving drought stress in crops is of the utmost urgency as it stands as the most significant climate-related agricultural threat, projected to cause > 50% yield reduction in major crops by 2050 at a global scale. Canadian agricultural regions, particularly the Prairies, are highly vulnerable to increasingly frequent and severe drought events, highlighting the pressing need for strategies to improve crop resilience. While recent studies suggest the plant microbiome plays a critical role in stress adaptation, mechanisms governing drought-driven microbiome evolution—recruitment, assembly, and functional contributions—with plant physiology and genetics remain poorly understood. This study selects oat as a model crop for its natural drought tolerance and recovery, including wild oat accessions for their higher resistance to drought, salinity, and diseases. A multi-omics approach is employed to investigate the evolutionary dynamics of the oat holobiont under drought stress, integrating physiological phenotyping, microbiome profiling, and plant growth-promoting bacteria (PGPB) isolation and characterization. Wild and modern oat genotypes are phenotyped for drought tolerance using Genotype by Yield * Trait (GYT) analysis, capturing physiological, morphological, and anatomical adaptations. Drought-induced shifts in rhizosphere and endosphere microbiomes are characterized using metagenomics approaches, assessing community composition, functional gene content, and microbial resilience. To further dissect microbial contributions, PGPB strains are isolated and functionally characterized for their roles in stress mitigation. By elucidating the microbiome's evolutionary response to drought and its interaction with plant physiology and genetics, this study will contribute to sustainable crop improvement strategies, leveraging microbiome engineering and host genetic selection to enhance resilience under climate stress.

Abstract to follow.

Scalable computing is no longer limited to centralized HPC centers. The Distributive Compute Protocol (DCP) transforms idle machines across university campuses into massive, parallel computing clusters—available at no cost to researchers. At institutions like Sheridan College and Queen's University, over 1,200 CPU cores are already pooled from lab computers using DCP. Python-based jobs—from Monte Carlo simulations to large-scale parameter sweeps—can be easily distributed across these campus-scale clusters and, if needed, burst onto DCP's planet-wide network.

In this session, you'll learn how to run your own embarrassingly parallel workloads using Python and DCP, without changing your code structure or relying on traditional HPC queues. We'll demonstrate a live run of MetaCast (by Martin Grunnill) across devices in the audience and on the campus cluster. Discover how DCP makes distributed computation accessible, scalable, and flexible—whether you're working on numerical simulations, sensitivity analyses, or large experimental pipelines.

Mosquito-borne diseases present a significant public health challenge, with effective prevention requiring accurate forecasting of mosquito populations and associated disease prevalence. West Nile Virus (WNV) spread rapidly across the United States, primarily due to its transmission through highly mobile, migratory birds. Climate change is expected to alter the large-scale distribution of mosquito populations and disease patterns, making predictive modeling increasingly essential due to its complexity. According to data from the Centers for Disease Control and Prevention (CDC), Maricopa County, Arizona, is among the top U.S. counties with a high WNV disease burden. Using a 10-year time series (2014-2024) of weekly mosquito abundance and WNV prevalence data collected in Maricopa County, we investigated how local climate variations influence mosquito population dynamics and disease transmission. We developed a mechanistic model of WNV dynamics driven by daily temperature and 30-day accumulated precipitation to predict mosquito populations and WNV prevalence. Our statistical forecasting framework leveraged climate factors to improve predictive accuracy by integrating adaptive modeling techniques and Bayesian methods to infer precise model parameters. A key enhancement addressed limitations observed in prior models, particularly the inability to capture spring season dynamics in mosquito populations. To achieve this, we applied the Ensemble Kalman Filter (EnKF) method to estimate both time-varying parameters (baseline population growth rate and mosquito bite rate) and static parameters. Using generalized additive models (GAMs), we forecasted these time-varying parameters on a two-week basis, incorporating precipitation and temperature as covariates. These forecasts were used as inputs for a mechanistic ordinary differential equation (ODE) model, which predicted mosquito abundance and WNV prevalence while capturing associated uncertainties. Our iterative framework was applied over a 52-week period, successfully capturing seasonal variations in mosquito populations and WNV prevalence throughout the decade-long dataset. Compared to traditional Markov Chain Monte Carlo (MCMC) approaches, the EnKF demonstrated superior performance in fitting mosquito abundance and WNV prevalence data. Despite changes in environmental conditions, data quality, and underlying dynamics over the 10-year period, our method consistently captured key trends and variations, highlighting its robustness and adaptability. This enhanced methodology provides actionable insights for public health decision-makers, supporting resource allocation and improving mosquito-borne disease prevention outcomes. Our findings underscore the importance of integrating climate data with adaptive filtering techniques to address forecasting challenges and enable effective responses to emerging or re-emerging mosquito-borne pathogens, which, driven by human behavior and environmental changes, may potentially escalate to pandemic levels.

Edge-based random network models, especially those based on bond percolation methods, can be used to model disease transmission on complex networks and accommodate social heterogeneity while keeping tractability. Here we present an application of an edge-based network model to the spread of syphilis in the Kingston, Frontenac and Lennox & Addington (KFL&A) region of Southeastern Ontario, Canada. We compared the results of using a network-based susceptible-infectious-recovered (SIR) model to those generated from using a traditional mass action SIR model. We found that the network model yields very different predictions, including a much lower estimate of the final epidemic size. We also used the network model to estimate the potential impact of introducing a rapid syphilis point of care test (POCT) and treatment intervention strategy that has recently been implemented by the public health unit to mitigate syphilis transmission.