arXiv subjects

Stefan Widgren

Publications and source records attributed to Stefan Widgren.

4 recordsLinked to original sources

arXiv2026

Bayesian modelling of herd-level infection dynamics in cattle: Local spread as the primary driver of Salmonella Dublin persistence on \"Oland

Salmonella Dublin (S. Dublin), a zoonotic serotype adapted to cattle, causes animal welfare issues and economic losses. The disease has proven particularly challenging to control in \"Oland, Sweden. This study uses Bayesian simulation-based inference of bulk tank milk sample results to analyse the S. Dublin infection dynamics in \"Oland cattle. The infection process was formulated as a dynamic state-space model and particle Markov-chain Monte Carlo methods were applied to infer the underlying infection dynamics and estimate the basic reproduction number ($R_0$) as well as the effective reproduction number ($R_t$). These metrics provide insight into transmission dynamics, enabling assessment of the effectiveness of the current S. Dublin control in Swedish cattle and identification of interventions that may reduce the prevalence. The results show that most holdings on \"Oland have $R_0 < 1$, indicating that infection is expected to die out after introduction. However, in a subset of holdings $R_0 > 1$, and there the risk for spread of S. Dublin is higher. Furthermore, the analysis reveals that on average, $R_t \approx 1$, suggesting a stable endemic presence unless effective interventions are implemented. In addition, the results show that it is insufficient to restrict the movements of infected cattle on \"Oland to bring $R_t < 1$, as local spread and within-herd transmission contribute equally to the force of infection (approximately 50% each). These findings demonstrate how Bayesian data-driven analysis can support evidence-based decision making for the control and eradication of S. Dublin in cattle.

q-bio.PE↗

arXiv2020

Bayesian epidemiological modeling over high-resolution network data

Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale quantitative disease spread models can nowadays be formulated. Such models have a great potential, e.g., in risk assessments in public health. Their main challenge is model parameterization given surveillance data, a problem which often limits their practical usage. We offer a solution to this problem by developing a Bayesian methodology suitable to epidemiological models driven by network data. The greatest difficulty in obtaining a concentrated parameter posterior is the quality of surveillance data; disease measurements are often scarce and carry little information about the parameters. The often overlooked problem of the model's identifiability therefore needs to be addressed, and we do so using a hierarchy of increasingly realistic known truth experiments. Our proposed Bayesian approach performs convincingly across all our synthetic tests. From pathogen measurements of shiga toxin-producing Escherichia coli O157 in Swedish cattle, we are able to produce an accurate statistical model of first-principles confronted with data. Within this model we explore the potential of a Bayesian public health framework by assessing the efficiency of disease detection and -intervention scenarios.

stat.ME↗

arXiv2018

SimInf: An R package for Data-driven Stochastic Disease Spread Simulations

We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and OpenMP to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goal was to make SimInf extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. In this paper, we provide a technical description of the framework and demonstrate its use on some basic examples. We also discuss how to specify and extend the framework with user-defined models.

q-bio.PE↗

arXiv2016

Fast event-based epidemiological simulations on national scales

We present a computational modeling framework for data-driven simulations and analysis of infectious disease spread in large populations. For the purpose of efficient simulations, we devise a parallel solution algorithm targeting multi-socket shared memory architectures. The model integrates infectious dynamics as continuous-time Markov chains and available data such as animal movements or aging are incorporated as externally defined events. To bring out parallelism and accelerate the computations, we decompose the spatial domain and optimize cross-boundary communication using dependency-aware task scheduling. Using registered livestock data at a high spatio-temporal resolution, we demonstrate that our approach not only is resilient to varying model configurations, but also scales on all physical cores at realistic work loads. Finally, we show that these very features enable the solution of inverse problems on national scales.

q-bio.PE↗