Biology subjects

Marion, G.

Publications and source records attributed to Marion, G..

5 recordsLinked to original sources

bioRxiv2024

iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown

We introduce the inference for populations at risk (iPAR) framework which enables modelling and estimation of spatial disease dynamics in scenarios where the population at risk is unknown or poorly quantified. This framework addresses a gap in spatial infectious disease modelling approaches, with current methods typically requiring knowledge of the spatial distribution of the population at risk or making simplifying assumptions that are not supported in many real-world outbreaks where only case-report data are available. The principles for iPAR are demonstrated in the context of a susceptible-infected disease dynamics model coupled with Bayesian inference implemented via data-augmentation Markov chain Monte Carlo (MCMC). This implementation of iPAR is tested for a range of scenarios using simulated outbreak data. Results indicate that the method can effectively estimate key properties of disease spread from spatio-temporal case reports and make useful predictions of future spread. The method is then applied to a case study exploring the 2015-2019 Estonian outbreak of African Swine Fever (ASF) in wild boar. Estimates of epidemiological parameters reveal evidence for long distance transmission, as well as disease control via reduction of the wild boar population in Estonia.

ecology↗

bioRxiv2024

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the "who-infected-whom" identification problem. We study samples from hosts (130 cattle, 111 badgers) with confirmed infection of M. bovis (causing bovine Tuberculosis), which has an estimated clock rate as slow as [~]0.1-1 variations per year. For each potential pathway between hosts, we calculate the relative likelihood that such a transmission event occurred. This is informed by an epidemiological model of transmission, and host life history data. By including WGS data, we shrink the number of plausible pathways significantly, relative to those deemed likely on the basis of life history data alone. Despite our uncertainty relating to the evolution of M. bovis, the WGS data are therefore a valuable adjunct to epidemiological investigations, especially for wildlife species whose life history data are sparse.

ecology↗

bioRxiv2022

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BackgroundInfectious disease spread in populations is controlled by individuals susceptibility (propensity to acquire infection), infectivity (propensity to pass on infection to others) and recoverability (propensity to recover/die). Estimating the effects of genetic risk factors on these host epidemiological traits can help reduce disease spread through genetic control strategies. However, the effects of previously identified disease resistance SNPs on these epidemiological traits are usually unknown. Recent advances in computational statistics make it now possible to estimate the effects of single nucleotide polymorphisms (SNPs) on these traits from longitudinal epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known how to optimally design disease transmission experiments or field studies to maximise the precision at which pleiotropic SNP effects estimates for susceptibility, infectivity and recoverability can be estimated. ResultsWe develop and validate analytical expressions for the precision of SNP effects estimates on the three host traits assuming a disease transmission experiment with one or more non-interacting contact groups. Maximising these leads to three distinct experimental designs, each specifying a different set of ideal SNP genotype compositions across groups: a) appropriate for a single contact-group, b) a multi-group design termed "pure", and c) a multi-group design termed "mixed", where pure and mixed refer to contact groups consisting of individuals with the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than infectivity. Data from multiple groups were found more informative about infectivity effects than from a single group containing the same number of individuals. Whilst the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferable because it uses information from naturally occurring infections rather than those artificially induced. The same optimal design principles apply to estimating other categorical fixed effects, such as vaccinations status, helping to more effectively quantify their epidemiological impact. An online software tool SIRE-PC has been developed which calculates the precision of estimated substitution and dominance effects of a single SNP (or vaccine status) associated with all three traits depending on experimental design parameters. ConclusionsThe developed methodology and software tool can be used to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables underlying host susceptibility, infectivity and recoverability.

genetics↗

bioRxiv2020

Reducing ecological complexity using the archetype approach - an application to natural pest control

Natural control of crop pests has the potential to complement or replace intensive agricultural practices, but its mainstream application requires reliable predictions in diverse socioecological settings. In lack of a widely accepted model of natural pest control, we review existing modelling approaches and critically examine their potential to provide understanding and predictions across agricultural landscapes. Models that explicitly represent the underlying mechanisms are better positioned to represent the diversity and context sensitivity of natural pest control than correlative models. Such mechanistic models have used diverse techniques to represent crop-pest-enemy combinations at various spatiotemporal scales. However, certain regions of the world and socioeconomic aspects of natural pest control are underrepresented, while modelling approaches are restricted by a fundamental trade-off between generality and realism. We propose that modelling natural pest control across agroecosystems requires a framework of context-specific generalizations, based on empirical evidence and theoretical expectations. Reviewed models of natural pest control indicate potential attributes of such a general predictive framework.

ecology↗

bioRxiv2019

Estimating genetic and non-genetic effects for host susceptibility, infectivity and recoverability using temporal epidemic data

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for "Susceptibility, Infectivity and Recoverability Estimation"), which allows simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease status measurements. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.

epidemiology↗