Biology subjects

Donnelly, C. A.

Publications and source records attributed to Donnelly, C. A..

10 recordsLinked to original sources

bioRxiv2026

A Bayesian modelling framework for inference of latent infection risk patterns from virus neutralisation assay titration data

Serological assays remain the standard approach for estimating the cumulative incidence of a pathogen and monitoring population immunity. Titration data from virus neutralisation assays are conventionally analysed using a nearly century-old interpolation-based method that neglects inherent imperfections in the assay and produces estimates with no measure of uncertainty. We introduce a two-part Bayesian framework for inferring latent infection risk directly from raw titration data. First, we develop a mechanistic model for serum antibody titration data that estimates antibody concentrations while quantifying uncertainty. Second, we propagate this uncertainty into an age-structured serocatalytic mixture model by integrating over posterior draws of individual antibody concentrations, allowing joint inference on latent serostate membership, force of infection, and serological waning rate. We applied the framework to three cross-sectional serosurveys of the population of England, for enteroviruses A71 (EV-A71) and D68 (EV-D68) and coxsackievirus A6 (CVA6), which are leading causes of severe respiratory illness and hand, foot, and mouth disease. We estimated consistently higher and more persistent lifetime antibody concentrations for EV-D68 than for EV-A71 and CVA6. The proportion of recently infected individuals peaks around 40% by age 5 years for CVA6 and around 32% by age 7 years for EV-A71, before declining with age. These estimates are conditional on a model structure excluding complete seroreversion, which, where identifiable, implied a half-life of several decades. For EV-D68, the inferred proportion previously infected exceeded 50% by age 13 years and grew, with the force of infection declining more gradually with age. These estimates differ from those previously obtained from binarised versions of the same data. Our framework uncovers the wide-ranging variation in antibody levels that are often obscured by conventional endpoint titre methods and infers infection rates directly from raw titration data, without dichotomising at predetermined seropositivity cut-offs, while making minimal assumptions about virus-specific infection mechanisms.

bioinformatics↗

bioRxiv2024

Deep learning from videography as a tool for measuring infection in poultry

Poultry farming is threatened by regular outbreaks of Escherichia coli (E. coli) that lead to significant economic losses and public health risks. However, traditional surveillance methods often lack sensitivity and scalability. Early detection of infected poultry using minimally invasive procedures is thus essential for preventing epidemics. To that end, we leverage recent advancements in computer vision, employing deep learning-based tracking to detect behavioural changes associated with E. coli infection in a case-control trial comprising two groups of 20 broiler chickens: (1) a healthy control group and (2) a group infected with a pathogenic E. coli field strain from the poultry industry. More specifically, kinematic features derived from deep learning-based tracking data revealed markedly reduced activity in the challenged group compared to the negative control. These findings were validated by lower mean optical flow in the infected flock, suggesting reduced movement and activity, and post-mortem physiological markers of inflammation which confirmed the severity of infection in the challenged group. Overall, this study demonstrates that deep learning-based tracking offers a promising solution for real-time monitoring and early infection detection in poultry farming, with the potential to help reduce economic losses and mitigate public health risks associated with infectious disease outbreaks in poultry.

animal behavior and cognition↗

bioRxiv2024

Rapid spread of African Swine Fever across Borneo

African Swine Fever (ASF) reached the island of Borneo at the end of 2020. The first mortalities occurred in wild bearded pigs (Sus barbatus) in Sabah, north-east Borneo. The virus then began to spread across the island but, confounded by COVID 19 lockdowns the spread was difficult to monitor on the ground. The Babi Hutan Project was launched in April 2021 to gather data on pig sightings using citizen science. Here we bring together the data from this project and other online sources to show how the virus encompassed almost the entire island within a one-year period. The speed of spread appears to increase with time following an exponential model: we estimate an average speed of spread of 0.89km/day after 100 days since the first observation and at 4.28km/day after 400 days. We include recommendations of next steps for the bearded pigs of Borneo.

ecology↗

bioRxiv2024

Bayesian inference of phylogenetic distances: revisiting the eigenvalue approach

Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more unusual biological phenomena such as variations in lineage-specific evolutionary rates over time (heterotachy), more complex approaches uch as the GTR with rate variation (GTR+G) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis. In this paper, we derive a hybrid approach between these two methods, incorporating G(, )-distributed rate variation and heterotachy into a hierarchical Bayesian GTR-style framework. Our approach is differentiable and amenable to both stochastic gradient descent for optimisation and Hamiltonian Markov chain Monte Carlo for Bayesian inference. We show the utility of our approach by studying hypotheses regarding the origins of the eukaryotic cell within the context of a universal tree of life and find evidence for a two-domain theory.

evolutionary biology↗

bioRxiv2023

Madariaga and Venezuelan equine encephalitis virus seroprevalence in rodent enzootic hosts in Eastern and Western Panama

While rodents are primary reservoirs of Venezuelan equine encephalitis virus (VEEV), their role in Madariaga virus (MADV) transmission remains uncertain, particularly given their overlapping geographic distribution. This study explores the interplay of alphavirus prevalence, rodent diversity, and land use within Darien and Western Panama provinces. A total of three locations were selected for rodent sampling in Darien province: Los Pavitos, El Real de Santa Maria and Santa Librada. Two sites were selected in Western Panama province: El Cacao and Ciri Grande. We used plaque reduction neutralization tests to assess MADV and VEEV seroprevalences in 599 rodents of 16 species across five study sites. MADV seroprevalence was observed at higher rates in Los Pavitos (Darien province), 9.0%, 95% CI: 3.6-17.6, while VEEV seroprevalence was elevated in El Cacao (Western Panama province), 27.3%, 95% CI: 16.1-40.9, and El Real de Santa Maria (Darien province), 20.4%, 95% CI: 12.6-29.7. Species like Oryzomys coesi, 23.1%, 95% CI: 5.0-53.8, and Transandinomys bolivaris, 20.0%, 95% CI: 0.5-71.6 displayed higher MADV seroprevalences than other species, whereas Transandinomys bolivaris, 80.0%, 95% CI: 28.3-99.4, and Proechimys semispinosus, 27.3%, 95% CI: 17.0-39.6, exhibited higher VEEV seroprevalences. Our findings provide support to the notion that rodents are vertebrate reservoirs of MADV and reveal spatial variations in alphavirus seropositivity among rodent species, with different provinces exhibiting distinct rates for MADV and VEEV. Moreover, specific rodent species are linked to unique seroprevalence patterns for these viruses, suggesting that rodent diversity and environmental conditions might play a significant role in shaping alphavirus distribution.

microbiology↗

bioRxiv2023

Sherlock - A flexible, low-resource tool for processing camera-trapping images

1O_LIThe use of camera traps to study wildlife has increased markedly in the last two decades. Camera surveys typically produce large datasets which require processing to isolate images containing the species of interest. This is time consuming and costly, particularly if there are many empty images that can result from false triggers. Computer vision technology can assist with data processing, but existing artificial intelligence algorithms are limited by the requirement of a training dataset, which itself can be challenging to acquire. Furthermore, deep-learning methods often require powerful hardware and proficient coding skills. C_LIO_LIWe present Sherlock, a novel algorithm that can reduce the time required to process camera trap data by removing a large number of unwanted images. The code is adaptable, simple to use and requires minimal processing power. C_LIO_LIWe tested Sherlock on 240 596 camera trap images collected from 46 cameras placed in a range of habitats on farms in Cornwall, UK, and set the parameters to find European badgers (Meles meles). The algorithm correctly classified 91.9% of badger images and removed 49.3% of the unwanted empty images. When testing model parameters, we found that faster processing times were achieved by reducing both the number of sampled pixels and bouncing attempts (the number of paths explored to identify a disturbance), with minimal implications for model sensitivity and specificity. When Sherlock was tested on two sites which contained no livestock in their images, its performance greatly improved and it removed 92.3% of the empty images. C_LIO_LIThough further refinements may improve its performance, Sherlock is currently an accessible, simple and useful tool for processing camera trap data. C_LI

ecology↗

bioRxiv2020

Endemic and epidemic human alphavirus infections in Eastern Panama, An Analysis of Population-Based Cross-Sectional Surveys

BackgroundMadariaga virus (MADV), has recently been associated with severe human disease in Panama, where the closely related Venezuelan equine encephalitis virus (VEEV) also circulates. In June, 2017, a fatal MADV infection was confirmed in a community of Darien province. MethodsWe conducted a cross-sectional outbreak investigation with human and mosquito collections in July 2017, where sera were tested for alphavirus antibodies and viral RNA. Additionally, by applying a catalytic, force-of-infection statistical model to two serosurveys from Darien province in 2012 and 2017, we investigated whether endemic or epidemic alphavirus transmission occurred historically. ResultsIn 2017, MADV and VEEV IgM seroprevalence was 1.6% and 4.4%, respectively; IgG antibody prevalences were MADV: 13.2%; VEEV: 16.8%; Una virus (UNAV): 16.0%; and Mayaro virus (MAYV): 1.1%. Active viral circulation was not detected. Evidence of MADV and UNAV infection was found near households -- raising questions about its vectors and enzootic transmission cycles. Insomnia was associated with MADV and VEEV infection, depression symptoms were associated with MADV, and dizziness with VEEV and UNAV. Force-of-infection analyses suggest endemic alphavirus transmission historically, with recent increased human exposure to MADV and VEEV in some regions. ConclusionsThe lack of additional neurological cases suggest that severe MADV and VEEV infections occur only rarely. Our results indicate that, over the past five decades, alphavirus infections have occurred at low levels in eastern Panama, but that MADV and VEEV infections have recently increased -- potentially during the past decade. Endemic infections and outbreaks of MADV and VEEV appear to differ spatially. Author summaryPrior to 2010, it was believed that the Madariaga virus (MADV) was primarily associated with equine disease. However, an outbreak reported in Panama, in an endemic area where Venezuelan equine encephalitis virus (VEEV) also circulates, suggested a change in its epidemiological profile. We aimed to reconstruct the epidemiological dynamics of MADV and VEEV, as well as additional alphaviruses known to circulate in the region in order to understand MADV emergence. For this, cross-sectional serosurveys were used to demonstrate that the Alphaviruses MADV, VEEV and Una virus have repeatedly infected humans in eastern Panama over the past five decades. Whilst their historical transmission has been low, we confirm that the transmission has recently increased for both MADV and VEEV.

microbiology↗

bioRxiv2019

Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation

The effective reproduction number, Rt, is an important prognostic for infectious disease epidemics. Significant changes in Rt can forewarn about new transmissions or predict the efficacy of interventions. The renewal model infers Rt from incidence data and has been applied to Ebola virus disease and pandemic influenza outbreaks, among others. This model estimates Rt using a sliding window of length k. While this facilitates real-time detection of statistically significant Rt fluctuations, inference is highly k -sensitive. Models with too large or small k might ignore meaningful changes or over-interpret noise-induced ones. No principled k -selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory. We derive exact incidence prediction distributions and integrate these within an APE framework to identify the k best supported by available data. We find that this k optimises short-term prediction accuracy and expose how common, heuristic k -choices, which seem sensible, could be misleading.

bioinformatics↗

bioRxiv2019

Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.

bioinformatics↗

bioRxiv2017

International risk of yellow fever spread from the ongoing outbreak in Brazil

The largest Yellow Fever (YF) outbreak in a decade in Latin America is underway in the Southeast of Brazil. In this article we provide a quantitative assessment of the risk of travel-related international spread of YF. We argue that mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance in the southern United States, Latin America (especially Argentina, Chile and Uruguay) and Europe (especially Portugal, Spain, Italy and Germany).

epidemiology↗