arXiv subjects

Christopher J. Banks

Publications and source records attributed to Christopher J. Banks.

7 recordsLinked to original sources

arXiv2025

Assessing the potential impact of environmental land management schemes on emergent infection disease risks

Financial incentives encourage the plantation of new woodland to increase habitat, biodiversity, carbon sequestration, as a contribution to meeting climate change and biodiversity conservation targets. Whilst these are largely positive effects, it is worth considering that this expansion of woodland can lead to increased presence of wildlife species in proximity to agricultural holdings that may pose an enhanced risk of disease transmission between wildlife and livestock. Wildlife and the provision of a reservoir for infectious disease is particularly important in the transmission dynamics of bovine tuberculosis, the case studied here. In this paper we develop an economic model for predicting changes in land use resulting from subsidies for woodland planting. We use this to assess the consequent impact on wild deer populations in the newly created woodland areas, and thus the emergent infectious disease risk arising from the proximity of new and existing wild deer populations and existing cattle holdings. We consider an area in the South-West of Scotland, having existing woodland, deer populations, and extensive and diverse cattle farm holdings. In this area we find that, with a varying level of subsidy and plausible new woodland creation scenarios, the contact risk between areas of wild deer and cattle increases between 26% and 35% over the risk present with a zero subsidy. This provides a foundation for extending to larger regions and for examining potential risk mitigation strategies, for example the targeting of subsidy in low disease risk areas, or provisioning for buffer zones between woodland and agricultural holdings.

q-bio.PE↗

arXiv2025

Machine learning augmented diagnostic testing to identify sources of variability in test performance

Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. We use machine learning to assess the surrounding risk landscape under which a diagnostic test is applied to augment its interpretation. We develop this to predict the occurrence of bovine tuberculosis incidents in cattle herds, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected improves by over 5 percentage points, or 240 additional infected herds detected in one year beyond those detected by the skin test alone. We also use feature importance testing for assessing the weighting of risk factors. While many factors are associated with increased risk of incidents, of note are several factors that suggest that in some herds there is a higher risk of infection going undetected.

cs.LG↗

arXiv2024

Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant. Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak's trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.

q-bio.PE↗

arXiv2022

Bovine Tuberculosis in Britain: identifying signatures of polarisation and controversy on Twitter

Approaches to disease control are influenced by and reflected in public opinion, and the two are intrinsically entwined. Bovine tuberculosis (bTB) in British cattle and badgers is one example where there is a high degree of polarisation in opinion. Bovine viral diarrhoea (BVD), on the other hand, does not have the same controversy. In this paper we examine how language subjectivity on Twitter differs when comparing the discourses surrounding bTB and BVD, using a combination of network analysis and language and sentiment analysis. That data used for this study was collected from the Twitter public API over a two-year period. We investigated the network structure, language content, and user profiles of tweets featuring both diseases. While analysing network structure showed little difference between the two disease topics, elements of the structure allowed us to better investigate the language structure and profile of users. We found distinct differences between the language and sentiment used in tweets about each disease, and in the profile of the users who were doing the tweeting. We hope that this will guide further investigation and potential avenues for surveillance or the control of misinformation.

cs.SI↗

arXiv2017

Verification of a lazy cache coherence protocol against a weak memory model

In this paper we verify a modern lazy cache coherence protocol, TSO-CC, against the memory consistency model it was designed for, TSO. We achieve this by first showing a weak simulation relation between TSO-CC (with a fixed number of processors) and a novel finite-state operational model which exhibits the laziness of TSO-CC and satisfies TSO. We then extend this by an existing parameterisation technique, allowing verification for an unlimited number of processors. The approach is executed entirely within a model checker, no external tool is required and very little in-depth knowledge of formal verification methods is required of the verifier.

cs.LO↗

arXiv2017

A More Sensitive Context

Logic of Behaviour in Context (LBC) is a spatio-temporal logic for expressing properties of continuous-state processes, such as biochemical reaction networks. LBC builds on the existing Metric Interval Temporal Logic (MITL) and adds a "context modality" that explores the behaviour of a system when composed with an external process. LBC models are terms of the Continuous π-Calculus (cπ), a process algebra with continuous state space. Our previously published LBC model-checking technique required examining many points along the behavioural trajectory of a process; and potentially computing further trajectories branching off at every such point. This raised two difficulties: mixing temporal and spatial modalities could require computing a large number of trajectories, with costly numerical solution of differential equations; and might still fail to check intermediate values between discrete points on those trajectories. In this paper we make progress against both of these problems using techniques from signal temporal logic and from sensitivity analysis. Boolean signals aggressively compress trace information, allowing more efficient computation; and sensitivity analysis lets us reliably check formulae over a region by calculating a smaller number of sample trajectories.

cs.LO↗

arXiv2015

Functional transcription factor target discovery via compendia of binding and expression profiles

Genome-wide experiments to map the DNA-binding locations of transcription-associated factors (TFs) have shown that the number of genes bound by a TF far exceeds the number of possible direct target genes. Distinguishing functional from non-functional binding is therefore a major challenge in the study of transcriptional regulation. We hypothesized that functional targets can be discovered by correlating binding and expression profiles across multiple experimental conditions. To test this hypothesis, we obtained ChIP-seq and RNA-seq data from matching cell types from the human ENCODE resource, considered promoter-proximal and distal cumulative regulatory models to map binding sites to genes, and used a combination of linear and non-linear measures to correlate binding and expression data. We found that a high degree of correlation between a gene's TF-binding and expression profiles was significantly more predictive of the gene being differentially expressed upon knockdown of that TF, compared to using binding sites in the cell type of interest only. Remarkably, TF targets predicted from correlation across a compendium of cell types were also predictive of functional targets in other cell types. Finally, correlation across a time course of ChIP-seq and RNA-seq experiments was also predictive of functional TF targets in that tissue.

q-bio.GN↗