arXiv subjects

Rowland R. Kao

Publications and source records attributed to Rowland R. Kao.

5 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↗

arXiv2020

Spontaneous divergence of disease status in an economic epidemiological game

We introduce a game inspired by the challenges of disease management in livestock farming and the transmission of endemic disease through a trade network. Success in this game comes from balancing the cost of buying new stock with the risk that it will be carrying some disease. When players follow a simple memory-based strategy we observe a spontaneous separation into two groups corresponding to players with relatively high, or low, levels of infection. By modelling the dynamics of both the disease and the formation and breaking of trade relationships, we derive the conditions for which this separation occurs as a function of the transmission rate and the threshold level of acceptable disease for each player. When interactions in the game are restricted to players that neighbour each other in a small-world network, players tend to have similar levels of infection as their neighbours. We conclude that success in economic-epidemiological systems can originate from misfortune and geographical circumstances as well as by innate differences in personal attitudes towards risk.

physics.soc-ph↗