Biology subjects

Thompson, R. N.

Publications and source records attributed to Thompson, R. N..

10 recordsLinked to original sources

bioRxiv2026

A spatial transmission model for evaluating interventions during highly pathogenic avian influenza outbreaks

During infectious disease outbreaks, epidemiological models can be used to project future outbreak dynamics and to explore scenarios under different control measures. Here, we present the spatially explicit avian influenza transmission model that we developed during the 2026 WiLiMan-ID consortium Highly Pathogenic Avian Influenza Modelling Challenge, which involved evaluating control measures (preventive and reactive culling, confinement and restocking bans) during a hypothetical avian influenza outbreak. We show that the control measures that were expected to reduce transmission effectively varied between different outbreak stages. For example, policy-makers could choose whether to focus culling efforts on: i) faster reactive culling and no preventive culling; or ii) slower reactive culling and preventive culling. Relatively early in the outbreak, when the number of available culling teams was limited, our results indicate that the two culling strategies were projected to lead to similar numbers of premises with notified infections over the following 28 days. Later in the outbreak, with more culling teams available, the second strategy was projected to lead to substantially fewer notified premises than the first strategy. In addition, we demonstrate that whether a strategy is beneficial or detrimental depends on policy-makers' objectives. For example, preventive culling may reduce transmission, but at the cost of culling poultry on premises without infections, illustrating a trade-off between limiting transmission and the cost of interventions. Our results highlight the importance of choosing policy objectives carefully and reassessing interventions at different outbreak stages to ensure that control policies remain appropriate for the current epidemiological situation.

ecology↗

bioRxiv2026

End-of-outbreak determination under seasonal transmission

At the apparent end of an infectious disease outbreak, policymakers must decide when to relax interventions, balancing epidemiological safety and economic efficiency. Here, we extend a renewal equation framework for estimating the risk of additional cases to scenarios with seasonal transmission, such as Aedes-borne virus transmission in temperate settings. In simulations, we demonstrate that the time of year is key to when it is safe to relax interventions under seasonal transmission: measures can be relaxed sooner if the last observed case occurs towards the end of the transmission season. We apply our framework to the 2017 chikungunya outbreak in Italy, integrating incidence data with estimates of temperature suitability for transmission, and further extend our approach to account for case under-reporting. Our model incorporating an end-of-season decline in transmission indicates a lower risk of additional cases late in the outbreak than a model based on incidence alone: assuming 60% case reporting, the seasonal model reaches a 1% risk threshold 22 days after the last observed case, compared with 39 days for the incidence-only model. Incorporating seasonality in model-based assessments of transmission risks at the end of an outbreak can therefore support more timely and reliable decision making.

ecology↗

bioRxiv2026

Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks

Demographic shifts are reshaping population age structures worldwide, with implications for infectious disease dynamics. Since contact patterns, susceptibility and infectiousness often vary by age, the risk that pathogen introductions initiate a substantial outbreak depends on the population's age distribution and associated behavioural characteristics. We develop an age-structured mathematical model to estimate the risk that a single pathogen introduction leads to sustained transmission (the probability of a major outbreak) under long-term demographic transitions, incorporating changes in age-specific contact patterns and behavioural adaptation (specifically, extended workforce participation in an ageing society). Using the Republic of Korea (projected to become the world's oldest population by 2050) as a case study, we show that population ageing may reduce the probability of a major outbreak due to older individuals' lower contact rates. However, this effect is attenuated for pathogens with increasing susceptibility or infectiousness with age, and by factors such as later retirement that increase contacts among older individuals. These findings demonstrate that, while outbreak risks are affected by demographic changes, they are further modified by associated behavioural responses, highlighting the importance of accounting for demographic and socio-behavioural context when assessing future infectious disease outbreak risks.

ecology↗

bioRxiv2025

A modelling assessment for the impact of control measures on highly pathogenic avian influenza transmission in poultry in Great Britain

Since 2020, large-scale outbreaks of highly pathogenic avian influenza (HPAI) H5N1 in Great Britain have resulted in substantial poultry mortality and economic losses. Alongside the costs, the risk of circulation leading to a viral reassortment that causes zoonotic spillover raises additional concerns. However, the precise mechanisms driving transmission between poultry premises and the impact of potential control measures in Great Britain, such as vaccination, are not fully understood. We have developed a spatial transmission model for the spread of HPAI in poultry premises calibrated to infected premises data for the 2022-23 season using Markov chain Monte Carlo. Our results indicate that enhanced biosecurity measures and/or vaccination of the premises surrounding an identified infected premises can substantially reduce the overall number of infected premises. Our findings highlight that enhanced control measures could limit the future impact of HPAI on the poultry industry and reduce the risk of broader health threats.

systems biology↗

bioRxiv2025

Unlocking plant health survey data: an approach to quantify the sensitivity and specificity of visual inspections

Invasive plant pests and pathogens cause significant environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) is usually ignored. These parameters facilitate calculation of surveillance metrics which are critical for effective contingency planning and outbreak management. To address this, twenty-three citizen scientist surveyors assessed up to 175 oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the gold-standard. The utility of a workflow utilising Bayesian modelling was then examined using simulated data to estimate these parameters in the absence of a rarely available gold-standard dataset. There was large variation in sensitivity and specificity between surveyors and symptoms, although the sensitivity was positively related to the number of symptoms on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with knowledge of whether a site has higher ([~]0.6) or lower ([~]0.3) true disease prevalence we show that sensitivity and specificity can be estimated without gold-standard data. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management. Author summaryThe increasing occurrence of emerging plant pests and diseases is affecting both agricultural and natural ecosystems. Effective management and control of such pests and diseases is much easier when they are detected early. Currently, visual surveys underpin plant health surveillance, but basic metrics of the reliability of visual detection such as the sensitivity (probability of correctly identifying a positive) and specificity (probability of correctly identifying a negative) are not routinely quantified. In this study, we first quantify the sensitivity and specificity of 23 trained citizen scientist surveyors at detecting three symptoms of acute oak decline, by comparing their symptom classifications against a dataset from an expert who has conducted long-term monitoring of these trees. We demonstrate how individuals vary greatly in their ability to detect symptoms, and how different symptoms are associated with different detection error. Secondly, based on this dataset we outline a workflow developed for scenarios realistic in plant health which utilises Bayesian modelling to estimate these parameters in the absence of a rarely available gold-standard expert dataset. In summary, our results highlight variation in the reliability of visual detection, and we provide a workflow to calculate this and facilitate optimisation of risk-based surveillance strategies in plant health.

ecology↗

bioRxiv2023

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we found that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher infectious periods but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we showed that a decreased incubation period and an increased proportion of asymptomatic infection were also positively selected for as SARS-CoV-2 mutated to the extent that people did not isolate. We demonstrated that the Omicron variants evolved in these ways to adapt to human behavior. The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

evolutionary biology↗

bioRxiv2022

Using "sentinel" plants to improve early detection of invasive plant pathogens

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens in new locations, which requires effective surveillance through the implementation of appropriate monitoring programs. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. Sentinel plants - alternative susceptible host species that display visible symptoms of infection more rapidly - could be introduced to at-risk populations and included in monitoring programs to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

plant biology↗

bioRxiv2020

2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations

The outbreak of pneumonia originating in Wuhan, China, has generated 830 confirmed cases, including 26 deaths, as of 24 January 2020. The virus (2019-nCoV) has spread elsewhere in China and to other countries, including South Korea, Thailand, Japan and USA. Fortunately, there has not yet been evidence of sustained human-to-human transmission outside of China. Here we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.37. However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.005. This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a large global epidemic.

ecology↗

bioRxiv2019

Accurate forecasts of the effectiveness of interventions against Ebola may require models that account for variations in symptoms during infection

Epidemiological models are routinely used to predict the effects of interventions aimed at reducing the impacts of Ebola epidemics. Most models of interventions targeting symptomatic hosts, such as isolation or treatment, assume that all symptomatic hosts are equally likely to be detected. In other words, following an incubation period, the level of symptoms displayed by an individual host is assumed to remain constant throughout an infection. In reality, however, symptoms vary between different stages of infection. During an Ebola infection, individuals progress from initial non-specific symptoms through to more severe phases of infection. Here we compare predictions of a model in which a constant symptoms level is assumed to those generated by a more epidemiologically realistic model that accounts for varying symptoms during infection. Both models can reproduce observed epidemic data, as we show by fitting the models to data from the ongoing epidemic in the Democratic Republic of Congo and the 2014-16 epidemic in Liberia. However, for both of these epidemics, when interventions are altered identically in the models with and without levels of symptoms that depend on the time since first infection, predictions from the models differ. Our work highlights the need to consider whether or not varying symptoms should be accounted for in models used by decision makers to assess the likely efficacy of Ebola interventions.

epidemiology↗

bioRxiv2018

Movement and conformity interact to establish local behavioural traditions in animal populations

The social transmission of information is critical to the emergence of animal culture. Two processes are predicted to play key roles in how socially-transmitted information spreads in animal populations: the movement of individuals across the landscape and conformist social learning. We develop a model that, for the first time, explicitly integrates these processes to investigate their impacts on the spread of behavioural preferences. Our results reveal a strong interplay between movement and conformity for determining whether local traditions establish across a landscape or whether a single preference dominates the whole population. The model is able to replicate a real-world cultural diffusion experiment in great tits Parus major, but also allows for a range of predictions for the emergence of animal culture under various initial conditions, habitat structure and strength of conformist bias to be made. Integrating social behaviour with ecological variation will be important for understanding the stability and diversity of culture in animals.

animal behavior and cognition↗