Biology subjects

Gilligan, C. A.

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

10 recordsLinked to original sources

bioRxiv2026

Sustaining smallholder banana production in Banana Bunchy Top Disease endemic landscapes: integrating clean seed, roguing, and farmer training

Banana bunchy top virus (BBTV) continues to threaten smallholder livelihoods and food security across sub-Saharan Africa. While clean-seed programmes are widely promoted, their long-term effectiveness is often compromised by rapid reinfection in endemic landscapes. We developed an integrated framework combining spatially explicit, stochastic epidemiological modelling with additional cost-benefit analysis and use of socio-behavioural data to evaluate strategies for stabilising production. Our results demonstrate that frequent monthly inspections and accurate symptom detection are essential for disease suppression. Crucially, the economic analysis reveals that prioritizing diagnostic competence as an economic asset is necessary: improving detection efficiency can more than double farmer net revenue under realistic market conditions. Socio-behavioural findings further confirm that a farmers ability to correctly recognise symptoms is the strongest predictor of roguing adoption, far outweighing demographic characteristics. These results provide quantifiable guidance for disease management and highlight that the sustainability of clean-seed interventions hinges on shifting policy from simple seed replacement to investing in farmer diagnostic capacity. Strengthening this local surveillance capability transforms informal seed systems into resilient, durable tools for safeguarding household nutrition and regional food security.

plant biology↗

bioRxiv2025

Parameterisation of epidemiological models from small field experiments: a case study of banana bunchy top virus transmission

Accurate estimation of epidemiological parameters from limited field data remains a major challenge in plant disease modeling. We present a novel data-augmented adaptive multiple importance sampling (DA-AMIS) framework that integrates Bayesian inference with stochastic epidemic modeling to estimate key transmission parameters from small field experiments. Using detailed individual-level observations from a 24-plant experiment on the natural spread of banana bunchy top virus (BBTV) in Benin, we jointly inferred infection timing, dispersal characteristics, and transmission rates for both primary and secondary infections. Model validation against independent datasets from BBTV field trials in Burundi and Malawi showed close correspondence between simulated and observed prevalence dynamics, confirming the generality of parameter estimates across regions. The inferred 12% infection rate of replanting suckers underscores the risk of disease introduction through planting material, while simulations identified April as the period of peak infection, providing actionable insights for surveillance timing.

plant biology↗

bioRxiv2025

Predicting the cross-continental spread of the cassava brown streak disease epidemic in sub-Saharan Africa

Cassava brown streak disease (CBSD) is a major threat to smallholder farmers in sub-Saharan Africa (SSA), where cassava is a staple crop. Caused by cassava brown streak ipomoviruses (CBSIs), CBSD has spread extensively since its detection in Uganda in 2004, raising concerns about ongoing spread through Southern and Central Africa and potential expansion to West Africa, home to the worlds largest cassava producer, Nigeria. Building on a stochastic epidemiological model that predicts CBSD spread at the scale of Uganda, we incorporate extensive field surveillance records to extend the model to all thirty-two major cassava-producing countries in SSA. We then deploy the model to address key strategic questions such as estimating the present day CBSD distribution and predicting rates of ongoing spread towards West Africa. We also evaluate the risk of direct introductions resulting from long range movement of infected planting material, which could trigger outbreaks beyond predicted localised dispersal limits. Our model predicts the likely arrival of CBSD in Nigeria via cross-continental spread within 25 years, and if directly introduced anywhere in West Africa, spreading to most West African nations within 10 years. The risks of ongoing and future CBSD spread highlighted in this study underscore the need for proactive phytosanitation measures, including clean seed programs, vector control, and quarantine policies to curb CBSD spread. Moreover, the model described in this study not only provides estimates for arrival times across SSA, but also lays the foundations for a continental-scale quantitative framework wherein both surveillance and management options can be explored and optimised.

ecology↗

bioRxiv2025

Relationship between spatial scale of pathogen dispersal and host landscape that minimizes infection rate in agricultural crops

ContextThe initial rate of pathogen invasion in crops is influenced by the spatial clustering of susceptible crops and the characteristics of pathogen dispersal. Previous studies have shown that various degrees of crop clustering can effectively reduce this invasion rate. However, the optimal degrees of clustering that minimize pathogen invasion have not previously been identified. ObjectivesThis study aims to determine analytically the range of crop clustering that minimizes the initial rate of pathogen invasion. MethodsWe studied artificial agricultural landscapes with crop areas arranged in identical square clusters on a regular square lattice. For pathogen dispersal, we used several common dispersal kernels, including Gaussian, negative exponential, and power-law. The optimal degree of clustering, defined by cluster size and separation distance, was calculated using a new analytical approximation for the pathogen invasion rate, which showed strong agreement with computer simulations. Additionally, we analysed a realistic cassava landscape at risk of invasion by cassava brown streak virus. ResultsWe identified a range of optimal cluster sizes and corresponding separation distances that minimize pathogen invasion rates for various dispersal kernels and landscapes with clusters of crop fields arranged on a regular square lattice. The methods can be extended to other geometrical configurations, such as long narrow fields. Using a cassava landscape as an example, we show how optimal crop clustering strategies can be derived to mitigate the potential invasion of cassava brown streak virus. ConclusionThe methods provides analytical insights that can help farmers and agricultural planners to optimize the spatial structure of agricultural landscapes to minimize initial pathogen invasion rates.

ecology↗

bioRxiv2025

Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava

Epidemiological modelling plays an important role in global food security by informing strategies for the control and management of invasion and spread of crop diseases. However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. Obtaining and refining data sets that fully represent a host landscape across territories can be a major challenge when predicting disease outbreaks. Therefore, it would be an advantage to prioritise areas in which data refinement efforts should be directed to improve the accuracy of epidemic prediction. In this paper, we present an analytical method to identify areas where potential errors in mapped host data would have the largest impact on modelled pathogen invasion and short-term spread. The method is based on an analytical approximation for the rate at which susceptible host crops become infected at the start of an epidemic. We show how implementing spatial prioritisation for data refinement in a cassava-growing region in sub-Saharan Africa could be an effective means for improving accuracy when modelling the dispersal and spread of the crop pathogen cassava brown streak virus (CBSV).

ecology↗

bioRxiv2025

Optimizing crop varietal mixtures for viral disease management: A case study on cassava virus epidemics

Cassava viral diseases, including Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD), pose significant threats to global food security, particularly in sub-Saharan Africa. This study explores the potential of varietal mixtures as a sustainable disease management strategy by introducing CropMix, a novel web-based application. The application encodes a flexible insect-borne plant pathogen transmission model to predict and optimize yields under scenarios of varietal mixtures. For instance, we use the application to evaluate the ability of virus-resistant cassava varieties to protect more susceptible varieties against CMD and CBSD, and we also consider mixtures involving tolerant varieties and non-host crops. For CMD, the high transmission rates of cassava mosaic begomoviruses limits the efficacy of mixtures, with susceptible monocultures emerging as better than susceptible-resistant mixtures whatever the whitefly pressure. In contrast, for CBSD, varietal mixtures demonstrate substantial benefits, with resistant varieties shielding susceptible ones and mitigating severe yield losses under moderate or high insect pressure. Management strategies involving non-host crops and complementary control measures, such as roguing, can further enhance outcomes. The models simplicity and adaptability make it suitable for tailoring recommendations to diverse insect-borne crop viral diseases and agroecological contexts. The study emphasizes the need for integrating real-world data and participatory frameworks to refine and implement disease management strategies. We discuss the critical balance between agronomic potential and farmer acceptability, underscoring the importance of collaborative efforts to ensure sustainable cassava production.

ecology↗

bioRxiv2023

A framework for modelling desert locust population dynamics and large-scale dispersal

As climate change leads to new areas being under threat from emerg-ing pests, there is increasing demand for approaches combining mechanistic models and geospatial information. Forecasting based on modelling can in-form surveillance, early warning and management. This paper introduces a novel framework for modelling desert locust population dynamics and migra-tion using knowledge of pest biology, climate and remote sensing data, wind trajectories and desert locust survey data from Locust Hub (FAO). Fur-thermore, we propose an algorithm for forecasting short-term and long-term desert locust migration based on limited reporting data.

ecology↗

bioRxiv2022

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa

The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. Since 2004, an epidemic of cassava brown streak disease (CBSD) has been spreading rapidly from Uganda, with the disease causing necrosis of the edible root tissue. Based on sparse surveillance data, the epidemic front is currently believed to be at least as far west as central DRC and as far south as Zambia. The DRC is the worlds highest per capita consumer of cassava and future spread threatens production in West Africa which includes Nigeria, the worlds largest producer of cassava. Here, we take a unique Ugandan CBSD surveillance dataset spanning 2004 to 2017 and develop, parameterise, and validate a landscape-scale, spatiotemporal epidemic model of CBSD at a 1 km2 resolution. While this paper focuses on Uganda, the model is designed to be readily extended to make predictions beyond Uganda for all 32 major cassava producing countries of SSA, laying the foundations for a tool capable of informing strategic policy decisions at a national and regional scale.

ecology↗

bioRxiv2019

When does spatial diversification usefully maximise the durability of crop disease resistance?

1Maximising the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialised. The factors that interact with and determine the magnitude of this spatial effect are not currently well understood however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multi-season semi-discrete epidemiological model tracks spatial spread of wild-type and resistance breaking pathogen strains, and incorporates a localised reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance breaking trait, the efficacy of host resistance, and the length of the timeframe of interest, all influence the strength of the spatial diversification effect. These interactions, which are often complex and non-linear in nature, produce substantial variation in the predicted yield gain from the use of a spatial diversification strategy. This in turn allows us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modelling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.

epidemiology↗

bioRxiv2018

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially-explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach - optimal control theory - allows the very best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of plants, animals and humans.

epidemiology↗