ArticlePLoS computational biology2020
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Modelling plant disease spread via high-resolution human mobility networks.Journal of the Royal Society, Interface · 2026Article
- Modelling the effects of virus-resistant planting material on the transmission dynamics of banana bunchy top disease.Scientific reports · 2025Article
- Developing a spatio-temporal model for banana bunchy top disease: leveraging remote sensing and survey data.Frontiers in plant science · 2025Article
- Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation.Journal of mathematical biology · 2024Article
- Spatial parameters associated with the risk of banana bunchy top disease in smallholder systems.PloS one · 2021Article
- The Epidemiology of Plant Virus Disease: Towards a New Synthesis.Plants (Basel, Switzerland) · 2020Review
- Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic.BMC public health · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected-Susceptible model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.