Evidence map›Paper›PMID 40551762›Full record

ArticleFrontiers in plant science2025

Developing a spatio-temporal model for banana bunchy top disease: leveraging remote sensing and survey data.

Renata Retkute, Christopher A Gilligan

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Renata RetkuteEpidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.
Christopher A GilliganEpidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemics of Banana Bunchy Top Disease (BBTD) in sub-Saharan Africa are threatening global food security and endangering the livelihoods of smallholder farmers. This study introduces methods for developing data-based models to derive banana production maps and process-based models to assess the potential spread of BBTV at a landscape scale. We introduce two novel aspects: a methodology for deriving probabilistic banana production maps based on high-resolution remote sensing products and parameterization of the epidemiological model for BBTD from limited survey data. We generated a countrywide banana production map for Tanzania and a state-wide map for Ogun State in Nigeria. We used the banana map together with published data from BBTD surveys to parameterize a model for BBTD spread in Tanzania. Our results emphasize the importance of surveys, as having data on the presence and absence of Banana Bunchy Top Virus (BBTV) at different stages of epidemics is crucial not only for effective control of the disease but also for prediction, including making reasonable model assumptions, model parameterization, and model validation that underpin predictions.

Indexed as

banana bunchy top viruscrop managementepidemiological modelingparameter estimationremote sensing

Identifiers

PMID40551762
PMCPMC12183257

What OpenQuestion holds

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Registered trials

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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.