Parameterisation of epidemiological models from small field experiments: a case study of banana bunchy top virus transmission
Retkute, R.; Omondi, A. B.; Soko, M.; Staver, C.; Thomas, J. E.; Gilligan, C. A.
Show abstract
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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown 96%
- Modelling the spread and mitigation of an emerging vector-borne pathogen:citrus greening in the U.S. 95%
- Using "sentinel" plants to improve early detection of invasive plant pathogens 94%
Similar papers in this journal
- Barrier effects on the spatial distribution of Xylella fastidiosa in Alicante, Spain 93%
- Modelling Inoculum Availability of Plurivorosphaerella nawae in Persimmon Leaf Litter with Bayesian Beta Regression 91%
- Management performance mapping and the value of information for regional prioritization of management interventions 90%
Similar papers in this journal
- Estimating relative generation times and relative reproduction numbers of Omicron BA.1 and BA.2 with respect to Delta in Denmark 92%
- Identifiability investigation of within-host models of acute virus infection 89%
- What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases? 89%
Similar papers in this journal
- PrioriTree: a utility for improving phylodynamic analyses in BEAST 91%
- Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease 90%
- HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations 90%
Similar papers in this journal
- GI-NemaTracker - A farm system-level mathematical model to predict the consequences of gastrointestinal parasite control strategies in sheep 92%
- Novel epidemiological model of gastrointestinal-nematode infection to assess grazing cattle resilience by integrating host growth, parasite, grass and environmental dynamics 91%
- GLOWORM-META: Modelling gastrointestinal nematode metapopulation dynamics to inform cattle biosecurity research 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.