Parameterisation of epidemiological models from small field experiments: A case study of banana bunchy top virus...

Bananas and plantains are among the world's most important staple food crops and provide daily calories, income, and nutritional security for millions of smallholder households, particularly across sub-Saharan Africa. Accurately estimating epidemiological parameters for major threats such as banana bunchy top virus (BBTV) is essential for predicting disease spread and designing effective management strategies, yet the limited and resource-constrained field data available in smallholder systems make this extremely challenging. Here, we introduce a data-augmented Adaptive Multiple Importance Sampling (DA-AMIS) framework that integrates Bayesian inference with a mechanistic epidemic model to recover key BBTV transmission parameters from small field experiments. Using detailed individual-level observations from a 24-plant experiment in Benin, we jointly infer latent infection times, aphid-mediated dispersal characteristics, and primary and secondary transmission rates. We validate these estimates against independent BBTV datasets from Burundi and Malawi, finding close correspondence between simulated and observed prevalence trajectories, demonstrating the transferability of inferred parameters across regions. Our results indicate that approximately 12% of replanting suckers are infected at planting, emphasizing the high risk of BBTV introduction through planting material, and simulations identify April as the period of peak infection pressure, providing actionable insight for surveillance timing. These findings show that small field experiments, when combined with advanced Bayesian computational methods, can yield robust and generalizable epidemiological parameter estimates.