Genetius

The study simulated the spatio‑temporal spread of banana bunchy top virus in Benin and evaluated a single‑year, country‑wide surveillance design, revealing that early detection is possible but costly (≈USD 100,000 yr⁻¹). Under a limited budget (USD 10,000 yr⁻¹), an optimal allocation of 500 sites with 10 samples each achieved a 75 % mean detection probability, albeit with up to a one‑year detection delay. The integrated simulation‑economic framework offers quantitative guidance for cost‑effective pathogen monitoring in smallholder systems.

The study introduces a data‑augmented adaptive multiple importance sampling (DA‑AMIS) framework that combines Bayesian inference with stochastic epidemic modeling to estimate transmission parameters of banana bunchy top virus (BBTV) from limited field data. Validation with independent datasets from Burundi and Malawi confirmed the robustness of the inferred infection and dispersal rates, revealing a 12% infection risk from replanting suckers and identifying April as the peak infection period.

This study integrates satellite mapping, field surveys, farmer interviews, and epidemiological modelling to map banana cultivation and BBTV incidence across Benin, identifying high‑risk southern regions. It reveals that informal planting‑material networks, low disease awareness, and socioeconomic vulnerability drive virus spread, and modelling shows injection‑based control as the most effective nationwide strategy. The findings support spatially targeted, socially informed management to protect banana livelihoods.

The study employed an in‑silico pipeline—combining homology modeling, molecular docking, ADMET profiling, and 100‑ns molecular dynamics—to discover inhibitors of coat proteins from four major banana viruses. Virtual screening of 100 plant‑derived compounds identified the flavonoid glucoside Saponarin as a top‑scoring, broad‑spectrum candidate that forms stable, allosteric interactions with conserved capsid residues, suggesting potential for antiviral crop protection.