Cost-effective early detection of banana bunchy top disease: insights from spatio-temporal modelling in Benin

Abstract Banana bunchy top disease (BBTD), caused by banana bunchy top virus (BBTV), threatens food security and livelihoods across sub-Saharan Africa. Effective surveillance is critical for early detection, but in many regions, monitoring is constrained to short operational windows due to limited funding and laboratory capacity. When surveillance is restricted to a single calendar year, sampling effort must be carefully allocated to maximize early detection, yet quantitative guidance linking detection performance to cost is lacking. To address this, we simulated the spatio-temporal spread of BBTV in Benin and retrospectively evaluated country-level BBTD surveillance using a single-year cross-sectional survey design. We found that early detection is feasible but prohibitively expensive (USD 100,000 per year). Under a constrained budget of USD 10,000 per year, an optimal strategy - defined as reaching 75% mean detection probability earliest - was 500 sites with 10 samples per site, though this could delay BBTD detection by up to one year. Our integrated simulation - economic framework quantifies trade-offs between cost and detection likelihood, providing guidance for resource-efficient national-scale BBTD surveillance and a transferable approach for plant pathogen monitoring in smallholder systems.