Speaker
Description
African animal trypanosomosis (AAT) is a substantial burden to livestock productivity across sub-Saharan Africa, yet its prevalence is underestimated because the most common diagnostic tests, the haematocrit centrifugation and buffy coat techniques (HCT/BCT), have low sensitivity. The polymerase chain reaction test (PCR) offers a higher sensitivity but is costly and rarely available in field settings. We used a Bayesian hierarchical latent-class model applied to bovine trypanosomosis data from over 40 studies across Africa to estimate the sensitivity and specificity of HCT/BCT and PCR. We then adjusted apparent prevalence and fitted geostatistical models for two high-burden, data-rich countries: Ethiopia and Nigeria. We quantified two sources of uncertainty: diagnostic uncertainty from imperfect tests, and coverage uncertainty from incomplete spatial sampling. We estimated sensitivities of 29.8% for HCT/BCT and 68.9% for PCR, with specificities exceeding 99%. Adjusting for diagnostic performance increased the mean prevalence from 0.086 to 0.299 in Nigeria and from 0.092 to 0.382 in Ethiopia, corresponding to 1.93 million and 3.39 million infected cattle within the tsetse belt. Diagnostic uncertainty dominated across tsetse-infested regions. By integrating diagnostic uncertainty into spatial models, we obtain a more realistic picture of AAT prevalence, improving surveillance and control targeting, with applicability to other neglected tropical diseases.
