In this talk, I will discuss applying a recently developed approach to estimate parameters of a disease transmission model for a group of macroparasites that infect an estimated 1.5 billion people worldwide. While the disease is widespread, its spread occurs on relatively local scales and the vulnerability of populations can vary from region to region. Hence, key epidemiological parameters of mechanistic transmission models vary across regions and understanding these differences is important for developing strategies to mitigate morbidity of the disease. We infer these parameters for 5183 distinct regional units across sub-Saharan Africa. Inferring these parameters is challenging since data is limited to relatively few points in space and time. Previously developed geostatistical maps use this limited data, along with socioeconomic and environmental indicators, to provide broad-scale distributional estimates of disease prevalence. Using a Bayesian statistical framework that employs an adaptive multiple importance sampling algorithm, we fit these geostatistical distributional data to a transmission model. We then use these parameterized transmission models to predict how various mitigation strategies will impact broad-scale disease prevalence.