edson Byamukama 3 , peace Habomugisha 3 , thomson Lakwo 4 , edridah tukahebwa 4 & frank o. Richards 2 concern is emerging regarding the challenges posed by spatial complexity for modelling and managing the area-wide elimination of parasitic infections. While this has led to calls for applying heterogeneitybased approaches for addressing this complexity, questions related to spatial scale, the discovery of locally-relevant models, and its interaction with options for interrupting parasite transmission remain to be resolved. We used a data-driven modelling framework applied to infection data gathered from different monitoring sites to investigate these questions in the context of understanding the transmission dynamics and efforts to eliminate Simulium neavei-transmitted onchocerciasis, a macroparasitic disease that causes river blindness in Western Uganda and other regions of Africa. We demonstrate that our Bayesian-based data-model assimilation technique is able to discover onchocerciasis models that reflect local transmission conditions reliably. Key management variables such as infection breakpoints and required durations of drug interventions for achieving elimination varied spatially due to site-specific parameter constraining; however, this spatial effect was found to operate at the larger focus level, although intriguingly including vector control overcame this variability. these results show that data-driven modelling based on spatial datasets and model-data fusing methodologies will be critical to identifying both the scale-dependent models and heterogeneity-based options required for supporting the successful elimination of S. neavei-borne onchocerciasis. In recent years, there has been growing appreciation of the role that mathematical models can play in guiding the control or eradication of the major preventable helminthic diseases, ranging from soil-transmitted helminthiases to schistosomiasis, lymphatic filariasis (LF), and onchocerciasis 1-11. This recognition has been catalysed by the expectation that quantitative mathematical models can provide a robust scientific basis for predicting the course of infection resulting from perturbations induced by interventions 12. A second major reason is the implicit belief that making such ecological predictions is possible, despite the complex, nonlinear, and open nature of the parasitic systems being studied and the approximations necessary to translate understanding of these systems into computer programs 4,13-15. Despite these constraints, helminth transmission models are created and used today in the belief not only that they offer the best available tools for providing dependable predictions of the future states of such systems, but also that they will continue to evolve towards more realistic representations of parasitic systems as knowledge of transmission processes and structures improves over time 16,17. Apart from the above challenges, which in essence question the intrinsic predictability of any complex ecological system 15,18,19 , a growing strand ...