Background: Achieving malaria elimination requires identifying hotspots and key risk factors to guide targeted interventions. In this context, spatial models are crucial to support decision making. Yet, in elimination settings, national-level models suffer from some limitations. They often rely on prevalence survey data, which may be less reliable in low-transmission settings due to excess zeroes and under-representation of high-risk age groups. In addition, they often assume stationary risk factors, while pre-elimination strategies may lead to the emergence of (temporarily) distinct endemic profiles within countries. To address these biases, we model malaria incidence in Senegal, targeting malaria elimination for 2030, using routine malaria data and stratifying models by transmission level. Methods: Malaria incidence rates by health facility were calculated using reported cases and estimated catchment population, allowing for overlap between health facility catchment areas. Malaria incidence was then modelled using fine-resolution environmental and socio-economic covariates within a Bayesian hierarchical framework implemented using the INLA-SPDE approach. Models were stratified by season and endemicity level and compared with a national benchmark model to assess the heterogeneity of malaria risk factors. Finally, dasymetric disaggregation was applied to generate high-resolution (1 km) maps of malaria incidence. Results: The modelled incidence of malaria decreased by three cases per 1,000 people between 2017 (47 per 1,000 people) and 2019 (44 per 1,000 people), before increasing by 15 cases per 1,000 people in 2021 (59 per 1,000 people). At the sub-national level, malaria incidence followed an increasing gradient from the urbanised north-west to the south-eastern regions. Despite comparatively low seasonal variation in malaria risk factors, the importance of specific risk factors varied across areas with different levels of endemicity. Some factors (e.g. precipitation, ethnicity and urban agriculture) were particularly associated with either low, moderate, or high transmission settings. Fine-scale maps of the posterior estimates show higher malaria incidence for areas closer to a health facility than those further away, thus highlighting potential underdiagnosis of malaria in remote areas. Conclusion: Our findings underscore the importance of taking both endemicity and risk factors into account while evaluating malaria incidence. This approach allows the identification of risk factors specific to each endemicity level, essential for the design and implementation of more effective targeted interventions. In the near future, as prevalence data become less reliable and the quality of health system data continues to improve, fine-scale incidence models and maps will become critical tools to support decision-making
