ABSTRACT Zambia, which spans the southern and central Africa region, contains the full continuum of malaria endemicity (hypo-, meso-, and holoendemic), positioning it as an ideal environment for the development of enhanced surveillance tools with broad applicability. Malaria control and elimination in Zambia requires optimizing strategies for current and novel interventions, and the lessons learned will be applicable elsewhere in sub- Saharan Africa with similar endemicities. The World Health Organization emphasizes the need to refocus on reducing morbidity and mortality and identifying the optimal set of interventions for areas of hyper- and holoendemic malaria. Increasing urbanization of human populations and the incursion of urban malaria vectors into sub-Saharan Africa present an additional new threat, particularly in mesoendemic areas. In low-transmission settings, subnational malaria elimination is challenging to achieve, verify, and sustain. Integrated surveillance approaches linked bidirectionally to interventions and tailored to local contexts are required to inform and support the implementation of existing and forthcoming interventions. We propose multiple approaches to quantifying and characterizing malaria transmission by combining traditional and novel data sets using mathematical modeling and machine learning-supported computational approaches. We will establish data streams and operationalize surveillance platforms in Zambia that will enable the development and integration of measurement and computational tools to transform malaria surveillance into a key intervention and address outstanding scientific questions on the barriers to control and elimination in high, medium, and low transmission settings. 1