Title : Evaluating the efficacy of malaria surveillance systems and their influence on malaria control decision-making in Sub-Saharan Africa
Abstract:
Malaria remains a major global public health challenge, with Sub-Saharan Africa bearing the highest burden of disease. Effective control and eventual elimination depend on strong epidemiological surveillance systems that generate timely, accurate, and actionable data for decision-making. Despite progress in digital health innovations, significant gaps persist in surveillance system performance, particularly in low and middle-income countries. This study evaluates the efficacy of malaria surveillance systems and examines their influence on malaria control decision-making within the context of Sub-Saharan Africa, with a focus on Zambia. Using a mixed-methods approach, the research integrates evidence from systematic review and field-based assessments across district, provincial, and national levels. Key indicators assessed include data completeness, timeliness, accuracy, and the extent of data utilization in guiding interventions. The findings reveal that while surveillance systems have improved with the integration of electronic reporting platforms, challenges such as underreporting, limited analytical capacity, and weak feedback mechanisms continue to affect system performance. Additionally, disparities exist between rural and urban settings, and across different levels of the health system. Importantly, the study demonstrates that high-quality surveillance systems significantly enhance evidence-based decision-making, enabling targeted interventions such as indoor residual spraying, insecticide-treated net distribution, and improved case management. Conversely, weak surveillance systems result in delayed responses and inefficient allocation of resources. The study underscores the critical role of strengthening surveillance systems in achieving global malaria control goals. It recommends investments in digital infrastructure, capacity building, and institutionalization of data-driven decision-making practices to improve public health outcomes.
Keywords: Malaria; Surveillance Systems; Sub-Saharan Africa; DHIS2; MHealth; Genomic Surveillance; Data-to-Action; Public Health Systems; Anopheles
