A Malaria Analytics Framework to Support Evolution and Interoperability of Global Health Surveillance Systems

Malaria is a leading cause of death in Africa. Many organizations, NGO's, and government agencies are collaborating to prevent, control, and eliminate malaria. In order to succeed in these shared goals, an integrated, consistent knowledge source to empower informed decision-making is required. Malaria surveillance is currently performed using dynamic, interconnected, systems which require rapid data exchange between different platforms. An important challenge these systems must overcome is the occurrence of dynamic changes in one or more interacting components, which can lead to inconsistencies and mismatches between components of the infrastructure. In this paper, we present our efforts toward the design and development of the semantic interoperability and evolution for malaria analytics platform, with the goal of improving data and semantic interoperability for dynamic malaria surveillance and to support the integration of data across multiple scales. The long term target is to deliver transparent and scalable tools for decision making for malaria elimination. Our analysis is focused on sentinel sites in selected African countries, including Uganda and Gabon.