A Semantic Framework to Improve Interoperability of Malaria Surveillance Systems
ObjectiveMalaria is one of the top causes of death in Africa and some other regions in the world. Data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. Improving the interoperability of data so...
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| Published in: | Online journal of public health informatics 2018-05, Vol.10 (1) |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | ObjectiveMalaria is one of the top causes of death in Africa and some other regions in the world. Data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. Improving the interoperability of data sources through the use of shared semantics is a key consideration when designing surveillance systems, which must be robust in the face of dynamic changes to one or more components of a distributed infrastructure. Here we introduce a semantic framework to improve interoperability of malaria surveillance systems (SIEMA).IntroductionIn 2015, there were 212 million new cases of malaria, and about 429,000 malaria death, worldwide. African countries accounted for almost 90% of global cases of malaria and 92% of malaria deaths. Currently, malaria data are scattered across different countries, laboratories, and organizations in different heterogeneous data formats and repositories. The diversity of access methodologies makes it difficult to retrieve relevant data in a timely manner. Moreover, lack of rich metadata limits the reusability of data and its integration. The current process of discovering, accessing and reusing the data is inefficient and error-prone profoundly hindering surveillance efforts.As our knowledge about malaria and appropriate preventive measures becomes more comprehensive malaria data management systems, data collection standards, and data stewardship are certain to change regularly. Collectively these changes will make it more difficult to perform accurate data analytics or achieve reliable estimates of important metrics, such as infection rates. Consequently, there is a critical need to rapidly re-assess the integrity of data and knowledge infrastructures that experts depend on to support their surveillance tasks.MethodsIn order to address the challenge of heterogeneity of malaria data sources we recruit domain specific ontologies in the field (e.g. IDOMAL (1)) that define a shared lexicon of concepts and relations. These ontologies are expressed in the standard Web Ontology Language (OWL).To over come challenges in accessing distributed data resources we have adopted the Semantic Automatic Discovery & Integration framework (SADI) (2) to ensure interoperability. SADI provides a way to describe services that provide access to data, detailing inputs and outputs of services and a functional description. Existing ontology terms are used when buildin |
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| ISSN: | 1947-2579 1947-2579 |