Fighting Malaria With Advanced Ontological Engineering

The mission of one of the world’s largest, most well-known international Health and Human Services (HHS) organizations is the eradication of malaria in Africa. In order to accomplish this effectively, information about malarial outbreaks must be tracked geographically in a comprehensive manner-and in real time.

To achieve their objectives, the HHS decided to employ an innovative software based upon a Decision Support System (DSS) which is combined with a Geographic Information System (GIS) that allows for actual geo-mapping of information. This new DSS is a semantic-based software that is also known as “ontological” software,” meaning that it can make correct inferences from disparate, dispersed data located in many different areas of an information system.

Frequently, data input is incomplete or entered in a system by numerous individuals who do not have data uniformity as one of their primary objectives. When there is a lack of such uniformity, most data systems cannot retrieve data properly-if they can retrieve it at all.

The HHS initiative in Africa called for a totally re-designed, more complex DSS-that was still simple to operate and understand by multiple users in the healthcare field. Clearly, due to data complexity and data recording variations, a new system with more comprehensive capabilities was mandatory.

The need to standardize different methodologies of data measurement and evaluation-and make it adaptable and, therefore, easily adopted in different countries-became very apparent. Additionally, New ontological DSS with GIS capabilities was expected to provide measurement and usage consistency whenever similar initiatives were launched in other countries worldwide.

This new system has been rolled out in the field and is capable of processing large volumes of disparate data and providing data evaluation results that facilitate accurate decision-making. Without such decision-making capabilities, the malarial intervention and impact measures would prove to be next to impossible to track and evaluate properly.

In the development phase, the DSS was expected to handle complex mathematical calculations and provide benchmarking with historical data to determine the malarial control program’s effectiveness. Its core responsibility was to assist proactively and reactively to address the disease spread and its control.

The intent of the system is to support public health officers in the field by providing accurate and fast analysis based on selected criteria. This will assist healthcare officials in examining patterns and trends and arriving at accurate decisions to control the disease spread and to proactively manage it.

The mission of the HHS is to produce better insecticides and provide improved tools to reduce the impact and transmission of such vector-borne diseases as malaria and dengue fever. Multiple initiatives are in progress in numerous countries at this time, and because of this unique DSS platform, the organization will be able to measure their initiatives’ impacts effectively.
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An inference-based DSS structure is difficult to program and maintain using standard knowledge-based or rule-based systems. If a new relationship or a hierarchy for a data element were to be discovered, non-ontological systems would be required to spend much more time with code modification than would an ontology-based DSS, such as the one described here.


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