“Multi-Model Data Integration and Knowledge Creation Tool for Major Infectious Diseases Control (MMDI-KIT)”
Data Gathering
The MMDI-KIT project gathers data from a diverse range of sources, including epidemiological databases containing disease case records, clinical data from healthcare facilities, environmental monitoring systems tracking vector breeding sites and climate patterns, genomic datasets detailing pathogen and host......
Data Integration and Management
Data integration and management involve the systematic collection, standardization, and storage of diverse data sources into a unified system. This process ensures data accuracy, consistency, and accessibility, enabling seamless analysis and knowledge extraction.
Data Analysis and Modeling
Data analysis and modeling transforms raw data into meaningful insights through diverse techniques, including statistical analysis, machine learning algorithms, and mathematical models. These methods uncover patterns, trends, and correlations, enabling predictive capabilities and informed decision-making.
Study Background
Jimma University is planning to conduct an innovative project titled "Development of Multi-Modal Data Integration and Knowledge Creation Tool (MMDI-KIT)" aiming to support the national effort on infectious diseases surveillance, prevention and control by applying emerging technologies such as data science, artificial intelligence and knowledge mining. Therefore, this Memorandum of Understanding (MoU) outlines to collaborate between Jimma University and Ethiopian Ministry of Health on MMDI-KIT project. This collaboration intends to improve data integration, knowledge generation, and predictive models in the control and prevention of major infectious diseases. By leveraging expertise and resources, Jimma University and the Ministry of Health will work in collaboration on the mentioned project to enhance decision-making processes and contribute to the prevention and control of infectious diseases in Ethiopia.
Field Work
Fieldwork for MMDI-KIT data collection is a multi-pronged endeavor, focusing on gathering diverse data types to populate the integrated system. Epidemiological data collection involves active case finding, community surveys, and the collection of clinical samples, such as blood or tissue, for laboratory analysis. Environmental data collection includes monitoring vector breeding sites, water quality, and climatic conditions through the deployment of sensors and direct sampling. Social data is gathered through community interviews, focus group discussions, and the collection of mobile phone data to understand mobility patterns and social interactions. Field teams utilize standardized data collection tools and protocols, ensuring data consistency and quality. Real-time data entry is prioritized, using mobile devices and online platforms to facilitate rapid data transfer and analysis. Furthermore, field teams are trained to conduct participatory mapping exercises with local communities, identifying high-risk areas and understanding local knowledge about disease transmission. They also engage in community sensitization campaigns, educating residents about disease prevention and control measures. The integration of community-based data collection methods ensures that the MMDI-KIT system captures local context and knowledge, enhancing the accuracy and relevance of the integrated data. Ethical considerations are paramount, with informed consent obtained from all participants and data privacy ensured through anonymization and secure data storage practices. The field data collected is crucial for validating and refining the models developed within the MMDI-KIT system, ensuring its effectiveness in supporting infectious disease control.
