Poster Number
47
Poster Title
Developing an information system for integrating clinical and genomic infectious disease data in Tanzania
Authors
1. Melkiory Beti - Kilimanjaro Clinical Research Institute (KCRI)
2. Patrick Kimu - Kilimanjaro Clinical Research Institute (KCRI)
3. Willfred Senyoni - University of Dar es Salaam (UDSM)
4. Boaz Wadugu - Kilimanjaro Clinical Research Institute (KCRI)
5. Tolbert Sonda - Kilimanjaro Clinical Research Institute (KCRI)
2. Patrick Kimu - Kilimanjaro Clinical Research Institute (KCRI)
3. Willfred Senyoni - University of Dar es Salaam (UDSM)
4. Boaz Wadugu - Kilimanjaro Clinical Research Institute (KCRI)
5. Tolbert Sonda - Kilimanjaro Clinical Research Institute (KCRI)
Abstract
Background
Infectious diseases is a serious issues in public health in low-and middle-income countries like Tanzania, where the use of information system that integrate clinical and genomic data is limited due the different data generation sources. To tackle this challenge we developed a system that link clinical data from a customized District Health Information System 2 (DHIS2) with genomic data sequenced from Nanopore machines and analysed by cgetools pipeline. The system help users to view clinical and genomic data such as patient symptoms and their multiple identified pathogens.
Methods
Clinical data were collected using customised DHIS2, an open-source software widely used in Tanzania, and R scripts that query the DHIS2 API to securely fetch clinical data and integrate it with genomic results from the CGETools bioinformatics pipeline, which uses tools like KmerFinder to identify pathogens from a single sample. Using the R Shiny web framework, the interactive web interface developed enables users to search patient IDs and view their clinical and genomic data.
Results
The system managed to integrate 21 datasets, linking clinical parameters like patient disease symptoms such as diarrhoea and fever and gender and age with genomic data showing identified pathogens. Allow users to search for patient IDs, retrieve relevant data, and visualize pathogen trends through interactive bar graphs, aiding epidemiological monitoring and outbreak detection.
Discussion
This data integration system development demonstrated the importance of linking clinical and genomic data for infectious disease surveillance. Use of an existing and open-source system like DHIS2 and cgetools pipeline facilitated efficient pathogen detection from single patient samples. Additionally, the visualization features proved essential for supporting real-time clinical decision-making.
Conclusion
By integrating clinical and genomic data, the system improved patient diagnosis, timely outbreak detection, and effective disease control. The scalable can be extended to other disease settings, contributing to improved public health outcomes.
Infectious diseases is a serious issues in public health in low-and middle-income countries like Tanzania, where the use of information system that integrate clinical and genomic data is limited due the different data generation sources. To tackle this challenge we developed a system that link clinical data from a customized District Health Information System 2 (DHIS2) with genomic data sequenced from Nanopore machines and analysed by cgetools pipeline. The system help users to view clinical and genomic data such as patient symptoms and their multiple identified pathogens.
Methods
Clinical data were collected using customised DHIS2, an open-source software widely used in Tanzania, and R scripts that query the DHIS2 API to securely fetch clinical data and integrate it with genomic results from the CGETools bioinformatics pipeline, which uses tools like KmerFinder to identify pathogens from a single sample. Using the R Shiny web framework, the interactive web interface developed enables users to search patient IDs and view their clinical and genomic data.
Results
The system managed to integrate 21 datasets, linking clinical parameters like patient disease symptoms such as diarrhoea and fever and gender and age with genomic data showing identified pathogens. Allow users to search for patient IDs, retrieve relevant data, and visualize pathogen trends through interactive bar graphs, aiding epidemiological monitoring and outbreak detection.
Discussion
This data integration system development demonstrated the importance of linking clinical and genomic data for infectious disease surveillance. Use of an existing and open-source system like DHIS2 and cgetools pipeline facilitated efficient pathogen detection from single patient samples. Additionally, the visualization features proved essential for supporting real-time clinical decision-making.
Conclusion
By integrating clinical and genomic data, the system improved patient diagnosis, timely outbreak detection, and effective disease control. The scalable can be extended to other disease settings, contributing to improved public health outcomes.