The platform integrates various data sources, each managed by a dedicated tool: data4health in the case of health data, clim4health for climate data, socio4health for socioeconomic data, land4health for land use and land cover data and drone4health for processing drones images. The tools handle everything from data acquisition and formatting to pre- and post-processing, creating harmonised datasets at the desired spatiotemporal resolution. All processes are integrated into (semi-)automated workflows. This will enable local users to link, interrogate and extract multi-scale spatiotemporal data, to understand the links between environmental change and infectious disease risk in their local context, and build robust early warning and response systems in low-resource settings.
Dependencies
The tools within the toolkit will be R-packages, and in some cases, also Python packages. Therefore the main dependencies are these two programming languages. Furthermore, the tools will depend on specific libraries, all of which can be found in the annex “Software dependencies”.
The tools within the toolkit will be R-packages, and in some cases, also Python packages. Therefore the main dependencies are these two programming languages. Furthermore, the tools will depend on specific libraries, all of which can be found in the annex “Software dependencies”.
Functioning
Each data source within the HARMONIZE toolkit will have a dedicated tool: health data will be managed by data4health, climate data by clim4health, socioeconomic data by socio4health, land use and land cover data by land4health and drone data by drone4health. Each tool will be developed as an R package, designed to access and preprocess its respective data type, aggregate it to the desired spatiotemporal resolution and transform it into a common data output structure. Where feasible, the tools will offer user-friendly options, in the form of graphic interfaces, for users without coding experience, ensuring broader accessibility and usability.
Each data source within the HARMONIZE toolkit will have a dedicated tool: health data will be managed by data4health, climate data by clim4health, socioeconomic data by socio4health, land use and land cover data by land4health and drone data by drone4health. Each tool will be developed as an R package, designed to access and preprocess its respective data type, aggregate it to the desired spatiotemporal resolution and transform it into a common data output structure. Where feasible, the tools will offer user-friendly options, in the form of graphic interfaces, for users without coding experience, ensuring broader accessibility and usability.
Development
The technology is being developed as part of the HARMONIZE project, which began in May 2022. The project is (as of July 2024) halfway through its planned four-year timeline. The first year was dedicated to ideation and conceptualization to determine the most useful applications of the tools. During the second year, working groups were formed for each tool of the toolkit. The tools data4health, clim4health, socio4health have already working prototypes, whereas land4health and drone4health are still in the early stages of development. Year 3 will be used to finish minimum viable products for all tools, and, finally, year 4 will be used for checking the reproducibility and replicability across new locations and proof-of-concept.
The technology is being developed as part of the HARMONIZE project, which began in May 2022. The project is (as of July 2024) halfway through its planned four-year timeline. The first year was dedicated to ideation and conceptualization to determine the most useful applications of the tools. During the second year, working groups were formed for each tool of the toolkit. The tools data4health, clim4health, socio4health have already working prototypes, whereas land4health and drone4health are still in the early stages of development. Year 3 will be used to finish minimum viable products for all tools, and, finally, year 4 will be used for checking the reproducibility and replicability across new locations and proof-of-concept.