Scalable, open-access and multidisciplinary data integration pipeline for climate-sensitive diseases

Journal article

Climate-sensitive infectious diseases pose an important challenge for human, animal and environmental health and it has been estimated that over half of known human pathogenic diseases can be aggravated by climate change. While climatic and weather conditions are important drivers of transmission of vector-borne diseases, socio-economic, behavioural, and land-use factors as well as the interactions among them impact transmission dynamics. Analysis of drivers of climate-sensitive diseases require rapid integration of interdisciplinary data to be jointly analysed with epidemiological (including genomic and clinical) data. Current tools for the integration of multiple data sources are often limited to one data type or rely on proprietary data and software. To address this gap, we develop a scalable and open-access pipeline for the integration of multiple spatio-temporal datasets that requires only the declaration of the country and temporal range and resolution of the study. The tool is locally deployable and can easily be integrated into existing climate-disease-modelling applications. We demonstrate the utility of the tool for dengue modelling in Vietnam where epidemiological data are legally required to remain local. We include a pipeline for bias correction of climate data to enhance their quality for downstream modelling tasks. The Dengue Advanced Readiness Tools-Pipeline empowers users by simplifying complex download, correction, and aggregation steps, fostering data-driven discovery of relationships between infectious diseases and their drivers in space and time, and enhancing reproducibility in research. Additional modules and datasets can be added to the existing ones to make the pipeline extendable to use cases other than the ones presented here.

Authors: Dasgupta, A; Perez-Fernandez, I; Huynh, T; Mills, C; Nicholls, RC

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Taylor and Francis
• Journal: Wellcome Open Research
• Volume: 10
• Pages: 467
• Publication date: 2025-11-15
• DOI: 10.12688/wellcomeopenres.24774.2
• EISSN: 2398502X
• ISSN: 2398502X
• Pmid: 41312305
• Language: English
• Keywords: Data Science; Climate-Sensitive Infectious Diseases; Dengue; Automated Workflows