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

Abhishek Dasgupta1,2, Iago Perez-Fernandez3, Tuyen Huynh4

  • 1Oxford Research Software Engineering Group, Doctoral Training Centre, University of Oxford, Oxford, UK.

Wellcome Open Research
|November 28, 2025
PubMed
Summary
This summary is machine-generated.

Climate change exacerbates infectious diseases. We developed an open-access pipeline to integrate diverse data for analyzing climate-sensitive disease transmission, improving research reproducibility.

Keywords:
automated workflowsclimate-sensitive infectious diseasesdata sciencedengue

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Area of Science:

  • Environmental science
  • Epidemiology
  • Computational biology

Background:

  • Climate change significantly impacts human, animal, and environmental health, with over half of known infectious diseases potentially aggravated by it.
  • Transmission of vector-borne diseases is influenced by climatic conditions, but socio-economic, behavioral, and land-use factors also play crucial roles.
  • Analyzing climate-sensitive diseases necessitates integrating interdisciplinary data with epidemiological, genomic, and clinical information, a task hindered by current data integration tools.

Purpose of the Study:

  • To develop a scalable, open-access data integration pipeline for analyzing climate-sensitive infectious diseases.
  • To address the limitations of existing tools that are often data-type specific or rely on proprietary software.
  • To enhance the reproducibility and accessibility of research on the complex interactions between climate and disease.

Main Methods:

  • Developed a scalable and open-access pipeline for integrating multiple spatio-temporal datasets.
  • The pipeline requires minimal user input, only needing the study's country, temporal range, and resolution.
  • Included a bias correction module for climate data to improve downstream modeling accuracy.

Main Results:

  • Demonstrated the pipeline's utility for dengue fever modeling in Vietnam, accommodating local data requirements.
  • Successfully simplified complex data download, correction, and aggregation processes.
  • The tool facilitates data-driven discovery of spatio-temporal relationships between infectious diseases and their drivers.

Conclusions:

  • The Dengue Advanced Readiness Tools-Pipeline (DART-P) empowers researchers by simplifying data integration for climate-disease modeling.
  • The pipeline enhances research reproducibility and fosters discovery of disease-driver relationships.
  • The open-access and extendable nature of the pipeline allows for adaptation to various use cases beyond the presented examples.