Integrating Spatial Omics with Environmental Exposure Data for Asthma Risk Prediction: Interpretability, Bias, Real-World Performance, Implementation, and Equity 

Waiswa Arajab

Department of Pharmacy Kampala International University Uganda

Email: arajab.waiswa@studwc.kiu.ac.ug

ABSTRACT

Asthma is a complex, chronic respiratory disease influenced by both genetic and environmental factors, and it carries significant public health and economic burdens. Emerging spatial omics technologies, including spatial transcriptomics, proteomics, and metabolomics, enable high-resolution molecular characterization of tissues. At the same time, environmental exposure datasets capture temporally and spatially resolved risk factors such as air pollution, urban vegetation, and land-use patterns. Integrating these heterogeneous datasets can improve predictive models for asthma risk, enhance the interpretability of biological and environmental interactions, and inform precision public health interventions. Challenges remain in model interpretability, bias, equity, real-world validation, and implementation, particularly in ensuring fairness across diverse populations and maintaining data privacy. Approaches to data fusion, bias detection, and stakeholder engagement are critical to facilitate ethical and effective deployment. This review highlights current methodologies, practical considerations, and prospective deployment scenarios for integrating spatial omics with environmental exposure data to advance asthma risk prediction, with a focus on equity, reproducibility, and translational impact.

Keywords: Asthma, Spatial Omics, Environmental Exposure, Predictive Modeling, and Health Equity.

CITE AS: Waiswa Arajab (2026). Integrating Spatial Omics with Environmental Exposure Data for Asthma Risk Prediction: Interpretability, Bias, Real-World Performance, Implementation, and Equity. RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 6(1):67-74. https://doi.org/10.59298/RIJSES/2026/616774