Data Science in Science

Data Science at the Intersection of Health and the Environment

Dear Colleagues,

Environmental extremes, global population shifts, and growing pressure on natural resources are reshaping risk and health outcomes within tightly interdependent socio‑environmental systems. These dynamics influence disease patterns, exposure pathways, population vulnerability, and the stability, capacity, and infrastructure of communities and healthcare systems.

Rapid advances in sensing technologies, remote‑sensing platforms, and large‑scale environmental and demographic datasets have created unprecedented opportunities for quantitative research. At the same time, the scale, heterogeneity, and uncertainty inherent in these data demand modern statistical, machine learning, and mathematical methods capable of integrating multiple data streams, modeling spatiotemporal dynamics, quantifying uncertainty, and supporting real-world decision making.

This Special Issue of Data Science in Science invites original research and review papers that advance methodological innovation, computational scalability, and real-world applications in environmental health; climate change adaptation, mitigation, and, resilience; and coupled human-environment systems. Contributions may involve novel analytic tools, new data resources, or integrative frameworks that strengthen our ability to diagnose, predict, or mitigate health risks in a complex and evolving environment.

Topics of Interest

Topics include - but are not limited to - the following research areas:

• Multisource climate data fusion

Integrating remote-sensing products, climate reanalyses, ecological observations, socioeconomic layers, and ground‑based monitoring networks to improve climate‑risk assessment and attribution.

• Spatio-temporal modeling of climate-sensitive systems

Modern probabilistic and machine learning approaches to characterize dynamic processes such as wildfire behavior, urban heat islands, coastal erosion, and hydrological extremes.

• Human adaptation and decision-making analytics

Quantitative models linking environmental stressors to migration, land‑use change, infrastructure adaptation, food security, and public‑health outcomes.

• Uncertainty quantification in climate impact modeling

Advances in Bayesian modeling, emulator design, large‑scale ensemble approaches, and uncertainty propagation for complex simulation pipelines.

• Machine learning for climate extremes

Methods for detection, prediction, and attribution of rare high‑impact events, including compound extremes, within changing climate baselines.

• Equity‑focused climate analytics

Data‑driven approaches assessing differential exposure, vulnerability, and adaptive capacity across communities.

• Network and systems modeling

Mathematical and computational modeling of cascading failures, inter-connected risks, and cross‑sectoral interactions (e.g., energy–water–food systems).

• Experimental design and next‑gen monitoring

Optimal sensor placement, adaptive sampling, and robust monitoring networks for environmental and socio‑environmental systems.

Sincerely yours,
Special Issue Editors, Data Science at the Intersection of Health and the Environment