Human-natural systems are increasingly interconnected. Data-driven science and engineering enhance our understanding of these complex processes. The intersection of Public Health and the Environment is among the most systemically important - each facing its own urgent crises, and with growing spillover of negative environmental outcomes influencing negative human health outcomes, in particular. Mathematical and Statistical tools are essential for understanding and addressing the complex and interrelated challenges of public and environmental health. Emergent data sources coupled with modern methods have great promise to help mitigate future risks, but transdisciplinary methodological innovations are required to comprehensively address multi-faced challenges.

New data-driven research directions at the intersection of public health and the environment may include: ​

• New descriptive and inferential approaches are needed to help summarize, interpret, link, and then analyze data across a spectrum of diverse sources, from epidemiological studies and health registries to environmental monitoring and surveys. These will enable discovery of shared patterns, trends, associations, and even causal relationships between environmental factors and health outcomes. 

• New species abundance, richness, and diversity models harvested through big data can help measure and contrast ecosystem and biodiversity health across spatial and temporal resolutions. They will also help assess human, climate, and invasive species impacts on the environment.  

• New sampling techniques can help design and optimize efficient and representative data collection methods for joint environmental and health studies, and provide new means for quantifying variability, bias, and greater uncertainty.   

• Advances in the co-modeling of extremes is essential to holistically model both the probability and size of rare events across the environment and human health, from fires, floods, plant and animal imbalance, and droughts to malnutrition, water and food insecurity, epidemics, and pandemics. They can further lead to the discovery of additional unrealized and unknown risks across public and environmental health.  

• Advanced mathematical modeling of the environment can help simulate dynamics and interactions of biological, physical, and chemical processes for the future environment. Tipping points can be hypothesized, and when coupled with causal models for health outcomes, the effects of different interventions and policies on diverse scenarios can be studied as robustness and resiliency is further integrated into environmental and health systems. 

About the NISS CoLab