Mathematics and Statistics Department: Internal Colloquium

The reality of climate change renders the study of environmental effects on human health more important than even. Quantifying such effects involves the analysis of meteorological, atmospheric and health data, using appropriate data models to estimate health risks as a function of environmental hazards. We present the utility of hierarchical statistical models, as a versatile tool for estimating such risks while capturing structures and uncertainty in the data. Focus is on temperature extremes and the synergistic effect from humidity and air pollution. Moreover we illustrate how estimates can be used for risk mitigation via warning systems and risk adaptation when used in conjunction with climate projections.