MCML - Modeling Cold-Related Excess Deaths via Stationary Vine Copulas

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MCML Authors

Thomas Nagler

Prof. Dr.

Core PI

Computational Statistics & Data Science

Abstract

Extreme cold temperature events have long been associated with excessmortality via many different causes of death. Climate change is expected tointensify the frequency and severity of these extreme temperature events. To quantify and model cold-related excess deaths and, in turn, to betterunderstand the potential impact of climate change on future mortality levels, we propose a new approach based on the state-of-the-art stationaryvine copulas. We adopt the S-vine model for the first time in the contextof climate-driven mortality risk, and introduce a special case of the modelto aid model comparison and enhance interpretability of the results. This model is referred to as a (stationary) centrally connected C-vine (CCC-vine). Three types of dependence are captured by the proposed models, whichare temporal dependence, contemporaneous cross-sectional dependence,and non-contemporaneous cross-sectional dependence. We fit the CCC-vine model to the US regional cause-specific death data over the period 1999–2018 and conclude that the model outperforms various benchmarkmodels including the Gaussian copula model and the VAR model. Based onthe fitted models, we generate several temperature scenarios and assesscause-specific excess deaths and overall excess deaths due to extreme coldtemperatures. We also analyze and compare the geographical differencesin cold-related excess deaths across six continental US regions. The resultsfrom our study can help public health interventions during extreme coldevents to reduce temperature-driven excess deaths.

article LNC26