Mortality Forecasting Under Climate Risk: A Stochastic Approach With Distributed Lag Nonlinear Models

MCML Authors

Han Li

Dr.

→ Group Nassir Navab
 Computer Aided Medical Procedures & Augmented Reality

Thomas Nagler

Prof. Dr.

Core PI

Computational Statistics & Data Science

Abstract

Assessing climate-driven mortality risk has become an emerging area of research in recent decades. In this article, we propose a novel approach to explicitly incorporate climate-driven effects into single- and multipopulation stochastic mortality models. The model consists of two components: a stochastic mortality model, and a distributed lag nonlinear model (DLNM). The stochastic component captures nonclimate long-term trend, volatility, and seasonal patterns in mortality rates. The DLNM component captures nonlinear and lagged effects of climate variables on mortality, and the impact of heat waves and cold waves across age groups. For model calibration, we propose a backfitting algorithm that disentangles climate-driven mortality risk from nonclimate-driven stochastic mortality risk. We demonstrate improved short-term (1–18 months) forecasting performance against four alternative models, using data from Athens, Lisbon, and Rome. As an application of the proposed models, we utilize future UTCI data to provide mortality forecasts under two representative concentration pathway (RCP) scenarios, accounting for both stochastic mortality improvement and climate risk. Results show declining winter mortality and rising summer mortality. Although we expect slightly lower overall mortality in the short term under RCP8.5 than RCP2.6, a long-term increase in mortality is anticipated under RCP8.5.

article MLN+26a

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Journal of the Royal Statistical Society

Series A (Statistics in Society) qnag082. Jul. 2026.

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Authors

J. Min • H. Li • T. Nagler • S. Li

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Links

DOI GitHub

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Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine