As climate continues to change, scientists are left to analyze the effects these changes will have on the public. In this article, a flexible class of distributed lag models are used to analyze the effects of heat on mortality in four major metropolitan areas in the U.S. (Chicago, Dallas, Los Angeles, and New York). Specifically, the proposed methodology uses Gaussian processes as a prior model for the distributed lag function. Gaussian processes are adequately flexible to capture a wide variety of distributed lag functions while ensuring smoothness properties of process realizations. Additionally, the proposed framework allows for probabilistic inference of the maximum lag. Applying the proposed methodology revealed that mortality displacement (or, harvesting) was present for most age groups and cities analyzed suggesting that heat advanced death in some individuals. Additionally, the estimated shape of the DL functions gave evidence that prolonged heat exposure and highly variable temperatures pose a threat to public health.

中文翻译：

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使用随机函数的灵活分布式滞后模型，用于估计与热相关的死亡的死亡率位移。

随着气候持续变化，科学家们不得不分析这些变化将对公众产生的影响。在本文中，使用一类灵活的分布式滞后模型来分析美国四个主要大都市区（芝加哥、达拉斯、洛杉矶和纽约）中高温对死亡率的影响。具体而言，所提出的方法使用高斯过程作为分布式滞后函数的先验模型。高斯过程足够灵活，可以捕获各种分布式滞后函数，同时确保过程实现的平滑性。此外，所提出的框架允许对最大滞后进行概率推断。应用所提议的方法表明死亡率转移（或，收获）存在于大多数年龄组和分析的城市，这表明高温会导致某些人死亡。此外，DL 函数的估计形状表明长时间暴露在高温和高度可变的温度对公共健康构成威胁。