  Using fine-grained, publicly available data, this paper studies the
association between environmental factors, i.e., variables capturing weather
and air pollution characteristics, and weekly mortality rates in small
geographical regions in Europe. Hereto, we develop a mortality modelling
framework where a baseline captures a region-specific, seasonal historical
trend observed within the weekly mortality rates. Using a machine learning
algorithm, we then explain deviations from this baseline using anomalies and
extreme indices constructed from the environmental data. We illustrate our
proposed modelling framework through a case study on more than 550 NUTS 3
regions (Nomenclature of Territorial Units for Statistics, level 3) located in
20 different European countries. Through interpretation tools, we unravel
insights into which environmental features are most important when estimating
excess or deficit mortality with respect to the baseline and explore how these
features interact. Moreover, we investigate harvesting effects of the
environmental features through our constructed weekly mortality modelling
framework. Our findings show that temperature-related features exert the most
significant influence in explaining deviations in mortality from the baseline.
Furthermore, we find that environmental features prove particularly beneficial
in southern regions for explaining elevated levels of mortality over short time
periods.
