A parameter-free population-dynamical approach to health workforce supply forecasting of EU countries
Peter Klimek, Katharina Ledebur, Michael Gyimesi, Herwig Ostermann,, Stefan Thurner

TL;DR
This paper introduces a novel population-dynamical model for forecasting health workforce supply in EU countries, effectively capturing demographic and epidemiological changes with minimal data requirements.
Contribution
It presents a parameter-free, stock-flow-consistent approach that only needs publicly available time series data, improving forecasting accuracy over existing models.
Findings
Decreasing generalist physician density in many countries.
Increasing specialist physician density, especially in Southern and Eastern Europe.
Limited impact of COVID-19 on long-term workforce trends.
Abstract
Many countries faced challenges in their health workforce supply like impending retirement waves, negative population growth, or a suboptimal distribution of resources across medical sectors even before the pandemic struck. Current quantitative models are often of limited usability as they either require extensive individual-level data to be properly calibrated or (in the absence of such data) become too simplistic to capture key demographic changes or disruptive epidemiological shocks like the SARS-CoV-2 pandemic. We propose a novel population-dynamical and stock-flow-consistent approach to health workforce supply forecasting that is complex enough to address dynamically changing behaviors while requiring only publicly available timeseries data for complete calibration. We demonstrate the usefulness of this model by applying it to 21 European countries to forecast the supply of…
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