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Local mortality estimates during the COVID-19 pandemic in Italy

Author

Listed:
  • Augusto Cerqua

    (Sapienza Università di Roma)

  • Roberta Di Stefano

    (Sapienza Università di Roma)

  • Marco Letta

    (Sapienza Università di Roma)

  • Sara Miccoli

    (Sapienza Università di Roma)

Abstract

Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The ‘official’ approach adopted by public institutions to estimate the ‘excess mortality’ during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving prediction accuracy of local mortality in ‘ordinary’ years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and June 2020. Such estimates allow us to provide insights about the demographic evolution of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

Suggested Citation

  • Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography 2020-06, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.
  • Handle: RePEc:ahy:wpaper:wp6
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    References listed on IDEAS

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    More about this item

    Keywords

    COVID-19; coronavirus; local mortality; Italy; machine learning; counterfactual building;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I10 - Health, Education, and Welfare - - Health - - - General
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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