Basic descriptive statistics on mortality data
The present project provides with some visualisation/analyses on data on 2020 mortality from various (national authoritative) sources based on availability.
1. Early mortality data from ISTAT
The "Data resources" section below references the data used for the study (freely available for download). Note that the results and figures presented in Ricciato's document refer to the data downloaded at the time of the study, hence on April, 16th.
You can rerun the notebook :
- Ricciato F. (2020): A preliminary view at 2020 mortality data from ISTAT, last updated on April 16th.
- Mortality data collected by ISTAT: "dati di mortalita", including micro datasets with daily death counts: "dataset analitico con i decessi giornalieri" and aggregated datasets with weekly death counts "dataset sintetico con i decessi per settimana".
- "Confini delle unita amministrative a fini statistici al 1o gennaio 2020".
- "Codici statistici delle unita amministrative territoriali: comuni, citta metropolitane, province e regioni".
- Identification of cities/municipalities: "Elenco dei communi".
- Timeline of deaths in IT: "L'andamento dei decessi del 2020".
- Visualisations of deaths in IT cities and provinces published by ISTAT (using the same data): "Andamento dei decessi".
2. Weekly excess mortality rates over Europe
This material will enable you to visualise available data on weekly excess mortality rates over available European countries (see below):
You can rerun the notebook :
Google colabenvironment (you will need a Google login): launch .
- Eurostat data on "Deaths by age group, sex, week and NUTS 3 region": demo_r_mweek3.
- Eurostat geographical data on regional units NUTS 2016 (see GISCO website).
You will need standard
Python library for data handling, e.g.,
matplotlib, and date manipulation, e.g.,
calendar (see also below). The code herein also uses the
environment.yml file in this directory provides with all requirements. See also the "Settings" cell of the
ITmortality.py source code file.
Most of the data used in the provided notebooks will fetch data from the original sources directly. Therefore, when (re)running the notebook, the results and figures you generate always refer to the latest available (possibly updated) data.
- Packages for metadata and data fetching:
- Packages for time-series and dataframe handling:
- Plotting package:
- Committee for the Coordination of Statistical Activities (2020): How COVID-19is changing the world: a statistical perspective
- INSEE discussion blog on mortality data and their interpretation (2020): "Mode d'emploi des donnees de l'INSEE".
- Study of COVID-19 over mortality by ISTAT(2020): Impatto dell'epidemia COVID-19 sulla mortalita totale della popolazione residente primo trimestre 2020
- Coronavirus: La luce in fondo al tunnel, neodemos, published on March 30th.
- Global coronavirus death toll could be 60% higher than reported, Financial Times, published on April 26th.
- Tracking covid-19 excess deaths across countries, The Economist, published on April 16th. https://mtmx.github.io/blog/deces_pandemie/
- An unprecedented context of health crisis, INED. Comparison of the COVID-19 pandemics in 6 countries, data available here.
- Timeline of deaths in FR regions: Deces pendant la pandemie.