mortality-viz

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

Objectives

This material will enable you to reproduce most of the figures presented in Ricciato's preliminary study on 2020 mortality data from ISTAT (see below):

• notebook

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.

(Re)run

You can rerun the notebook :

• in Google colab environment (you will need a Google login): launch .
• in binder environment:

Resources and references

• 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

Objectives

This material will enable you to visualise available data on weekly excess mortality rates over available European countries (see below):

• notebook

(Re)run

You can rerun the notebook :

• in Google colab environment (you will need a Google login): launch .

Resources and references

• 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).

Usage

You will need standard Python library for data handling, e.g., pandas, numpy, matplotlib, and date manipulation, e.g., datetime, calendar (see also below). The code herein also uses the pyeudatnat package.

The environment.yml file in this directory provides with all requirements. See also the "Settings" cell of the ITmortality.py source code file.

Note

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.

About

status	since 2020
contributors
license	EUPL

Software requirements

• Packages for metadata and data fetching: pydatutils and pyeudatnat.
• Packages for time-series and dataframe handling: numpy, pandas.
• Plotting package: matplotlib.

Other references / In the news

• 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.