An open-sourced, web-based application to analyze weekly excess mortality based on the Short-term Mortality Fluctuations data series
László Németh, Dmitri A. Jdanov, Vladimir M. Shkolnikov
Published: February 5, 2021
https://doi.org/10.1371/journal.pone.0246663
The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions.
The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries.
The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.
Live @ https://mpidr.shinyapps.io/stmortality/
Data descriptor article:
The short-term mortality fluctuation data series, monitoring mortality shocks across time and space Dmitri A. Jdanov, Ainhoa Alustiza Galarza, Vladimir M. Shkolnikov, Domantas Jasilionis, László Németh, David A. Leon, Carl Boe, and Magali Barbieri
Scientific Data volume 8, Article number: 235 (2021)
https://doi.org/10.1038/s41597-021-01019-1
This online data repository was supported by the Volkswagen Foundation (VolkswagenStiftung) in the framework of the supported project on "Strengthening a reliable evidence base for monitoring the COVID-19 and other disasters".