ru-covid19-regional-excess-mortality

This repository contains the supplementary materials (code, data and plots) for the paper “SPATIAL MODELLING OF KEY REGIONAL-LEVEL FACTORS OF COVID-19 MORTALITY IN RUSSIA”.

https://github.com/e-kotov/ru-covid19-regional-excess-mortality

Keywords

Repository

This repository contains the supplementary materials (code, data and plots) for the paper “SPATIAL MODELLING OF KEY REGIONAL-LEVEL FACTORS OF COVID-19 MORTALITY IN RUSSIA”.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# ru-covid19-regional-excess-mortality



Source code of the paper **Kotov, E., Goncharov, R., Kulchitsky, Y., Molodtsova, V., Nikitin, B. (2022). Spatial Modelling of Key Regional-Level Factors of COVID-19 Mortality in Russia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY, 15(2), 71–83. [https://doi.org/10.24057/2071-9388-2021-076](https://doi.org/10.24057/2071-9388-2021-076)**

Current data and code: [![DOI](https://zenodo.org/badge/471431313.svg)](https://zenodo.org/badge/latestdoi/471431313)





[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/e-kotov/ru-covid19-regional-excess-mortality/HEAD?urlpath=rstudio) to run the analysis in the cloud (due to multiple packages used in the analysis, you may have to wait about 10 minutes for the container to set up.)


You can see the published html version of the paper supplements at [https://e-kotov.github.io/ru-covid19-regional-excess-mortality/](https://e-kotov.github.io/ru-covid19-regional-excess-mortality/).

## Contents of the supplementray materials

- `/codebook/codebook.xlsx` - contains description of the variables

- `/paper` - a folder with the main script for performing the analysis

  - `paper/paper.Rmd` - the main script that can be rendered into html or pdf file
  
  - `paper/paper.html` - **the pre-rendered version of the analysis script** - this is what you want to open if you just want to read the results summary and see the plots
  
- `/R` - a folder with custom analysis functions and package installation scripts written in R

- `/data/` - data folder

  - `region_borders_with_data.gpkg` - GIS data set with all attributes and regional boundaries geometry
  
  - `region_borders_with_data.gal` - the spatial weights matrix file used for spatial autocorrelation tests and spatial modelling
  
  - `regions_data.csv` - a copy of the regions attributes in a plain-text csv file without regional boundaries geometry

- `/plots/` - folder with plots exported for the final paper

- `/summaries/` - folder with model summaries and coefficients


## Reproducing the analysis

If you want to re-run the analysis from scratch:

1. Install R https://cran.r-project.org and RStudio https://www.rstudio.com.

2. Extract all the files to a folder.

3. Open the `ru-covid19-regional-excess-mortality.Rproj` file in RStudio.

4. Open the `paper/paper.Rmd` file within RStudio file browser.

5. Press Knit button at the top to run the analysis. All packages should install automatically, the analysis should run and you should get an regenerated `paper/paper.html` file.

## ABSTRACT

Intensive socio-economic interactions are a prerequisite for the innovative development of the economy, but at the same time, they may lead to increased epidemiological risks. Persistent migration patterns, the socio-demographic composition of the population, income level, and employment structure by type of economic activity determine the intensity of socio-economic interactions and, therefore, the spread of COVID-19.

We used the excess mortality (mortality from April 2020 to February 2021 compared to the five-year mean) as an indicator of deaths caused directly and indirectly by COVID-19. Similar to some other countries, due to irregularities and discrepancies in the reported infection rates, excess mortality is currently the only available and reliable indicator of the impact of the COVID-19 pandemic in Russia.

We used the regional level data and fit regression models to identify the socio-economic factors that determined the impact of the pandemic. We used ordinary least squares as a baseline model and a selection of spatial models to account for spatial autocorrelation of dependent and independent variables as well as the error terms.

Based on the comparison of AICc (corrected Akaike information criterion) and standard error values, it was found that SEM (spatial error model) is the best option with reliably significant coefficients. Our results show that the most critical factors that increase the excess mortality are the share of the elderly population and the employment structure represented by the share of employees in manufacturing (C economic activity according to European Skills, Competences, and Occupations (ESCO) v1 classification). High humidity as a proxy for temperature and a high number of retail locations per capita reduce the excess mortality. Except for the share of the elderly, most identified factors influence the opportunities and necessities of human interaction and the associated excess mortality.

## KEY WORDS

COVID-19, spatial models, socio-economic factors, climatic factors, excess mortality, Russian regions

## FUNDING

The reported study was funded by RFBR according to the research project № 20-04-60490 "Ensuring balanced regional development during a pandemic with spatially differentiated regulation of socio-economic interaction, sectoral composition of the economy and local labour markets".

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Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  SPATIAL MODELLING OF KEY REGIONAL-LEVEL FACTORS OF COVID-19 MORTALITY IN RUSSIA: data, R code
  and plots
message: Please cite this dataset using these metadata.
type: dataset
authors:
  - given-names: Egor
    family-names: Kotov
    email: kotov.egor@gmail.com
    orcid: 'https://orcid.org/0000-0001-6690-5345'
    affiliation: HSE University

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