A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic

COVID-19 related deaths estimates underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares the observed number of deaths versus the number that would be expected if the pandemic did not occur. The expected number of deaths depends on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a workflow using R for estimating and visualising excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed workflow is fast to implement and allows for combining different models and presenting aggregated results based on factors such as age, sex, and spatial location. This makes it a particularly powerful and appealing workflow for online monitoring of the pandemic burden and timely policy making.

Garyfallos Konstantinoudis (MRC Centre for Environment and Health, Imperial College London,) , Virgilio Gómez-Rubio (Departamento de Matemáticas, Escuela Técnica Superior de Ingenier\(\acute{\imath}\)a Industrial-Albacete, Universidad de Castilla-La Mancha,) , Michela Cameletti (Department of Economics, University of Bergamo,) , Monica Pirani (MRC Centre for Environment and Health, Imperial College London,) , Gianluca Baio (Department of Statistical Sciences, University College London,) , Marta Blangiardo (MRC Centre for Environment and Health, Imperial College London,)

0.1 Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-055.zip

J. M. Aburto, R. Kashyap, J. Schöley, C. Angus, J. Ermisch, M. C. Mills and J. B. Dowd. Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: A population-level analysis. J Epidemiol Community Health, 2021.
T. Appelhans and F. Detsch. Leafpop: Include tables, images and graphs in leaflet pop-ups. 2021. URL https://CRAN.R-project.org/package=leafpop. R package version 0.1.0.
D. Attali and T. Edwards. Shinyalert: Easily create pretty popup messages (modals) in ’shiny’. 2021. URL https://CRAN.R-project.org/package=shinyalert. R package version 3.0.0.
J. Besag. Spatial interactions and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society: Series B, 36: 192–236, 1974.
J. Besag, J. York and A. Mollié. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1): 1–20, 1991.
R. Bivand. R packages for analyzing spatial data: A comparative case study with areal data. 2022. DOI 10.1111/gean.12319.
R. Bivand, T. Keitt and B. Rowlingson. Rgdal: Bindings for the ’geospatial’ data abstraction library. 2023. URL https://CRAN.R-project.org/package=rgdal. R package version 1.6-4.
M. A. P. Blangiardo Marta AND Cameletti. Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic. PLOS ONE, 15(10): 1–15, 2020. URL https://doi.org/10.1371/journal.pone.0240286.
W. Chang and B. Borges Ribeiro. Shinydashboard: Create dashboards with ’shiny’. 2021. URL https://CRAN.R-project.org/package=shinydashboard. R package version 0.7.2.
W. Chang, J. Cheng, J. Allaire, C. Sievert, B. Schloerke, Y. Xie, J. Allen, J. McPherson, A. Dipert and B. Borges. Shiny: Web application framework for r. 2022. URL https://CRAN.R-project.org/package=shiny. R package version 1.7.4.
B. Davies, B. L. Parkes, J. Bennett, D. Fecht, M. Blangiardo, M. Ezzati and P. Elliott. Community factors and excess mortality in first wave of the COVID-19 pandemic in England. Nature Communications, 12(1): 1–9, 2021.
M. Dowle and A. Srinivasan. Data.table: Extension of ‘data.frame‘. 2022. URL https://CRAN.R-project.org/package=data.table. R package version 1.14.6.
S. Garnier, N. Ross, R. Rudis, P. A. Camargo, M. Sciaini and C. Scherer. viridis - Colorblind-friendly color maps for r. 2021. URL https://sjmgarnier.github.io/viridis/. R package version 0.6.2.
G. Grolemund and H. Wickham. Dates and times made easy with lubridate. 2011. URL https://www.jstatsoft.org/v40/i03/.
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049, 2020.
R. J. Hijmans. Raster: Geographic data analysis and modeling. 2023. URL https://CRAN.R-project.org/package=raster. R package version 3.6-14.
K. Hufkens, R. Stauffer and E. Campitelli. The ecwmfr package: An interface to ECMWF API endpoints. 2019. URL https://bluegreen-labs.github.io/ecmwfr/.
N. Islam, V. M. Shkolnikov, R. J. Acosta, I. Klimkin, I. Kawachi, R. A. Irizarry, G. Alicandro, K. Khunti, T. Yates, D. A. Jdanov, et al. Excess deaths associated with COVID-19 pandemic in 2020: Age and sex disaggregated time series analysis in 29 high income countries. BMJ, 373: 2021.
J. Kaczorowski and C. Del Grande. Beyond the tip of the iceberg: Direct and indirect effects of COVID-19. The Lancet Digital Health, 3(4): e205–e206, 2021.
G. Konstantinoudis, M. Cameletti, V. Gómez-Rubio, I. L. Gómez, M. Pirani, G. Baio, A. Larrauri, J. Riou, M. Egger, P. Vineis, et al. Regional excess mortality during the 2020 COVID-19 pandemic in five European countries. Nature communications, 13(1): 482, 2022.
G. Konstantinoudis, A. Hauser and J. Riou. Bayesian ensemble modelling to monitor excess deaths during summer 2022 in Switzerland. arXiv preprint arXiv:2308.15251, 2023.
G. Konstantinoudis, D. Schuhmacher, H. Rue and B. D. Spycher. Discrete versus continuous domain models for disease mapping. Spatial and Spatio-temporal Epidemiology, 32: 100319, 2020. URL https://www.sciencedirect.com/science/article/pii/S1877584519301297.
V. Kontis, J. E. Bennett, T. Rashid, R. M. Parks, J. Pearson-Stuttard, M. Guillot, P. Asaria, B. Zhou, M. Battaglini, G. Corsetti, et al. Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries. Nature Medicine, 1–10, 2020.
V. Kontis, J. Bennett, R. Parks, T. Rashid, J. Pearson-Stuttard, P. Asaria, B. Zhou, M. Guillot, C. Mathers, Y. Khang, et al. Lessons learned and lessons missed: Impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in 40 industrialised countries prior to mass vaccination [version 1; peer review: 2 approved with reservations]. Wellcome Open Research, 6(279): 2021. DOI 10.12688/wellcomeopenres.17253.1.
E. Kontopantelis, M. A. Mamas, J. Deanfield, M. Asaria and T. Doran. Excess mortality in England and Wales during the first wave of the COVID-19 pandemic. J Epidemiol Community Health, 75(3): 213–223, 2021.
P. Moraga. Small Area Disease Risk Estimation and Visualization Using R. The R Journal, 10(1): 495–506, 2018. URL https://doi.org/10.32614/RJ-2018-036.
E. Neuwirth. RColorBrewer: ColorBrewer palettes. 2022. URL https://CRAN.R-project.org/package=RColorBrewer. R package version 1.1-3.
R. M. Parks, J. Benavides, G. B. Anderson, R. C. Nethery, A. Navas-Acien, F. Dominici, M. Ezzati and M.-A. Kioumourtzoglou. Association of tropical cyclones with county-level mortality in the US. JAMA, 327(10): 946–955, 2022.
E. Pebesma. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10(1): 439–446, 2018. URL https://doi.org/10.32614/RJ-2018-009.
T. L. Pedersen. Patchwork: The composer of plots. 2022. URL https://CRAN.R-project.org/package=patchwork. R package version 1.1.2.
D. Pierce. ncdf4: Interface to unidata netCDF (version 4 or earlier) format data files. 2023. URL https://CRAN.R-project.org/package=ncdf4. R package version 1.21.
T. Plate and R. Heiberger. Abind: Combine multidimensional arrays. 2016. URL https://CRAN.R-project.org/package=abind. R package version 1.4-5.
A. Riebler, S. H. Sørbye, D. Simpson and H. Rue. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4): 1145–1165, 2016.
J. Riou, A. Hauser, A. Fesser, C. L. Althaus, M. Egger and G. Konstantinoudis. Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland. Nature communications, 14(1): 90, 2023.
L. M. Rossen, A. M. Branum, F. B. Ahmad, P. Sutton and R. N. Anderson. Excess deaths associated with COVID-19, by age and race and ethnicity – United States, January 26–October 3, 2020. Morbidity and Mortality Weekly Report, 69(42): 1522, 2020.
H. Rue and L. Held. Gaussian Markov random fields: Theory and applications. CRC press, 2005.
H. Rue, S. Martino and N. Chopin. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (statistical methodology), 71(2): 319–392, 2009.
D. Simpson, H. Rue, A. Riebler, T. G. Martins, S. H. Sørbye, et al. Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1): 1–28, 2017.
J. Verbeeck, C. Faes, T. Neyens, N. Hens, G. Verbeke, P. Deboosere and G. Molenberghs. A linear mixed model to estimate COVID-19-induced excess mortality. Biometrics, 1–9, 2021. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13578.
L. A. Waller, B. P. Carlin, H. Xia and A. E. Gelfand. Hierarchical spatio-temporal mapping of disease rates. Journal of the American Statistical association, 92(438): 607–617, 1997.
D. M. Weinberger, J. Chen, T. Cohen, F. W. Crawford, F. Mostashari, D. Olson, V. E. Pitzer, N. G. Reich, M. Russi, L. Simonsen, et al. Estimation of excess deaths associated with the COVID-19 pandemic in the United States, March to May 2020. JAMA Internal Medicine, 180(10): 1336–1344, 2020.
H. Wickham. ggplot2: Elegant graphics for data analysis. Springer-Verlag New York, 2016. URL https://ggplot2.tidyverse.org.
H. Wickham. Reshaping data with the reshape package. 2007. URL http://www.jstatsoft.org/v21/i12/.
H. Wickham. Stringr: Simple, consistent wrappers for common string operations. 2022. URL https://CRAN.R-project.org/package=stringr. R package version 1.5.0.
H. Wickham, R. François, L. Henry, K. Müller and D. Vaughan. Dplyr: A grammar of data manipulation. 2023a. URL https://CRAN.R-project.org/package=dplyr. R package version 1.1.0.
H. Wickham, J. Hester and J. Bryan. Readr: Read rectangular text data. 2022. URL https://CRAN.R-project.org/package=readr. R package version 2.1.3.
H. Wickham, D. Vaughan and M. Girlich. Tidyr: Tidy messy data. 2023b. URL https://CRAN.R-project.org/package=tidyr. R package version 1.3.0.
D. Wuertz, T. Setz, Y. Chalabi and G. N. Boshnakov. timeDate: Rmetrics - chronological and calendar objects. 2023. URL https://CRAN.R-project.org/package=timeDate. R package version 4022.108.



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Konstantinoudis, et al., "A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic", The R Journal, 2023

BibTeX citation

  author = {Konstantinoudis, Garyfallos and Gómez-Rubio, Virgilio and Cameletti, Michela and Pirani, Monica and Baio, Gianluca and Blangiardo, Marta},
  title = {A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-055},
  doi = {10.32614/RJ-2023-055},
  volume = {15},
  issue = {2},
  issn = {2073-4859},
  pages = {89-104}