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Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors

The codes in this allow to reproduce the simulations presented in Cheam et al. (2023) https://hal.science/hal-03952739/.

Installation

The packages icamix, denpro and regpro, need to be installed from the source (because they were removed from the CRAN repository).

R CMD INSTALL icamix_1.0.6.tar.gz
R CMD INSTALL denpro_0.9.2.tar.gz
R CMD INSTALL regpro_0.1.1.tar.gz

The package and code execution require the installation of the following external libraries:

package_list <- c("regpro",
                  "forcats",
                  "latex2exp",
                  "ggplot2",
                  "fda",
                  "mixtools",
                  "HDclassif",
                  "VGAM",
                  "rwavelet",
                  "BiocManager",
                  "dtw",
                  "NMF",
                  "sClust",
                  "geoR",
                  "zoo",
                  "stringr",
                  "FactoMineR",
                  "xtable",
                  "gridExtra",
                  "dplyr",
                  "MASS",
                  "VarSelLCM")

Install missing packages:

isinstall <- sapply(package_list, 
                    function(x) x %in% rownames(installed.packages()))
package_list[isinstall]
sapply(package_list[!isinstall], install.packages)

Also install Biobase:

BiocManager::install("Biobase")

The codes are based on the Clustfun package, companion of the paper Cheam et al. (2023). To install the package, execute the following command in a terminal:

R CMD INSTALL --build Clustfun_1.0.0.tar.gz

Reproduction of figures and tables

Below, all the information are gathered to reproduce the numerical results presented in the paper.

Section 2 Description of the data

The dataset (COVIDfull.rda) was built from the files COVID-19_LUT.csv and Policy.rds downloaded from https://github.com/CSSEGISandData/COVID-19_Unified-Dataset Badr et al. (2023). The data and scripts are stored in the folder builddata-August2021/.

Load Section2.R to reproduce Figure 1.

source("Section2.r")

Section 4.1 Investigating the strengths of the proposed approach

Load Section4_1plot.R to reproduce Figures 2–4. The results are stored in resultsSection4_1.rda and can be re-executed running Section4_1run.R.

source("Section4_1plot.r")

Section 4.2 Comparing the proposed approach with other methods used for COVID-19 studies

Load Section4_2plot.R to reproduce Figures 5–6. The results are stored in resultsSection4_2.rda and can be executed running Section4_2run.R.

source("Section4_2plot.r")

Section 4.3 Investigating the robustness of the proposed approach

Load Section4_3plot.R to reproduce Figures 7–8. The results are stored in resultsSection4_3.rda and can be executed running Section4_3run.R.

source("Section4_3plot.r")

Investigating geographical disparities for COVID-19

Section 5.1 Population risk factors

The results of this section can be reproduced by running Section5-1.R

source("Section5-1.r")

1 2 3 4 5 6
PM2.5_PopWtd -0.5664122 0.0212741 -0.4345034 0.0000121 0.1993832 0.0540271
NO2_PopWtd 0.7958363 0.3504402 0.3072890 0.0025897 0.0413344 0.6924301
WorldPop_Density 0.5089838 0.0000000 -0.0061600 0.9530119 -0.3229638 0.0014989
Diabetes 0.2816515 0.4229080 0.7646337 0.0000000 -0.4502616 0.0000053
Obesity 0.3336598 0.0466550 0.8527638 0.0000000 -0.1532284 0.1403605
Smoking -0.2871324 0.1308322 -0.0830285 0.4262674 0.8082869 0.0000000
COPD 0.4248760 0.0081631 0.7307828 0.0000000 0.1981437 0.0555693
CVD 0.3014341 0.5895446 0.8457820 0.0000000 0.0678408 0.5158928
HIV 0.3708583 0.0000000 0.2717158 0.0080689 -0.6904543 0.0000000
Hypertension 0.1964048 0.7902244 0.8214016 0.0000000 0.3441937 0.0006817
WorldPop_65 0.0528298 0.0438759 0.2901739 0.0045538 0.8435328 0.0000000

Table 2

Section 5.2 Clustering of the regions

The results of this section can be reproduced by running Section5-2.R

source("Section5-2.r")

1 2 3 4 5 6 7 8 9 10
1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000
-1377.804 -1237.954 -1187.733 -1136.491 -1114.798 -1100.366 -1065.731 -1054.002 -1055.569 -1034.302

Table 3

1 2 3 4
338.9082 387.0266 585.7333 1350.0617
387.0266 371.4739 534.7584 1185.9924
585.7333 534.7584 549.9975 958.6426
1350.0617 1185.9924 958.6426 1084.5153

Table 4

1 2 3 4 5 6 7
0.1407480 860.0971 397.0239 958.8668 638.6910 1.0105156 0.4079440
0.3312190 1030.5490 414.8785 1219.6178 711.3828 1.1112846 0.3566133
0.2196141 1463.0474 364.7504 1736.1461 350.8012 1.2270311 0.2840139
0.3084189 2532.9712 961.5437 2253.9434 888.8210 0.9426568 0.3233362

Table 5

Section 5.3 Clusters analysis example: disparities and policy decisions

The results of this section can be reproduced by running Section5-3.R

source("Section5-3.r")

References

Badr, Hamada S., Benjamin F. Zaitchik, Gaige H. Kerr, Nhat-Lan H. Nguyen, Yen-Ting Chen, Patrick Hinson, Josh M. Colston, et al. 2023. “Unified Real-Time Environmental-Epidemiological Data for Multiscale Modeling of the COVID-19 Pandemic.” Scientific Data 10 (1): 367.

Cheam, Amay, Marc Fredette, Matthieu Marbac, and Fabien Navarro. 2023. “Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors.” Journal of the Royal Statistical Society Series C: Applied Statistics 72 (2): 387–413.

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