https://github.com/fabnavarro/covid-clustering
Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors
https://github.com/fabnavarro/covid-clustering
clustering covid data-science statistics wavelet-decomposition
Last synced: 15 days ago
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Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors
- Host: GitHub
- URL: https://github.com/fabnavarro/covid-clustering
- Owner: fabnavarro
- Created: 2023-01-31T07:23:00.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-21T09:36:26.000Z (over 2 years ago)
- Last Synced: 2025-02-21T16:39:12.850Z (over 1 year ago)
- Topics: clustering, covid, data-science, statistics, wavelet-decomposition
- Language: R
- Homepage: https://hal.science/hal-03952739/
- Size: 18.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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).
``` bash
R CMD INSTALL icamix_1.0.6.tar.gz
```
``` bash
R CMD INSTALL denpro_0.9.2.tar.gz
```
``` bash
R CMD INSTALL regpro_0.1.1.tar.gz
```
The package and code execution require the installation of the following
external libraries:
``` r
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:
``` r
isinstall <- sapply(package_list,
function(x) x %in% rownames(installed.packages()))
package_list[isinstall]
sapply(package_list[!isinstall], install.packages)
```
Also install `Biobase`:
``` r
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:
``` bash
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
Badr et al.
(2023). The data and scripts are stored in the folder
`builddata-August2021/`.
Load `Section2.R` to reproduce Figure 1.
``` r
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`.
``` 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`.
``` 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`.
``` 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`
``` 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`
``` 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`
``` 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.