README.md
In PascalCrepey/HospiCoV: HospiCoV
COVID-19 epidemic impact on healthcare resources
This R package contains functions to help interested researchers to estimate hospital resources required to treat patients infected by SARS-CoV-2.
This project is partly supported by the SPHINx project, funded by French National Research Agency.
Step 0: Installing the package:
For MacOSX and linux users
- To install packages from GitHub, the package “devtools” needs to be installed first. Then install the package from GitHub. Update or install all the required packages.
install.packages("devtools")
devtools::install_github("PascalCrepey/HospiCoV@*release")
This will install the latest "released" version of the package.
For Windows users
Download windows binary package.
Ensure that the zip package is in your working directory, then:
install.packages("hospicov_0.2.1.zip", repos = NULL, type = "win.binary")
Step 1: Preparing the data
To use run the model for a specific country, you must provide the following inputs:
- Pre-infected table: a table with a number of infected cases by location (it can be a unique location)
- Population table: a table of the age-stratified population for each location. The population must be stratified into the same 17 age groups as the contact matrix.
Age-specific contact matrices are made available for 152 countries by Prem et al.
The tables must follow a specific format for the model to run:
- Pre-infected table must have three columns (case sensitive):
- "Region": a name of region, or location
- "Date": must be identical for all rows, and in ISO-8601 format: "YYYY-MM-DD" (e.g. 2020-03-10)
- "preInfected": an integer, the number of infected cases.
|Region |Date | preInfected|
|:-----------------------|:----------|-----------:|
|Auvergne-Rhone-Alpes |2020-03-10 | 211|
|Bourgogne-Franche-Comte |2020-03-10 | 146|
|Bretagne |2020-03-10 | 81|
- Population table must have 18 columns:
- one column "Region", which matches the column "Region" of the Pre-infected table
- 17 columns corresponding to the 17 age groups below, with the corresponding population for each region
Age groups: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80P
|Region | 0-4| 5-9| 10-14| 15-19| 20-24| 25-29| 30-34| 35-39| 40-44| 45-49| 50-54| 55-59| 60-64| 65-69| 70-74| 75-79| 80P|
|:-----------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|Auvergne-Rhone-Alpes | 457173.55| 479982.02| 473564.70| 446192.66| 451363.51| 479052.66| 480439.02| 514600.38| 527126.18| 514953.9| 480081.26| 484827.77| 459145.57| 438335.49| 316475.54| 255891.89| 463162.94|
|Bourgogne-Franche-Comte | 170161.14| 193332.63| 186968.52| 157957.75| 165824.19| 178446.31| 180879.02| 200356.16| 212177.87| 213846.9| 210669.37| 188609.66| 210515.08| 201724.68| 139177.96| 117005.82| 215064.96|
|Bretagne | 182318.23| 211639.94| 208746.84| 177964.79| 172585.84| 188743.31| 198296.36| 221251.36| 226498.73| 225150.0| 222865.06| 204764.17| 222899.55| 217450.95| 145081.51| 126929.06| 227832.33|
Step 2: run shiny app
library(hospicov)
shiny_app()
Publication
Massonnaud C, Roux J, Crépey P. COVID-19: Forecasting short term hospital needs in France.
2020. PredictedFrenchHospitNeeds-EHESP-20200316.pdf.
PascalCrepey/HospiCoV documentation built on April 11, 2020, 3:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
COVID-19 epidemic impact on healthcare resources
This R package contains functions to help interested researchers to estimate hospital resources required to treat patients infected by SARS-CoV-2.
This project is partly supported by the SPHINx project, funded by French National Research Agency.
Step 0: Installing the package:
For MacOSX and linux users
- To install packages from GitHub, the package “devtools” needs to be installed first. Then install the package from GitHub. Update or install all the required packages.
install.packages("devtools")
devtools::install_github("PascalCrepey/HospiCoV@*release")
This will install the latest "released" version of the package.
For Windows users
Download windows binary package. Ensure that the zip package is in your working directory, then:
install.packages("hospicov_0.2.1.zip", repos = NULL, type = "win.binary")
Step 1: Preparing the data
To use run the model for a specific country, you must provide the following inputs:
- Pre-infected table: a table with a number of infected cases by location (it can be a unique location)
- Population table: a table of the age-stratified population for each location. The population must be stratified into the same 17 age groups as the contact matrix.
Age-specific contact matrices are made available for 152 countries by Prem et al.
The tables must follow a specific format for the model to run:
- Pre-infected table must have three columns (case sensitive):
- "Region": a name of region, or location
- "Date": must be identical for all rows, and in ISO-8601 format: "YYYY-MM-DD" (e.g. 2020-03-10)
- "preInfected": an integer, the number of infected cases.
|Region |Date | preInfected| |:-----------------------|:----------|-----------:| |Auvergne-Rhone-Alpes |2020-03-10 | 211| |Bourgogne-Franche-Comte |2020-03-10 | 146| |Bretagne |2020-03-10 | 81|
- Population table must have 18 columns:
- one column "Region", which matches the column "Region" of the Pre-infected table
- 17 columns corresponding to the 17 age groups below, with the corresponding population for each region
Age groups: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80P
|Region | 0-4| 5-9| 10-14| 15-19| 20-24| 25-29| 30-34| 35-39| 40-44| 45-49| 50-54| 55-59| 60-64| 65-69| 70-74| 75-79| 80P| |:-----------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:| |Auvergne-Rhone-Alpes | 457173.55| 479982.02| 473564.70| 446192.66| 451363.51| 479052.66| 480439.02| 514600.38| 527126.18| 514953.9| 480081.26| 484827.77| 459145.57| 438335.49| 316475.54| 255891.89| 463162.94| |Bourgogne-Franche-Comte | 170161.14| 193332.63| 186968.52| 157957.75| 165824.19| 178446.31| 180879.02| 200356.16| 212177.87| 213846.9| 210669.37| 188609.66| 210515.08| 201724.68| 139177.96| 117005.82| 215064.96| |Bretagne | 182318.23| 211639.94| 208746.84| 177964.79| 172585.84| 188743.31| 198296.36| 221251.36| 226498.73| 225150.0| 222865.06| 204764.17| 222899.55| 217450.95| 145081.51| 126929.06| 227832.33|
Step 2: run shiny app
library(hospicov)
shiny_app()
Publication
Massonnaud C, Roux J, Crépey P. COVID-19: Forecasting short term hospital needs in France. 2020. PredictedFrenchHospitNeeds-EHESP-20200316.pdf.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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