Building networks of hospitals through patients transfers
This R package contains functions to help interested researchers to build hospital networks from data on hospitalized patients transferred between hospitals.
The aim of the project is to provide a common framework to build and analyze hospital networks.
This project is partly supported by the NeWIS (NetWorks to Improve Surveillance) initiative, funded by JPIAMR, and by Sphinx project, funded by ANR.
You can install the release version of this package from CRAN as follows:
Or you can install the development version from GitHub: - The package
devtools needs to be installed first
This command will install the latest “released” version of the package.
checkBase()should be run first, and the resulting checked/repaired database should be used in the following step. The function checks if :
The minimal way of running this is:
checkBase(base) where base is the patient admission database. It takes the following parameters to adjust to the database in question: (default values are indicated in bold characters)
The best way the reconstruct the hospital network is creating a HospiNet object from the patient database. This object also allows for easy calculation of the network metrics as well as plotting and printing of results.
This function has a number of similar input parameters as the previous: subjectID, facilityID, disDate, admDate, verbose. Next to that, the following parameters can also be input:
The result of the reconstruction and analysis can be easily saved as an RDS file, using
since they are all stored in the HospiNet object. This object does not include the raw database, just the edge list (which is basically the same as the contact matrix), the various network metrics for each hospital, and metrics on the size of the used database (number of patients, admissions, hospitals, etc.).
Currently, the reconstructed network can be plotted as a matrix using
plot(HospiNet, type=”matrix”). This can also be done as a clustered matrix:
plot(HospiNet, type=”clustered_matrix”). In addition, you can also visualize the degree distribution of the nodes in the network with
plot(HospiNet, type = “degree”). We will try to include easy ways to plot the network in other ways as well.
install.packages("devtools") # install.packages only need to be run once # and can be commented after use library(devtools) # load the library allowing the HospitalNetwork package # download and installation install_github("PascalCrepey/HospitalNetwork@*release") # can be commented once it is installed library(HospitalNetwork) # load the HospitalNetwork library # Here, we create a dummy database for testing purposes, # final users can directly use their own database. This one looks like: # sID fID Adate Ddate # 1: s001 f09 2019-02-19 2019-02-26 # 2: s001 f10 2019-03-27 2019-03-31 # 3: s001 f09 2019-04-22 2019-04-25 # 4: s002 f08 2019-01-15 2019-01-20 # 5: s003 f11 2019-02-14 2019-02-19 # --- # 228: s098 f01 2019-02-08 2019-02-12 mydb = create_fake_subjectDB(n_subjects = 1000, n_facilities = 100) # checking the database mydb_checked = checkBase(mydb) # building the hospital network in a HospiNet object my_hosp_net = hospinet_from_subject_database(mydb_checked) # plot the network as a "contact matrix" plot(my_hosp_net) #plot the network as a "contact matrix" ordered by clusters (if any) plot(my_hosp_net, type = "clustered_matrix") # plot the degree (number of neighbors) distribution of hospitals in the network plot(my_hosp_net, type = "degree") # save the network (not the original database) saveRDS(my_hosp_net, file = "my_hosp_net.RDS")