The data in this example in files named
Admissions data shows all the admission to the facility in the entire run, as well as whether or not the patient admitted imported the disease.
After some iterations of the simulation, the last row of data in this file is not complete. If you see errors when you read in the data, check the .txt file and, if necessary, delete the last record.
# Read admissions.txt and show descriptive info and stats
# Read admissions.txt
admissions_df <- read.table('../data/admissions.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)
# Show structure
cat('Admissions data:')Admissions data:head(admissions_df) time patientid importation
1 -1.00 75 false
2 0.00 76 false
3 0.15 77 false
4 0.25 78 false
5 0.70 79 false
6 0.96 80 falseThis dataset shows the time and patient id of a detection via the clinical (symptoms-base) route. DetectionCount is a check column. In some edge cases a patient may be detected, treated, recovered and detected again, in which case detection count for that patient would be >1.
#read clinical_detections and give descriptive stats.
clinical_detections_df <- read.table('../data/clinicalDetection.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)
#show sample of rows:
head(clinical_detections_df) Time DetectedPatientID DetectionCount
1 2.66 83 1
2 24.98 107 1
3 26.09 131 1
4 28.37 116 1
5 29.24 139 1
6 33.95 96 1#show descriptive stats
summary(clinical_detections_df) Time DetectedPatientID DetectionCount
Min. : 2.66 Min. : 83 Min. :1
1st Qu.:1038.26 1st Qu.: 3795 1st Qu.:1
Median :2262.56 Median : 8179 Median :1
Mean :2363.93 Mean : 8462 Mean :1
3rd Qu.:3457.09 3rd Qu.:12349 3rd Qu.:1
Max. :5464.87 Max. :19628 Max. :1 The output file daily_population_stats.txt shows the total number of patients in the facility on a given day, the number of those who are colonized, detected and isolation. By default, it starts at the end of the burn-in period. There is another sanity check embedded in this file; it should always be the case that detected == isolated. We isolate detected patients, and we don’t isolate anybody else.
# Read daily_population_stats.txt (comma-delimited)
daily_pop_df <- read.table('../data/daily_population_stats.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)
# Show structure and summary
cat('Daily population stats:')Daily population stats:head(daily_pop_df) time total_population colonized detected isolated
1 3651 99 44 7 7
2 3651 99 44 7 7
3 3652 100 43 7 7
4 3652 100 43 7 7
5 3653 102 45 7 7
6 3653 102 45 7 7cat('\nSummary statistics:')
Summary statistics:summary(daily_pop_df) time total_population colonized detected
Min. :3651 Min. : 71.0 Min. :15.00 Min. : 4.00
1st Qu.:4036 1st Qu.: 90.0 1st Qu.:28.00 1st Qu.:14.00
Median :4422 Median : 96.0 Median :32.00 Median :18.00
Mean :4422 Mean : 96.2 Mean :32.68 Mean :17.64
3rd Qu.:4807 3rd Qu.:102.0 3rd Qu.:38.00 3rd Qu.:21.00
Max. :5193 Max. :126.0 Max. :54.00 Max. :35.00
isolated
Min. : 4.00
1st Qu.:14.00
Median :18.00
Mean :17.64
3rd Qu.:21.00
Max. :35.00 # Time-series plot: population, colonized, detected vs time
library(ggplot2)
# Assume columns: time, Total Population, Colonized, Detected
ggplot(daily_pop_df, aes(x = time)) +
geom_line(aes(y = `total_population`, color = 'Population')) +
geom_line(aes(y = colonized, color = 'Colonized')) +
geom_line(aes(y = detected, color = 'Detected')) +
labs(title = 'Daily Population Time Series', x = 'Time', y = 'Count', color = 'Legend') +
theme_minimal()
These are the distribution of daily samples of the total population of the sim, the colonized and detected counts.
# Histogram of Total Population
ggplot(daily_pop_df, aes(x = total_population)) +
geom_histogram(binwidth = 1, fill = 'skyblue', color = 'black') +
labs(title = 'Histogram of Total Population', x = 'total_population', y = 'Frequency') +
theme_minimal()
# Histogram of Colonized
ggplot(daily_pop_df, aes(x = colonized)) +
geom_histogram(binwidth = 1, fill = 'orange', color = 'black') +
labs(title = 'Histogram of Colonized', x = 'colonized', y = 'Frequency') +
theme_minimal()
# Histogram of Detected
ggplot(daily_pop_df, aes(x = detected)) +
geom_histogram(binwidth = 1, fill = 'purple', color = 'black') +
labs(title = 'Histogram of Detected', x = 'detected', y = 'Frequency') +
theme_minimal()
These represent patients who’s colonization with the organism has ceased.
# Read decolonization.demo.txt (comma-delimited)
decolonization_df <- read.table('../data/decolonization.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)
# Show structure and summary
cat('Decolonization events:')Decolonization events:head(decolonization_df) time decolonized_patient_id
1 14.72 78
2 23.87 112
3 28.40 124
4 30.08 110
5 30.40 125
6 45.75 100cat('\nSummary statistics:')
Summary statistics:summary(decolonization_df) time decolonized_patient_id
Min. : 14.72 Min. : 78
1st Qu.:1307.01 1st Qu.: 4530
Median :2695.80 Median : 9473
Mean :2654.49 Mean : 9431
3rd Qu.:3913.38 3rd Qu.:13925
Max. :5465.38 Max. :19624 # Read detection_verification.demo.txt (comma-delimited)
detection_verification_df <- read.table('../data/detection_verification.demo.txt', header=TRUE, sep=',', stringsAsFactors=TRUE)
# Show structure and summary
cat('Detection verification events:')Detection verification events:head(detection_verification_df) time patient_id source colonized detection_count
1 10.12 79 CLINICAL true 1
2 10.99 88 CLINICAL true 1
3 17.79 130 CLINICAL true 1
4 26.91 140 CLINICAL true 1
5 27.87 118 CLINICAL true 1
6 33.23 137 CLINICAL true 1cat('\nSummary statistics:')
Summary statistics:summary(detection_verification_df) time patient_id source colonized
Min. : 10.12 Min. : 79 CLINICAL :1266 true:2913
1st Qu.:2673.47 1st Qu.: 9480 SURVEILLANCE:1647
Median :4056.75 Median :14263
Mean :3622.80 Mean :12767
3rd Qu.:4791.95 3rd Qu.:16841
Max. :5474.36 Max. :19347
detection_count
Min. :0.0000
1st Qu.:0.0000
Median :0.0000
Mean :0.4346
3rd Qu.:1.0000
Max. :1.0000 This is every surveillance test run after the end of the burn-in period.
# Read surveillance.demo.txt (comma-delimited)
surveillance_df <- read.table('../data/surveillance.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)
# Show structure and summary
cat('Surveillance events:')Surveillance events:head(surveillance_df) Time Patient Colonized Detected
1 3664.08 13233 true true
2 3664.24 13234 false false
3 3664.61 13235 true true
4 3664.68 13236 false false
5 3664.94 13237 true true
6 3664.94 13238 false falsecat('\nSummary statistics:')
Summary statistics:summary(surveillance_df) Time Patient Colonized Detected
Min. :3664 Min. :13233 Length:7106 Length:7106
1st Qu.:4081 1st Qu.:14701 Class :character Class :character
Median :4481 Median :16148 Mode :character Mode :character
Mean :4491 Mean :16163
3rd Qu.:4901 3rd Qu.:17614
Max. :5335 Max. :19150 # Read transmission.demo.txt (comma-delimited)
transmission_df <- read.table('../data/transmissions.demo.txt', header=TRUE, sep=',', stringsAsFactors=FALSE)Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,
: number of items read is not a multiple of the number of columns# Show structure and summary
cat('Transmission events:')Transmission events:head(transmission_df) time from_patientID to_patientID
1 6.45 104 79
2 7.94 110 108
3 8.71 100 76
4 9.12 79 78
5 12.64 80 121
6 13.07 112 109cat('\nSummary statistics:')
Summary statistics:summary(transmission_df) time from_patientID to_patientID
Min. : 6.45 Min. : 75 Min. : 75
1st Qu.:1140.95 1st Qu.: 4021 1st Qu.: 4028
Median :2273.90 Median : 8083 Median : 8114
Mean :2311.97 Mean : 8157 Mean : 8173
3rd Qu.:3452.97 3rd Qu.:12165 3rd Qu.:12152
Max. :4881.69 Max. :17098 Max. :17166
NA's :1