6 SIR Epidemic Model
sir_result <- epi_analyze(
data = NULL, outcome = NULL, type = "sir",
N = 1000, beta = 0.3, gamma = 0.1, days = 50
)
epi_visualize(sir_result, x = "time", y = "Infected", type = "curve", main = "Epidemic Curve")
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EpidigiR is an R package for epidemiological analysis, modeling, and visualization…
EpidigiR is an R package for epidemiological analysis, modeling, and visualization, designed with minimal dependencies and comprehensive functionality. It provides three main functions to cover 12 epidemiological topics, including a digital epidemiology aspect that leverages real-time data integration and advanced computational techniques to enhance disease tracking and prediction.
The package includes nine datasets to support these analyses: epi_prevalence, sir_data, geno_data, ml_data, nlp_data, clinical_data, daly_data, survey_data, diagnostic_data, and survival_data.
This vignette demonstrates how to use these functions and datasets for various epidemiological tasks.
The package includes the following datasets:
data(epi_prevalence)
result <- epi_analyze(
epi_prevalence,
outcome = "cases",
population = "population",
group = "region",
type = "summary"
)
print(result)## group mean_outcome population prevalence incidence_rate
## 1 East 140.0000 34000.00 0.4243056 4.117647
## 2 North 133.3333 30000.00 0.4666667 4.444444
## 3 South 120.0000 29333.33 0.4345238 4.090909
## 4 West 100.0000 24333.33 0.4333333 4.109589sir_result <- epi_analyze(
data = NULL, outcome = NULL, type = "sir",
N = 1000, beta = 0.3, gamma = 0.1, days = 50
)
epi_visualize(sir_result, x = "time", y = "Infected", type = "curve", main = "Epidemic Curve")
data(epi_prevalence)
coordinates(epi_prevalence) <- ~lon + lat
epi_visualize(epi_prevalence, x = "prevalence", type = "map", main = "Prevalence Map")
data(clinical_data)
clinical_data$outcome <- as.factor(clinical_data$outcome)
model <- epi_model(clinical_data, formula = outcome ~ age + health_score + dose, type = "logistic")
head(model$predictions)## lambda.min
## 1 0.41
## 2 0.41
## 3 0.41
## 4 0.41
## 5 0.41
## 6 0.41## note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .## NULL## group daly
## 1 group_1 809.1125
## 2 group_2 1171.2362
## 3 group_3 806.3073
## 4 group_4 1392.1371
## 5 group_5 1291.4882
## 6 group_6 509.8396
## 7 group_7 870.1247
## 8 group_8 1220.5410
## 9 group_9 776.4134
## 10 group_10 627.1544
## 11 group_11 1444.5109
## 12 group_12 964.0353
## 13 group_13 1070.6309
## 14 group_14 1023.3304
## 15 group_15 253.7084
## 16 group_16 1174.8507
## 17 group_17 712.7858
## 18 group_18 285.2372
## 19 group_19 588.3101
## 20 group_20 1113.2849## statistic p_value
## X-squared 1.769353 0.4128477## standardized_rate
## 1 33.45531data(ml_data)
epi_model(ml_data, formula = outcome ~ age + exposure + genetic_risk, type = "logistic")## $coefficients
## 4 x 1 sparse Matrix of class "dgCMatrix"
## lambda.min
## (Intercept) -0.4054651
## age .
## exposure .
## genetic_risk .
##
## $predictions
## lambda.min
## 1 0.4
## 2 0.4
## 3 0.4
## 4 0.4
## 5 0.4
## 6 0.4
## 7 0.4
## 8 0.4
## 9 0.4
## 10 0.4
## 11 0.4
## 12 0.4
## 13 0.4
## 14 0.4
## 15 0.4
## 16 0.4
## 17 0.4
## 18 0.4
## 19 0.4
## 20 0.4
## 21 0.4
## 22 0.4
## 23 0.4
## 24 0.4
## 25 0.4
## 26 0.4
## 27 0.4
## 28 0.4
## 29 0.4
## 30 0.4
## 31 0.4
## 32 0.4
## 33 0.4
## 34 0.4
## 35 0.4
## 36 0.4
## 37 0.4
## 38 0.4
## 39 0.4
## 40 0.4
## 41 0.4
## 42 0.4
## 43 0.4
## 44 0.4
## 45 0.4
## 46 0.4
## 47 0.4
## 48 0.4
## 49 0.4
## 50 0.4
## 51 0.4
## 52 0.4
## 53 0.4
## 54 0.4
## 55 0.4
## 56 0.4
## 57 0.4
## 58 0.4
## 59 0.4
## 60 0.4
## 61 0.4
## 62 0.4
## 63 0.4
## 64 0.4
## 65 0.4
## 66 0.4
## 67 0.4
## 68 0.4
## 69 0.4
## 70 0.4
## 71 0.4
## 72 0.4
## 73 0.4
## 74 0.4
## 75 0.4
## 76 0.4
## 77 0.4
## 78 0.4
## 79 0.4
## 80 0.4
## 81 0.4
## 82 0.4
## 83 0.4
## 84 0.4
## 85 0.4
## 86 0.4
## 87 0.4
## 88 0.4
## 89 0.4
## 90 0.4
## 91 0.4
## 92 0.4
## 93 0.4
## 94 0.4
## 95 0.4
## 96 0.4
## 97 0.4
## 98 0.4
## 99 0.4
## 100 0.4Perform survival analysis using survival_data.
## $survfit
## Call: survfit(formula = surv_obj ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 100 71 11 9.24 14.4
##
## $summary
## Call: survfit(formula = surv_obj ~ 1)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.046 100 1 0.9900 0.00995 0.97069 1.000
## 0.292 99 1 0.9800 0.01400 0.95294 1.000
## 0.316 98 1 0.9700 0.01706 0.93714 1.000
## 0.318 97 1 0.9600 0.01960 0.92235 0.999
## 0.421 96 1 0.9500 0.02179 0.90823 0.994
## 0.562 94 1 0.9399 0.02379 0.89440 0.988
## 0.674 93 1 0.9298 0.02559 0.88096 0.981
## 0.986 90 1 0.9195 0.02731 0.86745 0.975
## 1.453 89 1 0.9091 0.02889 0.85422 0.968
## 1.883 88 1 0.8988 0.03036 0.84122 0.960
## 2.161 87 1 0.8885 0.03172 0.82842 0.953
## 2.596 84 1 0.8779 0.03306 0.81543 0.945
## 2.804 82 1 0.8672 0.03434 0.80242 0.937
## 2.810 81 1 0.8565 0.03555 0.78956 0.929
## 2.847 80 1 0.8458 0.03668 0.77685 0.921
## 3.000 78 1 0.8349 0.03778 0.76407 0.912
## 3.062 77 1 0.8241 0.03881 0.75142 0.904
## 3.135 76 1 0.8132 0.03979 0.73888 0.895
## 3.165 74 1 0.8022 0.04074 0.72625 0.886
## 3.197 73 1 0.7913 0.04164 0.71372 0.877
## 3.771 72 1 0.7803 0.04249 0.70129 0.868
## 3.800 71 1 0.7693 0.04328 0.68895 0.859
## 4.204 70 1 0.7583 0.04404 0.67671 0.850
## 4.802 67 1 0.7470 0.04481 0.66411 0.840
## 4.807 66 1 0.7357 0.04554 0.65160 0.831
## 5.066 65 1 0.7243 0.04622 0.63917 0.821
## 5.646 64 1 0.7130 0.04687 0.62683 0.811
## 5.726 63 1 0.7017 0.04747 0.61457 0.801
## 5.887 60 1 0.6900 0.04810 0.60189 0.791
## 5.909 59 1 0.6783 0.04868 0.58930 0.781
## 6.293 57 1 0.6664 0.04926 0.57653 0.770
## 6.436 56 1 0.6545 0.04980 0.56383 0.760
## 6.855 55 1 0.6426 0.05030 0.55122 0.749
## 7.907 54 1 0.6307 0.05075 0.53868 0.738
## 8.457 51 1 0.6183 0.05124 0.52564 0.727
## 8.498 50 1 0.6060 0.05169 0.51268 0.716
## 9.240 49 1 0.5936 0.05209 0.49981 0.705
## 9.659 48 1 0.5812 0.05245 0.48701 0.694
## 9.724 47 1 0.5689 0.05278 0.47430 0.682
## 9.746 46 1 0.5565 0.05306 0.46166 0.671
## 10.244 44 1 0.5439 0.05334 0.44875 0.659
## 10.279 43 1 0.5312 0.05358 0.43593 0.647
## 10.477 42 1 0.5186 0.05377 0.42319 0.635
## 10.672 41 1 0.5059 0.05393 0.41053 0.623
## 11.003 40 1 0.4933 0.05404 0.39795 0.611
## 11.149 38 1 0.4803 0.05416 0.38505 0.599
## 11.293 37 1 0.4673 0.05423 0.37225 0.587
## 11.685 36 1 0.4543 0.05425 0.35952 0.574
## 11.920 35 1 0.4413 0.05423 0.34688 0.562
## 12.290 34 1 0.4284 0.05417 0.33433 0.549
## 12.546 32 1 0.4150 0.05410 0.32140 0.536
## 13.291 30 1 0.4011 0.05404 0.30806 0.522
## 13.480 29 1 0.3873 0.05392 0.29483 0.509
## 14.395 27 1 0.3730 0.05380 0.28112 0.495
## 14.967 24 1 0.3574 0.05375 0.26618 0.480
## 15.631 21 1 0.3404 0.05382 0.24970 0.464
## 15.705 19 1 0.3225 0.05389 0.23243 0.447
## 15.707 18 1 0.3046 0.05379 0.21546 0.431
## 16.059 17 1 0.2867 0.05353 0.19881 0.413
## 16.199 16 1 0.2687 0.05309 0.18246 0.396
## 16.424 15 1 0.2508 0.05249 0.16643 0.378
## 17.312 14 1 0.2329 0.05171 0.15074 0.360
## 18.569 13 1 0.2150 0.05074 0.13538 0.341
## 18.878 11 1 0.1954 0.04975 0.11868 0.322
## 21.678 10 1 0.1759 0.04846 0.10251 0.302
## 22.483 9 1 0.1564 0.04685 0.08691 0.281
## 25.361 8 1 0.1368 0.04489 0.07192 0.260
## 27.253 5 1 0.1095 0.04346 0.05026 0.238
## 40.410 3 1 0.0730 0.04155 0.02390 0.223
## 44.987 2 1 0.0365 0.03312 0.00616 0.216
## 72.110 1 1 0.0000 NaN NA NAdata(nlp_data)
nlp_result <- epi_analyze(nlp_data, outcome = NULL, population = NULL, type = "nlp", n = 5)
head(nlp_result)## word frequency
## region region 33
## dengue dengue 32
## south south 32
## influenza influenza 31
## north north 31## $clusters
## [1] 3 2 3 1 3 1 1 3 2 2 1 2 2 1 3 2 3 1 3 2 3 2 2 2 2 1 2 2 2 3 3 1 3 3 3 3 1
## [38] 1 1 3 2 2 2 2 1 2 2 2 2 3 1 3 2 2 2 2 3 2 3 2 3 2 3 2 2 3 3 2 2 2 3 2 1 3
## [75] 3 3 2 3 1 3 1 2 3 2 3 3 2 3 1 1 2 2 1 3 1 3 2 3 2 3
##
## $centers
## age exposure genetic_risk
## 1 74.00521 0.4178058 0.3936161
## 2 34.17596 0.5159618 0.4529798
## 3 55.09524 0.4881809 0.5910485data(ml_data)
ml_data$outcome <- as.factor(ml_data$outcome)
svm_model <- epi_model(ml_data, formula = outcome ~ age + exposure + genetic_risk, type = "svmRadial")
svm_model$performance## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 60 40
## 1 0 0
##
## Accuracy : 0.6
## 95% CI : (0.4972, 0.6967)
## No Information Rate : 0.6
## P-Value [Acc > NIR] : 0.5433
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 6.984e-10
##
## Sensitivity : 1.0
## Specificity : 0.0
## Pos Pred Value : 0.6
## Neg Pred Value : NaN
## Prevalence : 0.6
## Detection Rate : 0.6
## Detection Prevalence : 1.0
## Balanced Accuracy : 0.5
##
## 'Positive' Class : 0
## ## test_id sensitivity specificity accuracy
## 1 test_1 0.8602151 0.8585859 0.8593750
## 2 test_2 0.7619048 0.6923077 0.7257143
## 3 test_3 0.8181818 0.7857143 0.8012422
## 4 test_4 0.8253968 0.8846154 0.8622754
## 5 test_5 0.8584906 0.8380952 0.8483412
## 6 test_6 0.9108911 0.7625000 0.8453039
## 7 test_7 0.8349515 0.8000000 0.8196721
## 8 test_8 0.8596491 0.7641509 0.8136364
## 9 test_9 0.9135802 0.7583333 0.8208955
## 10 test_10 0.8823529 0.6746988 0.7797619data(clinical_data)
epi_visualize(clinical_data, x = "arm", y = "outcome", type = "boxplot", main = "Outcome by Treatment Arm")
data(ml_data)
epi_visualize(ml_data, x = "age", y = "outcome", type = "scatter", main = "Age vs. Disease Outcome")
EpidigiR offers a streamlined yet powerful toolkit for epidemiological analysis, featuring three key functions—epi_analyze, epi_model, and epi_visualize—and nine datasets that address all major topics. These tools support a range of analyses, from SIR modeling to sophisticated machine learning methods such as Random Forest and SVM. Furthermore, it integrates a digital epidemiology component, utilizing real-time data and advanced computational approaches to improve disease monitoring and forecasting, providing a valuable resource for researchers and analysts.
EpidigiR is released under the MIT License © 2025 Esther Atsabina Wanjala.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.