logistic_growth {swash} | R Documentation |
Logistic Growth Model for Epidemic Data
Description
Estimation of logistic growth models from cumulative infections data, linearized OLS and/or NLS
Usage
logistic_growth(
y,
t,
S = NULL,
S_start = NULL,
S_end = NULL,
S_iterations = 10,
S_start_est_method = "bisect",
seq_by = 10,
nls = TRUE
)
Arguments
y |
|
t |
|
S |
Saturation value for the model |
S_start |
Start value of the saturation value for estimation |
S_end |
End value of the saturation value for estimation |
S_iterations |
Number of iterations for saturation value search |
S_start_est_method |
Method for saturation value search, either "bisect" or "trial_and_error" |
seq_by |
No of segments for the "trial_and_error" estimation of the saturation value |
nls |
Nonlinear estimation? |
Details
This function allows the estimation of a logistic growth model. The user must specify the dependent variable (cumulative infections) and the time variable (time counter or date values). The estimation is performed using a linearized model as an OLS estimator and as an NLS estimator. For the former, the saturation value can either be specified by the user or found using a search algorithm. The parameters from the OLS fit are used as starting values for the NLS estimation.
Value
object of class loggrowth-class
Author(s)
Thomas Wieland
References
Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. doi:10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81
Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002
Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324
See Also
loggrowth-class, growth, exponential_growth
Examples
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level
COVID19Cases_geoRegion <-
COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total
COVID19Cases_geoRegion <-
COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave
COVID19Cases_BS <-
COVID19Cases_geoRegion[(COVID19Cases_geoRegion$geoRegion == "ZH")
& (COVID19Cases_geoRegion$sumTotal > 0),]
# COVID cases for Zurich
loggrowth_BS <- logistic_growth (
y = as.numeric(COVID19Cases_BS$sumTotal),
t = COVID19Cases_BS$datum,
S = 5557,
S_start = NULL,
S_end = NULL,
S_iterations = 10,
S_start_est_method = "bisect",
seq_by = 10,
nls = TRUE
)
# Logistic growth model with stated saturation value
summary(loggrowth_BS)
# Summary of logistic growth model
plot(loggrowth_BS)
# Plot of logistic growth model