growth {swash} | R Documentation |
Logistic Growth Models for Regional Infections
Description
Estimates N
logistic growth models for N
regions.
Usage
growth(
object,
S_iterations = 10,
S_start_est_method = "bisect",
seq_by = 10,
nls = TRUE
)
Arguments
object |
object of class |
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
The function estimates logistic growth models for regional infections based on a sbm
object. See logistic_growth
for further details.
Value
list
with two entries:
results: |
Object of class |
logistic_growth_models: |
Object of 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
logistic_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
CH_covidwave1 <-
swash (
data = COVID19Cases_geoRegion,
col_cases = "entries",
col_date = "datum",
col_region = "geoRegion"
)
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)
CH_covidwave1_growth <- growth(CH_covidwave1)
CH_covidwave1_growth
# Logistic growth models for sbm object CH_covidwave1