growth_initial {swash} | R Documentation |
Exponential Growth Models for Regional Infections
Description
Estimates N
exponential growth models for a given time period in N
regions.
Usage
growth_initial(
object,
time_units = 10,
GI = 4
)
Arguments
object |
object of class |
time_units |
|
GI |
Generation interval for computing |
Details
The function estimates exponential growth models for regional infections based on a sbm
object. Such models are design for the analysis of the initial phase of an epidemic spread. The user must state how much time units (from start) are included. See exponential_growth
for further details of the estimation.
Value
list
with two entries:
results: |
Object of class |
exponential_growth_models: |
Object of class |
Author(s)
Thomas Wieland
References
Bonifazi G et al. (2021) A simplified estimate of the effective reproduction number Rt using its relation with the doubling time and application to Italian COVID-19 data. The European Physical Journal Plus 136, 386. doi:10.1140/epjp/s13360-021-01339-6
Chowell G, Viboud C, Hyman JM, Simonsen L (2015) The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates. PLOS Currents Outbreaks, ecurrents.outbreaks.8b55f4bad99ac5c5db3663e916803261. doi:10.1371/currents.outbreaks.8b55f4bad99ac5c5db3663e916803261
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) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Science 131, 104924. doi:10.1016/j.ssci.2020.104924
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_initialgrowth_3weeks <-
growth_initial(
CH_covidwave1,
time_units = 21
)
CH_covidwave1_initialgrowth_3weeks$results
# Exponential models for sbm object CH_covidwave1
# initial growth in the first 3 weeks