sbm-class {swash} | R Documentation |
Class "sbm"
Description
The class "sbm"
contains the results of the Swash-Backwash Model and the related input data as well as additional information. Use summary(sbm)
and plot(sbm)
for results summary and plotting, respectively.
Objects from the Class
Objects can be created by the function swash
.
Slots
R_0A
:Object of class
"numeric"
Model result: spatial reproduction numberR_{0A}
integrals
:Object of class
"numeric"
Model result: integralsS_A
,I_A
, andR_A
velocity
:Object of class
"numeric"
Model result: velocity measurest_{FE}
andt_{LE}
occ_regions
:Object of class
"data.frame"
Model result: Occurence at regional levelSIR_regions
:Object of class
"data.frame"
Model result: Susceptible, infected and recovered regions over timecases_by_date
:Object of class
"data.frame"
Total cases by datecases_by_region
:Object of class
"data.frame"
Cumulative cases by regioninput_data
:Object of class
"data.frame"
Input datadata_statistics
:Object of class
"numeric"
Diagnostics of input datacol_names
:Object of class
"character"
Original column names in input data
Methods
- confint
signature(object = "sbm")
: Creates bootstrap confidence intervals forsbm
objects.- plot
signature(x = "sbm")
: Plots the results of the Swash-Backwash Model; two plots: edges over time, total infections per time unitsignature(x = "sbm")
: Prints ansbm
object; usesummary(sbm)
for results- show
signature(object = "sbm")
: Prints ansbm
object; usesummary(sbm)
for results- summary
signature(object = "sbm")
: Prints a summary ofsbm
objects (results of the Swash-Backwash Model)- growth
signature(object = "sbm")
: Estimates logistic growth models fromsbm
objects- growth_initial
signature(object = "sbm")
: Estimates exponential growth models fromsbm
objects for a given time period
Author(s)
Thomas Wieland
References
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
Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8
Smallman-Raynor MR, Cliff AD, Stickler PJ (2022) Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33. Geographical Analysis 54, 197–216. doi:10.1111/gean.12272
Smallman-Raynor MR, Cliff AD, The COVID-19 Genomics UK (COG-UK) Consortium (2022) Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021. Epidemiology and Infection 150, e145. doi:10.1017/S0950268822001285.
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
Examples
showClass("sbm")