Package 'shinySIR'

Title: Interactive Plotting for Mathematical Models of Infectious Disease Spread
Description: Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
Authors: Sinead E. Morris [aut, cre] , Ottar N. Bjornstad [ctb]
Maintainer: Sinead E. Morris <sinead.morris@columbia.edu>
License: MIT + file LICENSE
Version: 0.1.2
Built: 2025-03-22 07:12:20 UTC
Source: CRAN

Help Index

Model help

Description

This function prints a list of all built in models, along with their parameter arguments and a short description.

Usage

default_models()

Value

data frame of model descriptions.

Examples

default_models()

Get default initial conditions

Description

This function returns the default parameter vectors for a particular built-in model.

Usage

get_ics(model)

Arguments

model

name of the model to be solved. Examples include: SIR and SIR vaccination.

Value

named vector of default initial conditions.

Get model display names

Description

This function gets the display name for any built-in model.

Usage

get_name(model)

Arguments

model

character specifying the name of the built-in model.

Value

character of the corresponding display name.

Examples

get_name(model = "SIR")

Get default parameters

Description

This function returns the default parameter vectors for a particular built-in model.

Usage

get_params(model)

Arguments

model

name of the model to be solved. Examples include: SIR and SIR vaccination.

Value

list of default parameter vectors.

Examples

get_params(model = "SIR")

Plot model output. This function plots the output of a fitted model data frame.

Description

Plot model output. This function plots the output of a fitted model data frame.

Usage

plot_model(output, linesize, textsize, xlabel, ylabel, legend_title,
  levels, values, ...)

Arguments

output

data frame output from solve_eqns().
linesize

numeric value for line width in ggplot.
textsize

numeric value for textsize in ggplot.
xlabel

character string for x axis label.
ylabel

character string for y axis label.
legend_title

character string for legend title.
levels

character vector of the variable names in the order they should be plotted. Default is to obtain the order from the initial conditions vector 'ics'.
values

vector specifying manual color scale. Length must equal the number of model variables.
...

extra argument to be passed through to ggplot scale_colour_manual: use 'labels' to change the legend names

Value

ggplot object

Solve equations

Description

This function solves an ODE model using 'deSolve' and returns the output as a data frame.

Usage

run_shiny(model = "SIR", neweqns = NULL, ics = NULL, tstart = 0,
  timestep = 1, tmax = 365, parm0 = NULL, parm_names = NULL,
  parm_min = NULL, parm_max = NULL, sigfigs = 4, showtable = TRUE,
  linesize = 1.2, textsize = 14, xlabel = "Time",
  ylabel = "Number of individuals", legend_title = "Compartment",
  slider_steps = NULL, values = NULL, ...)

Arguments

model

name of the model to be solved. Examples of built-in models are: "SIR", "SIR vaccination". Default is "SIR".
neweqns

function specifying the equations of the user-defined model. Only to be used if a model is required that is not built-in. Default is NULL.
ics

named numeric vector specifying the initial conditions i.e. the initial values of all model variables. Default is c(S = 9999, I = 1, R = 0) for the SIR model.
tstart

numerical value of form c(tmin, tmax) indicating the time to start simulations. Default value is 0.
timestep

numerical value indicating time step be used when solving equations. Default value is 1/365.
tmax

numerical value indicating maximum time point to be considered.
parm0

named numeric vector of starting parameter values. Names must correspond to those used in the model equations.
parm_names

character vector of parameter names to be displayed in shiny menu. Must be in the same order as 'parm0'.
parm_min

named numeric vector of minimum parameter values.
parm_max

named numeric vector of maximum parameter values.
sigfigs

number of significant figures to round parameter input vectors. Default is 4.
showtable

logical TRUE/FALSE. Should the table of transformed parameters be shown? Only applies to built-in models. Default is TRUE.
linesize

numeric value for line width in ggplot output. Default is 1.2.
textsize

numeric value for textsize in ggplot output. Default is 14.
xlabel

character string for x axis plotting label. Default is "Time".
ylabel

character string for y axis plotting label. Default is "Number of individuals".
legend_title

character string for legend title. Default is "Compartment".
slider_steps

numeric vector of step size to include between slider input values. Should be NULL or a vector with an entry for each parameter input. Default is NULL.
values

vector specifying manual color scale (if desired). Length must equal the number of model variables.
...

extra argument to be passed through to ggplot scale_colour_manual: use 'labels' to change the legend names.

Value

data frame of model solutions in long format.

Examples

run_shiny(model = "SIR")

Launch a shiny-app simulating the seasonal SEIR model

Description

#' This launches an app running the SEIR model i.e. a model incorporating latency and seasonal forcing in transmission.

Usage

seir.app

Format

An object of class shiny.appobj of length 5.

Details

Launch app for details

Examples

## Not run: seir.app

Launch a shiny-app simulating the SEIRS model

Description

This launches an app running the SEIRS model i.e. a model incorporating latency and loss of immunity.

Usage

seirs.app

Format

An object of class shiny.appobj of length 5.

Details

Launch app for details

Examples

## Not run: seirs.app

SIR model

Description

These equations describe the classic SIR model with no births or deaths.

Usage

SIR(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

SIR model with demography

Description

These equations describe the classic SIR model with equal births and deaths.

Usage

SIRbirths(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

SIRS model

Description

These equations describe the classic SIRS model without births or deaths.

Usage

SIRS(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

SIRS model with demography

Description

These equations describe the classic SIRS model with equal birth and death rates.

Usage

SIRSbirths(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

SIRS model with vaccination at birth

Description

These equations describe the classic SIRS model with equal birth and death rates and vaccination at birth.

Usage

SIRSvaccination(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

SIR model with vaccination at birth

Description

These equations describe the classic SIR model with births and deaths, constant population size, and (optional) vaccination at birth.

Usage

SIRvaccination(t, y, parms)

Arguments

t

numeric vector of time points
y

numeric vector of variables
parms

named vector of model parameters.

Value

equation list

SIS model

Description

These equations describe the classic SIS model with no births or deaths.

Usage

SIS(t, y, parms)

Arguments

t

numeric vector of time points
y

numeric vector of variables
parms

named vector of model parameters.

Value

equation list

SIS model with demography

Description

These equations describe the classic SIR model with equal births and deaths.

Usage

SISbirths(t, y, parms)

Arguments

t

numeric vector of time points.
y

numeric vector of variables.
parms

named vector of model parameters.

Value

equation list

Solve equations

Description

This function solves an ODE model using 'deSolve' and returns the output as a data frame.

Usage

solve_eqns(eqns, ics, times, parms)

Arguments

eqns

name of the model to be solved. Examples include: SIR and SIR vaccination.
ics

named numeric vector specifying the initial conditions i.e. the initial values of all model variables.
times

numerical vector indicating the time points at which the equation should be solved.
parms

named numeric vector of parameter values.

Value

data frame of model solutions in long format.