DSAIDE: A package to learn about Dynamical Systems Approaches to Infectious Disease Epidemiology

Description

This package provides a number of shiny apps that simulate various infectious disease dynamics models By manipulating the models and working through the instructions provided within the shiny UI, you can learn about some important concepts in infectious disease epidemiology. You will also learn how models can be used to study such concepts.

Details

Start the main menu of the package by calling dsaidemenu().

To learn more about how to use the package, see the documentation on the package website: https://ahgroup.github.io/DSAIDE/

Author(s)

Maintainer: Andreas Handel andreas.handel@gmail.com (ORCID)

Other contributors:

• Cody Dailey [contributor]

• Isaac Chun-Hai Fung [contributor]

• Ronald Galiwango [contributor]

• Salil Goyal [contributor]

• Spencer Hall [contributor]

• Ben Listyg [contributor]

• Brian McKay [contributor]

• John Rossow [contributor]

• Sina Solaimanpour [contributor]

• Alexis Vittengl [contributor]

• Henok Woldu [contributor]

See Also

Useful links:

• https://ahgroup.github.io/DSAIDE/

• https://github.com/ahgroup/DSAIDE/

• Report bugs at https://github.com/ahgroup/DSAIDE/issues

The main menu for the DSAIDE package

Description

This function opens a Shiny app with a menu that will allow the user to run the different simulations.

Usage

dsaidemenu()

Details

Run this function with no arguments to start the main menu (a Shiny app) for DSAIDE

Author(s)

Andreas Handel

Examples

## Not run: dsaidemenu()

1918 Influenza mortality data

Description

Weekly influenza deaths per 100,000 during the 1918 pandemic for New York City.

Usage

flu1918data

Format

A data frame/tibble with these variables:

Date

Week of reporting, date format

Deaths

New deaths in NYC per week per 100,000, numeric

Details

Data is from Supplementary Table 1 of Mills et al 2004 Nature: https://www.nature.com/articles/nature03063

See this article and citations therein for more details on the data. Note that only a subset of the data is present here and the data are meant to be used for illustrative purposes only. For a proper re-analysis of these data, use the source mentioned above or check out data available through project Tycho: https://www.tycho.pitt.edu/

A helper function which processes and displays the documentation part for each app

Description

This function take the documentation provided as html file and extracts sections to generate the tabs with content for each Shiny app. This is a helper function and only useful for this package.

Usage

generate_documentation(docfilename)

Arguments

Details

This function is called by the Shiny UIs to populate the documentation and information tabs.

Value

tablist A list of tabs for display in a Shiny UI.

Author(s)

Andreas Handel

A helper function that produces a call to a simulator function for specific settings

Description

This function takes a modelsettings structure and uses that information to create an unevaluated function call that runs the simulator function with the specified settings

Usage

generate_fctcall(modelsettings)

Arguments

Details

This function produces a function call for specific settings.

Value

A string containing an unevaluated function call with the specified settings, or an error message

A helper function that takes simulation results and produces ggplot plots

Description

This function generates plots to be displayed in the Shiny UI. This is a helper function. This function processes results returned from the simulation, supplied as a list.

Usage

generate_ggplot(res)

Arguments

Details

This function can be called to produce plots, i.e. those displayed for each app. The input needed by this function is produced by either calling the run_model function (as done when going through the UI) or manually transforming the output from a simulate_ function into the correct list structure as explained here.

Value

A ggplot plot structure for display in a Shiny UI.

Author(s)

Andreas Handel

A helper function that takes simulation results and produces plotly plots

Description

This function generates plots to be displayed in the Shiny UI. This is a helper function. This function processes results returned from the simulation, supplied as a list.

Usage

generate_plotly(res)

Arguments

Details

This function can be called to produce plots, i.e. those displayed for each app. The input needed by this function is produced by either calling the run_model() function (as done when going through the UI) or manually transforming the output from a simulate_ function into the correct list structure explained below.

Value

A plotly plot structure for display in a Shiny UI.

Author(s)

Yang Ge, Andreas Handel

A helper function that takes a model and generates shiny UI elements

Description

This function generates shiny UI inputs for a supplied model. This is a helper function called by the shiny app.

Usage

generate_shinyinput(
 use_mbmodel = FALSE,
 mbmodel = NULL,
 use_doc = FALSE,
 model_file = NULL,
 model_function = NULL,
 otherinputs = NULL,
 packagename = NULL
)

Arguments

Details

This function is called by the Shiny app to produce the Shiny input UI elements. It produces UI by 3 different ways. 1. If use_mbmodel is TRUE, an mbmodel list structure, which needs to be provided, is used 2. If use_mbmodel is FALSE and use_doc is TRUE, the documentation header of the function is used. For that approach, model_file needs to contain the name/path to the R script for the function The doc needs to have a specific format for this. 3. If both use_mbmodel and use_doc are FALSE, the function formals are parsed and used as UI. For that approach, model_function needs to specify the name of the model model_function is assumed to be the name of a function. The formals of the function will be parsed to create UI elements. Non-numeric arguments of functions are removed and need to be included in the otherinputs argument.

Value

A renderUI object that can be added to the shiny output object for display in a Shiny UI

A helper function that takes result from the simulators and produces text output

Description

This function generates text to be displayed in the Shiny UI. This is a helper function. This function processes results returned from the simulation, supplied as a list.

Usage

generate_text(res)

Arguments

Details

This function is called by the Shiny server to produce output returned to the Shiny UI.

Value

HTML formatted text for display in a Shiny UI.

Author(s)

Andreas Handel

Cases of norovirus during an outbreak

Description

Norovirus case data from an outbreak among children on a school trip

Usage

norodata

Format

A data frame with these variables:

Date

Day of outbreak, all in December 2007, numeric

Cases

New cases for the specified date, numeric

Details

The data are from Kuo 2009 Wien Klin Woch: "A non-foodborne norovirus outbreak among school children during a skiing holiday, Austria, 2007" Specifically, the data comes from figure 1 of this article. The total number of susceptibles was 284.

See this article and citations therein for more details on the data. Note that only a subset of the data is present here and the data are meant for illustrative purposes only. For a proper re-analysis of these data, use the source mentioned above.

A function that runs an app for specific settings and processes results for plot and text generation

Description

This function runs a model based on information provided in the modelsettings list passed into it.

Usage

run_model(modelsettings)

Arguments

Details

This function runs a model for specific settings.

Value

A vectored list named "result" with each main list element containing the simulation results in a dataframe called dat and associated metadata required for generate_plot and generate_text functions. Most often there is only one main list entry (result[[1]]) for a single plot/text.

Characteristics of ID

Description

A compartmental model with several different compartments: Susceptibles (S), Infected and Pre-symptomatic (P), Infected and Asymptomatic (A), Infected and Symptomatic (I), Recovered and Immune (R) and Dead (D)

Usage

simulate_Characteristics_of_ID_ode(
 S = 1000,
 P = 1,
 A = 0,
 I = 0,
 R = 0,
 D = 0,
 bP = 0,
 bA = 0,
 bI = 0.001,
 gP = 0.1,
 gA = 0.1,
 gI = 0.1,
 f = 0,
 d = 0,
 tstart = 0,
 tfinal = 200,
 dt = 0.1
)

Arguments

Details

The model tracks the dynamics of susceptible, presymptomatic, asymptomatic, symptomatic, recovered, and dead individuals. Susceptible (S) individuals can become infected by presymptomatic (P), asymptomatic (A), or infected (I) hosts. All infected individuals enter the presymptomatic stage first, from which they can become symptomatic or asymptomatic. Asymptomatic hosts recover within some specified duration of time, while infected hosts either recover or die, thus entering either R or D. Recovered individuals are immune to reinfection. This model is part of the DSAIDE R package, more information can be found there.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel, Alexis Vittengl

Model creation date

2020年09月29日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_Characteristics_of_ID_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_Characteristics_of_ID_ode(S = 2000,P = 2,A = 0,I = 0,R = 0,D = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

Complex ID Control

Description

A compartmental model with several different compartments: Susceptibles (S), Infected and Pre-symptomatic (P), Infected and Asymptomatic (A), Infected and Symptomatic (I), Recovered and Immune (R) and Dead (D). Also modeled is an environmental pathogen stage (E), and susceptible (Sv) and infected (Iv) vectors.

Usage

simulate_Complex_ID_Control_ode(
 S = 1000,
 P = 1,
 A = 1,
 I = 1,
 R = 0,
 D = 0,
 E = 1,
 Sv = 1000,
 Iv = 1,
 nH = 0.01,
 mH = 0.001,
 nV = 0.01,
 mV = 0.001,
 bP = 0.02,
 bA = 0.02,
 bI = 0.02,
 bE = 0.02,
 bV = 0.02,
 bH = 0.02,
 gP = 0.7,
 gA = 0.1,
 gI = 0.1,
 pI = 0.04,
 pA = 0.03,
 c = 0.7,
 f = 0.08,
 d = 0.01,
 w = 0.003,
 tstart = 0,
 tfinal = 100,
 dt = 0.1
)

Arguments

Details

Susceptible individuals (S) can become infected by pre-symptomatic (P), asymptomatic (A) or symptomatic (I) hosts. The rates at which infections from the different types of infected individuals (P, A and I) occur are governed by 3 parameters, _b~P~_, _b~A~_, and _b~I~_. Susceptible individuals (S) can also become infected by contact with the environment or infected vectors, at rates _b~E~_ and _b~v~_. Susceptible vectors (S~v~) can become infected by contact with symptomatic hosts at rate _b~h~_. All infected hosts first enter the presymptomatic stage. They remain there for some time (determined by rate _g~P~_, the inverse of which is the average time spent in the presymptomatic stage). A fraction _f_ of presymptomatic hosts move into the asymptomatic category, and the rest become symptomatic infected hosts. Asymptomatic infected hosts recover after some time (specified by the rate _g~A~_). Similarly, the rate _g~I~_ determines the duration the symptomatic hosts stay in the symptomatic state. For symptomatic hosts, two outcomes are possible. Either recovery or death. The parameter _d_ determines the fraction of hosts that die due to disease. Recovered individuals are initially immune to reinfection. They can lose their immunity at rate _w_ and return to the susceptible compartment. Symptomatic and asymptomatic hosts shed pathogen into the environment at rates p~A~ and p~I~. The pathogen in the environment decays at rate _c_. New susceptible hosts and vectors enter the system (are born) at rates _n~h~_ and _n~v~_. Mortality (death unrelated to disease) for hosts and vectors occurs at rates _m~h~_ and _m~v~_.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel, Alexis Vittengl

Model creation date

2020年09月24日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

# To run the simulation with default parameters:
result <- simulate_Complex_ID_Control_ode()
# To choose values other than the standard one, specify them like this:
result <- simulate_Complex_ID_Control_ode(P = 2,A = 2,I = 2,R = 0,D = 0,E = 2,Iv = 2)
# You can display or further process the result, like this:
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l')
print(paste('Max number of S: ',max(result$ts[,'S'])))

Drug Resistance Evolution

Description

An SIR-type model that includes drug treatment and resistance.

Usage

simulate_Drug_Resistance_Evolution_stochastic(
 S = 1000,
 Iu = 1,
 It = 1,
 Ir = 1,
 R = 0,
 bu = 0.002,
 bt = 0.002,
 br = 0.002,
 gu = 1,
 gt = 1,
 gr = 1,
 f = 0,
 cu = 0,
 ct = 0,
 tfinal = 100,
 rngseed = 123
)

Arguments

Details

The model includes susceptible, infected untreated, treated and resistant, and recovered compartments. The processes which are modeled are infection, treatment, resistance generation and recovery.

This code was generated by the modelbuilder R package. The model is implemented as a set of stochastic equations using the adaptivetau package. The following R packages need to be loaded for the function to work: adpativetau

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年10月05日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_Drug_Resistance_Evolution_stochastic() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_Drug_Resistance_Evolution_stochastic(S = 2000,Iu = 2,It = 2,Ir = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

Environmental Transmission model

Description

An SIR model including environmental transmission

Usage

simulate_Environmental_Transmission_model_ode(
 S = 1000,
 I = 1,
 R = 0,
 P = 0,
 bI = 0.004,
 bP = 0,
 n = 0,
 m = 0,
 g = 2,
 q = 0,
 c = 0,
 tstart = 0,
 tfinal = 60,
 dt = 0.1
)

Arguments

Details

The model includes susceptible, infected, recovered and environmental pathogen compartments. Infection can occur through direct contact with infected or through contact with pathogen in the environment. Infected individuals shed into the environment, pathogen decays there.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年12月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_Environmental_Transmission_model_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_Environmental_Transmission_model_ode(S = 2000,I = 2,R = 0,P = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

Host Heterogeneity Model

Description

An SIR type model stratified for two different types of hosts.

Usage

simulate_Host_Heterogeneity_Model_ode(
 S1 = 1000,
 I1 = 1,
 R1 = 0,
 S2 = 200,
 I2 = 1,
 R2 = 0,
 b11 = 0.002,
 b12 = 0,
 b21 = 0,
 b22 = 0.01,
 g1 = 1,
 g2 = 1,
 w1 = 0,
 w2 = 0,
 tstart = 0,
 tfinal = 60,
 dt = 0.1
)

Arguments

Details

This model tracks susceptibles, infected and recovered of 2 different types. Think of those types as e.g. males/females, children/adults, etc. The model includes infection, recovery and waning immunity processes for both hosts.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel, Alexis Vittengl

Model creation date

2020年10月05日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_Host_Heterogeneity_Model_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_Host_Heterogeneity_Model_ode(S1 = 2000,I1 = 2,R1 = 0,S2 = 400,I2 = 2,R2 = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S1'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S1: ',max(result$ts[,'S1']))) 

SEIRSd model

Description

A SEIRS model with 4 compartments

Usage

simulate_SEIRSd_model_stochastic(
 S = 1000,
 E = 1,
 I = 1,
 R = 0,
 bE = 0,
 bI = 0.001,
 gE = 1,
 gI = 1,
 w = 1,
 n = 0,
 m = 0,
 tfinal = 100,
 rngseed = 123
)

Arguments

Details

The model includes susceptible, exposed/asymptomatic, infected/symptomatic, and recovered compartments. The processes that are modeled are infection, progression to infectiousness, recovery and waning immunity. Demographics through natural births and deaths are also included.

This code was generated by the modelbuilder R package. The model is implemented as a set of stochastic equations using the adaptivetau package. The following R packages need to be loaded for the function to work: adpativetau

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月28日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_SEIRSd_model_stochastic() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_SEIRSd_model_stochastic(S = 2000,E = 2,I = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

SIRSd model

Description

A SIRSd model with 3 compartments. Processes are infection, recovery, births deaths and waning immunity.

Usage

simulate_SIRSd_model_ode(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.002,
 g = 1,
 w = 1,
 n = 0,
 m = 0,
 tstart = 0,
 tfinal = 100,
 dt = 0.1
)

Arguments

Details

The model includes susceptible, infected, and recovered compartments. The processes which are modeled are infection, recovery, natural births and deaths and waning immunity.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_SIRSd_model_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_SIRSd_model_ode(S = 2000,I = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

SIRSd model

Description

A SIRSd model with 3 compartments. Processes are infection, recovery, births deaths and waning immunity.

Usage

simulate_SIRSd_model_stochastic(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.002,
 g = 1,
 w = 1,
 n = 0,
 m = 0,
 tfinal = 100,
 rngseed = 123
)

Arguments

Details

The model includes susceptible, infected, and recovered compartments. The processes which are modeled are infection, recovery, natural births and deaths and waning immunity.

This code was generated by the modelbuilder R package. The model is implemented as a set of stochastic equations using the adaptivetau package. The following R packages need to be loaded for the function to work: adpativetau

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_SIRSd_model_stochastic() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_SIRSd_model_stochastic(S = 2000,I = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

SIR model

Description

A basic SIR model with 3 compartments and infection and recovery processes

Usage

simulate_SIR_model_discrete(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.002,
 g = 1,
 tstart = 0,
 tfinal = 100,
 dt = 0.1
)

Arguments

Details

The model includes susceptible, infected, and recovered compartments. The two processes that are modeled are infection and recovery.

This code was generated by the modelbuilder R package. The model is implemented as a set of discrete time equations using a for loop. The following R packages need to be loaded for the function to work: none

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_SIR_model_discrete() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_SIR_model_discrete(S = 2000,I = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

SIR model

Description

A basic SIR model with 3 compartments and infection and recovery processes

Usage

simulate_SIR_model_ode(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.002,
 g = 1,
 tstart = 0,
 tfinal = 100,
 dt = 0.1
)

Arguments

Details

The model includes susceptible, infected, and recovered compartments. The two processes that are modeled are infection and recovery.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_SIR_model_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_SIR_model_ode(S = 2000,I = 2,R = 0) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of S: ',max(result$ts[,'S']))) 

Simulation to illustrate parameter scan of the basic SIR model with births and deaths #'

Description

This function simulates the SIRS model ODE for a range of parameters. The function returns a data frame containing the parameter that has been varied and the outcomes (see details).

Usage

simulate_SIR_modelexploration(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.002,
 g = 1,
 w = 0,
 n = 0,
 m = 0,
 tstart = 0,
 tfinal = 1000,
 dt = 0.1,
 samples = 10,
 parmin = 5e-04,
 parmax = 0.005,
 samplepar = "b",
 pardist = "lin"
)

Arguments

Details

This code illustrates how to systematically analyze the impact of a specific parameter. The SIR ODE model with births and deaths is simulated for different parameter values. The user can specify which parameter is sampled, and the simulation returns for each parameter sample the max and final value for the variables. Also returned is the varied parameter and an indicator if steady state was reached.

Value

The function returns the output as a list, list element 'dat' contains the data frame with results of interest. The first column is called xvals and contains the values of the parameter that has been varied as specified by 'samplepar'. The remaining columns contain maximum and final state numbers of susceptible, infected and recovered Smax, Imax, Rmax and Sfinal, Ifinal, Rfinal. A final boolean variable 'steady' is returned for each simulation. It is TRUE if the simulation reached steady state, otherwise FALSE.

Notes

The parameter dt only determines for which times the solution is returned, it is not the internal time step. The latter is set automatically by the ODE solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. specify negative parameter values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

See Also

See the shiny app documentation corresponding to this simulator function for more details on this model.

Examples

# To run the simulation with default parameters just call the function:
## Not run: res <- simulate_SIR_modelexploration()
# To choose parameter values other than the standard one, specify them, like such:
res <- simulate_SIR_modelexploration(tfinal=100, samples=5, samplepar='g', parmin=0.1, parmax=1)
# You should then use the simulation result returned from the function, like this:
plot(res$dat[,"xvals"],res$data[,"Imax"],xlab='Parameter values',ylab='Max Infected',type='l')

Simulation to illustrate uncertainty and sensitivity analysis

Description

This function performs uncertainty and sensitivity analysis using the SIRS model.

Usage

simulate_SIR_usanalysis(
 Smin = 1000,
 Smax = 1000,
 Imin = 10,
 Imax = 10,
 bmin = 0.005,
 bmax = 0.01,
 gmean = 0.5,
 gvar = 0.01,
 nmin = 0,
 nmax = 0,
 mmin = 0,
 mmax = 0,
 wmin = 0,
 wmax = 0,
 samples = 5,
 rngseed = 100,
 tstart = 0,
 tfinal = 500,
 dt = 0.1
)

Arguments

Details

The SIRS model with demographics is simulated for different parameter values. The user provides ranges for the initial conditions and parameter values and the number of samples. The function does Latin Hypercube Sampling (LHS) of the parameters and runs the model for each sample. Distribution for all parameters is assumed to be uniform between the min and max values. The only exception is the recovery parameter, which (for illustrative purposes) is assumed to be gamma distributed with the specified mean and variance. This code is part of the DSAIDE R package. For additional model details, see the corresponding app in the DSAIDE package.

Value

The function returns the output as a list. The list element 'dat' contains a data frame. The simulation returns for each parameter sample the peak and final value for I and final for S. Also returned are all parameter values as individual columns and an indicator stating if steady state was reached. A final variable 'steady' is returned for each simulation. It is TRUE if the simulation did reach steady state, otherwise FALSE.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. specify negative parameter values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

See Also

See the Shiny app documentation corresponding to this simulator function for more details on this model.

Examples

# To run the simulation with default parameters just call the function:
## Not run: result <- simulate_SIR_usanalysis()
# To choose parameter values other than the standard one, specify them, like such:
result <- simulate_SIR_usanalysis(gmean = 2, gvar = 0.2, samples = 5, tfinal = 50)
# You should then use the simulation result returned from the function, like this:
plot(result$dat[,"g"],result$dat[,"Ipeak"],xlab='values for g',ylab='Peak Bacteria',type='l')

Vector transmission model

Description

A basic model with several compartments to model vector-borne transmission

Usage

simulate_Vector_transmission_model_ode(
 Sh = 1000,
 Ih = 1,
 Rh = 0,
 Sv = 1000,
 Iv = 1,
 b1 = 0.003,
 b2 = 0.003,
 g = 2,
 w = 0,
 n = 200,
 m = 0.1,
 tstart = 0,
 tfinal = 100,
 dt = 0.1
)

Arguments

Details

The model tracks the dynamics of susceptible, infected, and recovered hosts, and susceptible and infected vectors. Infection, recovery, and waning immunity processes are implemented for hosts. Births and deaths and infection processes are implemented for vectors.

This code was generated by the modelbuilder R package. The model is implemented as a set of ordinary differential equations using the deSolve package. The following R packages need to be loaded for the function to work: deSolve.

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have negative values for parameters), the code will likely abort with an error message.

Model Author

Andreas Handel

Model creation date

2020年09月01日

Code Author

generated by the modelbuilder R package

Code creation date

2021年07月19日

Examples

 
# To run the simulation with default parameters: 
result <- simulate_Vector_transmission_model_ode() 
# To choose values other than the standard one, specify them like this: 
result <- simulate_Vector_transmission_model_ode(Sh = 2000,Ih = 2,Rh = 0,Sv = 2000,Iv = 2) 
# You can display or further process the result, like this: 
plot(result$ts[,'time'],result$ts[,'Sh'],xlab='Time',ylab='Numbers',type='l') 
print(paste('Max number of Sh: ',max(result$ts[,'Sh']))) 

Simulation of a compartmental infectious disease transmission model illustrating different types of direct transmission

Description

This model allows for the simulation of different direct transmission modes

Usage

simulate_directtransmission_ode(
 S = 999,
 I = 1,
 bd = 0.005,
 bf = 0,
 A = 2,
 n = 0,
 m = 0,
 g = 0.1,
 w = 0,
 scenario = 1,
 tmax = 120
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a list, with the element ts, which is a dataframe whose columns represent time, the number of susceptibles, the number of infected, and the number of recovered.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. any negative values or fractions > 1), the code will likely abort with an error message

Author(s)

Andreas Handel

References

See e.g. Keeling and Rohani 2008 for SIR models and the documentation for the deSolve package for details on ODE solvers

See Also

The UI of the Shiny app 'DirectTransmission', which is part of this package, contains more details on the model.

Examples

 # To run the simulation with default parameters just call this function:
 result <- simulate_directtransmission_ode()
 # To choose parameter values other than the standard one, specify them like such:
 result <- simulate_directtransmission_ode(S = 100, tmax = 100, A=10)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[,"time"],result$ts[,"S"],xlab='Time',ylab='Number Susceptible',type='l')

Fitting a SIR-type model to flu data

Description

Fitting fitting mortality data from the 1918 influenza pandemic to an SIR-type model to estimate R0. For the data, see 'flu1918data'.

Usage

simulate_flu_fit(
 S = 5e+06,
 I = 1,
 D = 0,
 b = 1e-06,
 blow = 1e-08,
 bhigh = 1e-04,
 g = 1,
 glow = 0.01,
 ghigh = 100,
 f = 0.01,
 flow = 1e-04,
 fhigh = 1,
 usesimdata = 0,
 bsim = 1e-06,
 gsim = 1,
 fsim = 0.01,
 noise = 0,
 iter = 1,
 solvertype = 1,
 logfit = 0,
 rngseed = 100
)

Arguments

Details

A simple compartmental ODE model is fitted to data. The model includes susceptible, infected, and dead compartments. The two processes that are modeled are infection and recovery. A fraction of recovered can die. Data can either be real or created by running the model with known parameters and using the simulated data to determine if the model parameters can be identified. The fitting is done using solvers/optimizers from the nloptr package (which is a wrapper for the nlopt library). The package provides access to a large number of solvers. Here, we only implement 3 solvers, namely 1 = NLOPT_LN_COBYLA, 2 = NLOPT_LN_NELDERMEAD, 3 = NLOPT_LN_SBPLX For details on what those optimizers are and how they work, see the nlopt/nloptr documentation.

Value

The function returns a list containing as elements the best fit time series data frame, the best fit parameters, the data and the final SSR

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. specify negative parameter or starting values, the code will likely abort with an error message.

Author(s)

Andreas Handel

See Also

See the Shiny app documentation corresponding to this function for more details on this model.

Examples

# To run the code with default parameters just call the function:
## Not run: result <- simulate_flu_fit()
# To apply different settings, provide them to the simulator function, like such:
result <- simulate_flu_fit(iter = 5, logfit = 1, solvertype = 2, usesimdata = 1)

Simulation of a compartmental infectious disease transmission model with 3 types of hosts and intervention

Description

This model allows for the simulation of an ID with 3 types of hosts. Groups are assumed to be children, adults and elderly. Intervention can be applied to any of the groups for a certain duration.

Usage

simulate_idcontrolmultigroup_ode(
 Sc = 1000,
 Ic = 0,
 Sa = 1000,
 Ia = 1,
 Se = 1000,
 Ie = 0,
 bcc = 3e-04,
 bca = 1e-04,
 bce = 1e-04,
 bac = 1e-04,
 baa = 3e-04,
 bae = 1e-04,
 bec = 1e-04,
 bea = 1e-04,
 bee = 3e-04,
 gc = 0.1,
 ga = 0.1,
 ge = 0.1,
 wc = 0,
 wa = 0,
 we = 0,
 mc = 0.001,
 ma = 0.01,
 me = 0.1,
 f1 = 0,
 T1_start = 50,
 T1_end = 150,
 f2 = 0,
 T2_start = 50,
 T2_end = 150,
 f3 = 0,
 T3_start = 50,
 T3_end = 150,
 tmax = 600
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time. The model implement basic processes of infection, recovery and death. Waning immunity is also implemented. Control is applied, which reduces transmission by the indicated proportion, during times tstart and tend. Control can be applied at different levels to the different groups.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. any negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_idcontrolmultigroup_ode()

Simulation of a compartmental infectious disease transmission model to study the reproductive number

Description

Simulation of a basic SIR compartmental model with these compartments: Susceptibles (S), Infected/Infectious (I), Recovered and Immune (R).

The model is assumed to be in units of months when run through the Shiny App. However as long as all parameters are chosen in the same units, one can directly call the simulator assuming any time unit.

Usage

simulate_idcontrolmultioutbreak_ode(
 S = 1000,
 I = 1,
 R = 0,
 b = 0.001,
 g = 1,
 f = 0.3,
 tstart = 10,
 tend = 50,
 tnew = 50,
 tmax = 100
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time. The model implement basic processes of infection at rate b and recovery at rate g. Treatment is applied, which reduces b by the indicated proportion, during times tstart and tend. At time intervals given by tnew, a new infected individual enters the population. The simulation also monitors the number of infected and when they drop below 1, they are set to 0.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

See Also

The UI of the app 'Multi Outbreak ID Control', which is part of the DSAIDE package, contains more details.

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_idcontrolmultioutbreak_ode()
 # To choose parameter values other than the standard one, 
 # specify the parameters you want to change, e.g. like such:
 result <- simulate_idcontrolmultioutbreak_ode(S = 2000, I = 10, tmax = 100, g = 0.5)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')

Simulation of a compartmental infectious disease transmission model including seasonality

Description

Simulation of a compartmental model with several different compartments: Susceptibles (S), Infected and Pre-symptomatic (P), Infected and Asymptomatic (A), Infected and Symptomatic (I), Recovered and Immune (R) and Dead (D).

This model includes natural births and deaths and waning immunity. It also allows for seasonal variation in transmission. The model is assumed to run in units of months. This assumption is hard-coded into the sinusoidally varying transmission coefficient, which is assumed to have a period of a year.

Usage

simulate_idpatterns_ode(
 S = 1000,
 P = 1,
 bP = 0,
 bA = 0,
 bI = 0.002,
 s = 0,
 gP = 1,
 gA = 1,
 gI = 1,
 f = 0,
 d = 0,
 w = 0,
 n = 0,
 m = 0,
 timeunit = 1,
 tmax = 300
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. have I0 > PopSize or any negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

References

See e.g. Keeling and Rohani 2008 for SIR models and the documentation for the deSolve package for details on ODE solvers

See Also

The UI of the app, which is part of this package, contains more details on the model.

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_idpatterns_ode()
 # To choose parameter values other than the standard one, specify them like such:
 result <- simulate_idpatterns_ode(S = 2000, P = 10, tmax = 100, f = 0.1, d = 0.2, s = 0.1)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')

Simulation of a compartmental infectious disease transmission model illustrating the impact of ID surveillance

Description

This model allows for the exploration of the impact of ID surveillance on transmission dynamics

Usage

simulate_idsurveillance_ode(
 S = 1000,
 P = 1,
 tmax = 200,
 bP = 0,
 bA = 0,
 bI = 0.001,
 gP = 0.5,
 f = 0,
 d = 0,
 w = 0,
 m = 0,
 n = 0,
 rP = 0,
 rA = 0,
 rI = 0.5
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel, Ronald Galiwango

See Also

The UI of the app 'Parasite Model', which is part of the DSAIDE package, contains more details.

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_idsurveillance_ode()
 # To choose parameter values other than the standard one, 
 # specify the parameters you want to change, e.g. like such:
 result <- simulate_idsurveillance_ode(S = 2000, tmax = 100, f = 0.5)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')

Simulation of a compartmental infectious disease transmission model to study the reproductive number

Description

Simulation of a basic SIR compartmental model with these compartments: Susceptibles (S), Infected/Infectious (I), Recovered and Immune (R).

The model is assumed to be in units of months when run through the Shiny App. However as long as all parameters are chosen in the same units, one can directly call the simulator assuming any time unit.

Usage

simulate_idvaccine_ode(
 S = 1000,
 I = 1,
 f = 0,
 e = 0,
 b = 0.01,
 g = 10,
 n = 0,
 m = 0,
 w = 0,
 tmax = 300
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel

References

See e.g. Keeling and Rohani 2008 for SIR models and the documentation for the deSolve package for details on ODE solvers

See Also

The UI of the app contains more details on the model.

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_idvaccine_ode()
 # To choose parameter values other than the standard one,
 # specify the parameters you want to change, e.g. like such:
 result <- simulate_idvaccine_ode(S = 2000, I = 10, tmax = 100, g = 0.5, n = 0.1)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')

Simulation of a compartmental infectious disease transmission model with 2 types of pathogens

Description

This model allows for the simulation of 2 IDs in a single host

Usage

simulate_multipathogen_ode(
 S = 1000,
 I1 = 1,
 I2 = 0,
 I12 = 0,
 b1 = 0.001,
 b2 = 0,
 b12 = 0,
 g1 = 0.5,
 g2 = 0.5,
 g12 = 0.5,
 a = 0,
 tmax = 100
)

Arguments

Details

A compartmental ID model with several states/compartments is simulated as a set of ordinary differential equations. The function returns the output from the odesolver as a matrix, with one column per compartment/variable. The first column is time.

Value

This function returns the simulation result as obtained from a call to the deSolve ode solver.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. any negative values or fractions > 1), the code will likely abort with an error message.

Author(s)

Andreas Handel and Spencer Hall

References

See e.g. Keeling and Rohani 2008 for SIR models and the documentation for the deSolve package for details on ODE solvers

See Also

The UI of the Shiny app 'Multi-Pathogen Dynamics', which is part of this package, contains more details on the model

Examples

 # To run the simulation with default parameters just call the function:
 result <- simulate_multipathogen_ode()
 # To choose parameter values other than the standard one, specify them like such:
 result <- simulate_multipathogen_ode(S = 100, I2 = 10, tmax = 100, b1 = 2.5)
 # You should then use the simulation result returned from the function, like this:
 plot(result$ts[,"time"],result$ts[,"I1"], xlab="Time",ylab="Number Infected Type 1",type="l")

Fitting a simple SIR type model to norovirus outbreak data

Description

This function runs a simulation of a compartment model using a set of ordinary differential equations. The model describes a simple SIR model with an additional environmental source of infection The user provides initial conditions and parameter values for the system. The function simulates the ODE using an ODE solver from the deSolve package.

Usage

simulate_noro_fit(
 S = 100,
 I = 1,
 R = 0,
 b = 0.001,
 blow = 1e-10,
 bhigh = 0.1,
 g = 0.5,
 glow = 0.001,
 ghigh = 100,
 n = 0,
 nlow = 0,
 nhigh = 1000,
 t1 = 8,
 t2 = 15,
 fitmodel = 1,
 iter = 100,
 solvertype = 1
)

Arguments

Details

Three versions of a simple SIR type compartmental ODE model are fit to cases of norovirus during an outbreak. #' @section Warning: This function does not perform any error checking. So if you try to do something nonsensical (e.g. specify negative parameter or starting values), the code will likely abort with an error message.

Value

The function returns a list containing the best fit timeseries, the best fit parameters, the data and the AICc for the model.

Author(s)

Andreas Handel

See Also

See the Shiny app documentation corresponding to this function for more details on this model.

Examples

# To run the code with default parameters just call the function:
## Not run: result <- simulate_noro_fit()
# To apply different settings, provide them to the simulator function, like such:
result <- simulate_noro_fit(iter = 5, fitmodel = 2)