ConTESSA

A shiny app to help EVALUATE THE IMPACT OF COVID-19 TEST-TRACE-ISOLATE PROGRAMS

Lucy D’Agostino McGowan

Wake Forest University

@LucyStats

lucymcgowan.com/talk

The test-trace-isolate model behind the scenes

Some key aspects of the shiny application

The tti package to implement the model

A demonstration of how it works

01. The model

03. The Shiny App

02. The R package

04. Demo

The goal of this work

Test-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions.

​

Effective use of such programs requires methods to estimate their current and anticipated impact.

The model

01.

The test-trace-isolate model behind the scenes

https://doi.org/10.1101/2020.09.02.20186916

A mathEmatical framework

for estimating the effective reproductive number given a test-trace-isolate program

Reproductive Number

The average number of onward transmissions an infected individual is expected to make

Compartments

Infections detected through testing & subsequently isolated

Infections in the community (undetected)

Infections among quarantined contacts of identified cases

Detect

Community

Quarantine

Compartments

Infections detected through testing and subsequently isolated

Infections in the community (undetected)

Infections among quarantined contacts of identified cases

Detect

Community

Quarantine

Compartments

Infections detected through testing and subsequently isolated

Infections in the community (undetected)

Infections among quarantined contacts of identified cases

Detect

Community

Quarantine

Compartments

Infections detected through testing and subsequently isolated

Infections in the community (undetected)

Infections among quarantined contacts of identified cases

Detect

Community

Quarantine

Proportion of infections in each Compartment at time t

Detect

Community

Quarantine

Proportion of infections in each Compartment at time t

0.2

0.7

0.1

Detect

Community

Quarantine

Reproductive Number

The average number of onward transmissions an infected individual is expected to make

Reproductive Number

R_D = 2

R_C = 2.5

R_Q = 1

Detect

Community

Quarantine

Grantz, K. H., Lee, E. C., D’Agostino McGowan, L.., Lee, K. H., Metcalf, C. J. E., Gurley, E. S., & Lessler, J. (2020). Maximizing and evaluating the impact of test-trace-isolate programs. medRxiv.

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Time from symptom onset to isolation

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Time from symptom onset to isolation

infectiousness distribution

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Time from symptom onset of case to quarantine of contacts

Reproductive Number

R_D =γ_DR_C

R_C

R_Q =γ_QR_C

Detect

Community

Quarantine

Time from symptom onset of case to quarantine of contacts

infectiousness distribution

Reproductive Number

R_D = 2

R_C = 2.5

R_Q = 1

Detect

Community

Quarantine

Infection matrix

Infected by

Someone detected

Someone in quarantine

Someone in the community

New infections at time t

x

New infections at time t

Detect

Community

Quarantine

0.2

0.7

0.1

New infections from time t

Detect

Community

Quarantine

0.2 x 2

0.7 x 2.5

0.1 x 1

How are they detected?

Infected by

Someone detected

Someone in quarantine

Someone in the community

How are they detected?

Infected by

Someone detected

Someone in quarantine

Someone in the community

End up detected

End up in quarantine

End up in the

community (undetected)

How are they detected?

Infected by

Someone detected

Someone in quarantine

Someone in the community

End up detected

End up in quarantine

End up in the

community (undetected)

How are they detected?

How are they detected?

How are they detected?

ω_D: probability that a contact of a detected individual in the detect compartment is traced and quarantined

​

How are they detected?

How are they detected?

ω_Q: probability that a contact of a detected individual in the quarantine compartment is traced and quarantined

​

How are they detected?

How are they detected?

ρ: probability that a community infection is detected and isolated

​

How are they detected?

Detection Matrix

Infected by

Someone detected

Someone in quarantine

Someone in the community

End up detected

End up in quarantine

End up in the

community (undetected)

Grantz, K. H., Lee, E. C., D’Agostino McGowan, L.., Lee, K. H., Metcalf, C. J. E., Gurley, E. S., & Lessler, J. (2020). Maximizing and evaluating the impact of test-trace-isolate programs. medRxiv.

Proportion of infections at time t+1

x

Proportion of infections at time t

Infect matrix

x

Detect matrix

Proportion of infections at time t+1

x

Proportion of infections at time t

Infect matrix

x

Detect matrix

Proportion of infections at time t+1

x

Proportion of infections at time t

Infect matrix

x

Detect matrix

(Normalized)

Proportion of infections at time t+1

x

Proportion of infections at time t

Infect matrix

x

Detect matrix

(Normalized) divide by ∑DQC(Infect)

Proportion of infections at time t+1

0.18

0.70

0.12

Detect

Community

Quarantine

Effective reproductive number at time t+1

Detect

Community

Quarantine

0.18 x 2

0.70 x 2.5

0.12 x 1

+

+

Effective reproductive number at time t+1

Detect

Community

Quarantine

0.18 x 2

0.70 x 2.5

0.12 x 1

+

+

2.23

Detection Matrix

Infected by

Someone detected

Someone in quarantine

Someone in the community

End up detected

End up in quarantine

End up in the

community (undetected)

Proportion of infections at time t+1

x

Proportion of infections at time t

Infect matrix

x

Detect matrix

(Normalized) divide by ∑DQC(Infect)

Proportion of infections at time t+1

0.44

0.44

0.22

Detect

Community

Quarantine

Effective reproductive number at time t+1

Detect

Community

Quarantine

0.44 x 2

0.44 x 2.5

0.22 x 1

+

+

2.1

The R

package

02.

The tti R package can be found on GitHub and CRAN

installation

install.packages(“tti”)

devtools::install_github(“HopkinsIDD/tti”)

Default compartments

Ds: Detected and symptomatic�Da: Detected and asymptomatic�Qcds: Quarantined community contact infected by a symptomatic person�Qhds: Quarantined household contact infected by a symptomatic person�Qcda: Quarantined community contact infected by an asymptomatic person�Qhda: Quarantined household contact infected by an asymptomatic person�Qq: Quarantined contact infected by a quarantined person�Cs: In the community and symptomatic�Ca: In the community and asymptomatic

Default compartments

Ds: Detected and symptomatic�Da: Detected and asymptomatic�Qcds: Quarantined community contact infected by a symptomatic person�Qhds: Quarantined household contact infected by a symptomatic person�Qcda: Quarantined community contact infected by an asymptomatic person�Qhda: Quarantined household contact infected by an asymptomatic person�Qq: Quarantined contact infected by a quarantined person�Cs: In the community and symptomatic�Ca: In the community and asymptomatic

Default compartments

Ds: Detected and symptomatic�Da: Detected and asymptomatic�Qcds: Quarantined community contact infected by a symptomatic person�Qhds: Quarantined household contact infected by a symptomatic person�Qcda: Quarantined community contact infected by an asymptomatic person�Qhda: Quarantined household contact infected by an asymptomatic person�Qq: Quarantined contact infected by a quarantined person�Cs: In the community and symptomatic�Ca: In the community and asymptomatic

The main function

get_r_effective_df()

parameters

alpha

The probability of an asymptomatic infection.

R

Reproductive number.

kappa

Relative transmissibility of an asymptomatic individual compared to a symptomatic individual.

eta

Probability contact is a household contact.

nu

Relative risk of infection for a household contact compared to a community contact.

​

parameters

t_ds

Time delay from symptom onset to isolation in detected symptomatic person.

t_da

Time delay from symptom onset to isolation in detected asymptomatic person.

t_qcs

Time delay from symptomatic index cases's symptom onset to quarantine of community contacts.

t_qca

Time delay from asymptomatic index cases's symptom onset to quarantine of community contacts.

t_qhs

Time delay from symptomatic index cases's symptom onset to quarantine of household contacts.

t_qha

Time delay from asymptomatic index cases's symptom onset to quarantine of community contacts.

t_q

Time delay from quarantined index cases's symptom onset to quarantine of contacts.

​

​

parameters

​

omega_c

The probability of being traced and quarantined given community contact of a person.

omega_h

The probability of being traced and quarantined given household contact of a person.

omega_q

The probability of being traced and quarantined given quarantine contact of a person.

quarantine_days

The number of days contacts are told to quarantine.

rho_s

The probability of detection and isolation given symptomatic.

rho_a

The probability of detection and isolation given asymptomatic.

​

​

​

parameters

​

offset

Offset of infectiousness compared to symptoms onset. Default is -2.31.

shape

Shape of the gamma distribution of infectious period. Default is 1.65.

rate

Rate of the gamma distribution of infectious period. Default is 0.5.

stoch

Whether to run stochastic model with overdispersion.

theta

Overdispersion parameter for negative binomial distribution.

n_inf

Number of infections (or effective population size of infected individuals).

n_iter

Number of iterations of stochastic model to run for each unique parameter value.

​

​

​

The main function

> get_r_effective_df(alpha = 0.3)

# A tibble: 1 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = 0.3)

# A tibble: 1 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = 0.3)

# A tibble: 1 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.2, 0.3))

# A tibble: 3 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

2 2.36 0.126 0.2 2.5 0.5 0.5 4 3 3 3

3 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

The main function

> get_r_effective_df(alpha = c(0, 0.3), R = c(2, 2.5))

# A tibble: 4 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 1.88 0.138 0 2 0.5 0.5 4 3 3 3

2 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

3 1.89 0.120 0.3 2 0.5 0.5 4 3 3 3

4 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

​

The main function

> get_r_effective_df(alpha = c(0, 0.3), R = c(2, 2.5))

# A tibble: 4 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 1.88 0.138 0 2 0.5 0.5 4 3 3 3

2 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

3 1.89 0.120 0.3 2 0.5 0.5 4 3 3 3

4 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

​

The main function

> get_r_effective_df(alpha = c(0, 0.3), R = c(2, 2.5))

# A tibble: 4 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 1.88 0.138 0 2 0.5 0.5 4 3 3 3

2 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

3 1.89 0.120 0.3 2 0.5 0.5 4 3 3 3

4 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

​

The main function

> get_r_effective_df(alpha = c(0, 0.3), R = c(2, 2.5))

# A tibble: 4 x 20

r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs

<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 1.88 0.138 0 2 0.5 0.5 4 3 3 3

2 2.35 0.138 0 2.5 0.5 0.5 4 3 3 3

3 1.89 0.120 0.3 2 0.5 0.5 4 3 3 3

4 2.37 0.120 0.3 2.5 0.5 0.5 4 3 3 3

# … with 10 more variables: t_qca <dbl>, t_qhs <dbl>, t_qha <dbl>, t_q <dbl>,

# omega_c <dbl>, omega_h <dbl>, omega_q <dbl>, quarantine_days <dbl>,

# rho_s <dbl>, rho_a <dbl>

​

​

The shiny application

03.

Behind the scenes of ConTESSA

github.com/hopkinsidd/contessa

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

...

}

shinyApp(ui, server)

​

Shiny application

library(shiny)

library(shinydashboard)

​

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(),

dashboardBody()

)

​

server <- function(input, output) {

​

}

shinyApp(ui, server)

​

Report (RMD)

---

title: “Your title”

​

params:

a: NA

b: NA

---

​

​

​

```{r}

print(params$a)

print(params$b)

```

​

Report (RMD)

---

title: “Your title”

​

params:

a: NA

b: NA

---

​

​

​

```{r}

print(params$a)

print(params$b)

```

​

Report (RMD)

---

title: “Your title”

​

params:

a: NA

b: NA

---

​

​

​

```{r}

print(params$a)

print(params$b)

```

​

Report (server)

output$report <- downloadHandler(

filename = "report.pdf",

content <- function(file) {

tmp <- file.path(tempdir(), "report.Rmd")

file.copy("report.Rmd", tmp, overwrite = TRUE)

params <- list(a = input$a, b = input$b)

​

rmarkdown::render(

tmp,

output_file = file,

params = params,

envir = new.env(parent = globalenv())

)

})

​

​

Report (server)

output$report <- downloadHandler(

filename = "report.pdf",

content <- function(file) {

tmp <- file.path(tempdir(), "report.Rmd")

file.copy("report.Rmd", tmp, overwrite = TRUE)

params <- list(a = input$a, b = input$b)

​

rmarkdown::render(

tmp,

output_file = file,

params = params,

envir = new.env(parent = globalenv())

)

})

​

​

Report (server)

output$report <- downloadHandler(

filename = "report.pdf",

content <- function(file) {

tmp <- file.path(tempdir(), "report.Rmd")

file.copy("report.Rmd", tmp, overwrite = TRUE)

params <- list(a = input$a, b = input$b)

​

rmarkdown::render(

tmp,

output_file = file,

params = params,

envir = new.env(parent = globalenv())

)

})

​

​

Report (server)

output$report <- downloadHandler(

filename = "report.pdf",

content <- function(file) {

tmp <- file.path(tempdir(), "report.Rmd")

file.copy("report.Rmd", tmp, overwrite = TRUE)

params <- list(a = input$a, b = input$b)

​

rmarkdown::render(

tmp,

output_file = file,

params = params,

envir = new.env(parent = globalenv())

)

})

​

​

Report (server)

output$report <- downloadHandler(

filename = "report.pdf",

content <- function(file) {

tmp <- file.path(tempdir(), "report.Rmd")

file.copy("report.Rmd", tmp, overwrite = TRUE)

params <- list(a = input$a, b = input$b)

​

rmarkdown::render(

tmp,

output_file = file,

params = params,

envir = new.env(parent = globalenv())

)

})

​

​

Report (ui)

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(

downloadButton("report", "Generate report")

),

dashboardBody()

)

​

​

​

Report (ui)

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(

downloadButton("report", "Generate report")

),

dashboardBody()

)

​

​

​

Report (ui)

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(

downloadButton("report", "Generate report")

),

dashboardBody()

)

​

​

​

Report (ui)

ui <- dashboardPage(

dashboardHeader(),

dashboardSidebar(

downloadButton("report", "Generate report")

),

dashboardBody()

)

​

​

​

Save Inputs (server)

output$save <- downloadHandler(

filename <- function() {

glue("{input$file_name}.yaml")

},

content <- function(file) {

inputs <- reactiveValuesToList(input)

input_yaml <- as.yaml(inputs)

write_yaml(input_yaml, file = file)

}

)

​

​

​

​

Save Inputs (server)

output$save <- downloadHandler(

filename <- function() {

glue("{input$file_name}.yaml")

},

content <- function(file) {

inputs <- reactiveValuesToList(input)

input_yaml <- as.yaml(inputs)

write_yaml(input_yaml, file = file)

}

)

​

​

​

​

Save Inputs (server)

output$save <- downloadHandler(

filename <- function() {

glue("{input$file_name}.yaml")

},

content <- function(file) {

inputs <- reactiveValuesToList(input)

input_yaml <- as.yaml(inputs)

write_yaml(input_yaml, file = file)

}

)

​

​

​

​

The

Demo

04.

Demonstration of the ConTESSA Application in Action

iddynamicsjhu.shinyapps.io/contessa-v2/

THANKS!

@LucyStats

CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik

​

​