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server.R
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server.R
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# libraries, global functions and global values are handled in "global.R"
# For the original (non-shiny) code see https://doi.org/10.5281/zenodo.3727255
# where it may be easier to follow without the reactive wrappers
# Define server logic
server <- function(input, output, session) {
# define the reset button
observeEvent(input$reset_input, {
shinyjs::reset("sidePanel")
})
#####################
# Infectiousness tab
#####################
### Get parameters
# Parameters start using our published values and then can be altered by the user
getDoublingTime <- reactive({
input$doublingTime
})
getEnvInfType <- reactive({
input$env.type
})
getEnvDecay <- reactive({
input$envDecayRate
})
getEnvConst <- reactive({
input$envConstant
})
getIncperMeanlog <- reactive({
log(input$incperMedian)
})
getIncperSdlog <- reactive({
input$incperSdlog
})
getSerIntShape <- reactive({
input$serIntShape
})
getSerIntScale <- reactive({
input$serIntScale
})
getXp <- reactive({
1 # relative infectiousness of presymptomatic to symptomatic individuals (fixed at 1)
})
getXa <- reactive({
input$xa # the relative infectiousness of asymptomatic c.f. symptomatic individuals
})
getP.a <- reactive({
input$P.a # the fraction of all infections that are asymptomatic
})
getFrac.Re <- reactive({
input$frac.Re # the fraction of all transmissions that are environmentally mediated
})
################
# Calculations
###############
# following Chris's method, create "dummy" which uses model.gen.solve and other functions to create a list of the values for
# env.scale.constant
# R0
# RSorP
# RA
# RS
# RP
# RE
# theta.obs.predicted
getDummy <- reactive({
# get parameters
frac.Re <- getFrac.Re()
P.a <- getP.a()
doubling.time <- getDoublingTime()
xp <- getXp()
xa <- getXa()
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
serint.shape <- getSerIntShape()
serint.scale <- getSerIntScale()
theta.obs <- 0.83
env.decay.rate <- getEnvDecay()
env.constant.duration <- getEnvConst()
env.infectiousness.type <- getEnvInfType()
# get reactive functions
model.gen.beta.env.div.by.E.RSorP <- getModelGenBetaEnvDivByERSorP()
model.gen.full.beta.div.by.RSorP <- getModelGenFullBetaDivByRSorP()
# use "model.gen.solve" in a reactive way:
r <- log(2) / doubling.time # units of per day
integral.of.model.gen.beta.s.div.by.RSorP <-
integrate(model.gen.beta.s.div.by.RSorP,
lower = 0,
upper = Inf,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp)$value
validate(need(try( env.scale.constant <- (1 + P.a * xa * integral.of.model.gen.beta.s.div.by.RSorP) /
(((1 / frac.Re) - 1) * integrate(model.gen.beta.env.div.by.E.RSorP,
lower = 0,
upper = Inf,
serint.scale = serint.scale,
serint.shape = serint.shape,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)$value
)
), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders"))
RSorP <- 1 / integrate(function(tau) {
model.gen.full.beta.div.by.RSorP(tau = tau,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xa = xa,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
xp = xp,
env.scale.constant = env.scale.constant,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type) *
exp(-r * tau)}, lower = 0, upper = Inf)$value
R0 <- RSorP * integrate(model.gen.full.beta.div.by.RSorP,
lower = 0,
upper = Inf,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xa = xa,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
xp = xp,
env.scale.constant = env.scale.constant,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)$value
should.equal.one <- integrate(function(tau) {exp(-r * tau) * RSorP *
model.gen.full.beta.div.by.RSorP(tau = tau,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xa = xa,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
xp = xp,
env.scale.constant = env.scale.constant,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)},
lower = 0, upper = Inf)$value
RS <- integrate(model.gen.beta.sym.tot,
lower = 0,
upper = Inf,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp,
RSorP = RSorP)$value
RP <- integrate(model.gen.beta.presym.tot,
lower = 0,
upper = Inf,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp,
RSorP = RSorP)$value
RA <- RSorP * P.a * xa * integral.of.model.gen.beta.s.div.by.RSorP
RE <- env.scale.constant *
RSorP *
integrate(function(tau) {
model.gen.beta.env.div.by.E.RSorP(tau = tau,
serint.scale = serint.scale,
serint.shape = serint.shape,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)},
lower = 0, upper = Inf)$value
theta.obs.predicted <- 1 - integrate(function(tau) {
model.gen.beta.sym.tot(tau = tau,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp,
RSorP = RSorP) *
exp(-r * tau)},
lower = 0, upper = Inf)$value
# return
list(env.scale.constant = env.scale.constant,
R0 = R0,
RSorP = RSorP,
RA = RA,
RS = RS,
RP = RP,
RE = RE,
theta.obs.predicted = theta.obs.predicted)
})
# extract each component from the list "dummy"
getR0 <- reactive({
dummy <- getDummy()
dummy$R0
})
getRSorP <- reactive({
dummy <- getDummy()
dummy$RSorP
})
getRe <- reactive({
dummy <- getDummy()
dummy$RE
})
getRa <- reactive({
dummy <- getDummy()
dummy$RA
})
getRs <- reactive({
dummy <- getDummy()
dummy$RS
})
getRp <- reactive({
dummy <- getDummy()
dummy$RP
})
getTheta <- reactive({
rp <- getRp()
R0 <- getR0()
1 - rp / R0
})
getEnvScaleConst <- reactive({
dummy <- getDummy()
dummy$env.scale.constant
})
# following "model.gen.beta.env.div.by.E.RSorP"
getModelGenBetaEnvDivByERSorP <- reactive({
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
P.a <- getP.a()
xp <- getXp()
env.decay.rate <- getEnvDecay()
env.constant.duration <- getEnvConst()
env.infectiousness.type <- getEnvInfType()
# because of the way that "env.decay.rate" and "env.constant.duration" depend on
# "env.infectiousness.type", there is a delay when shiny is launched, when these are undefined
# so we use "req" to wait until they are defined before continuing the calculation
if (env.infectiousness.type == "constant") {
req(env.constant.duration)
} else {
req(env.decay.rate)
}
Vectorize(function(tau,
incper.meanlog,
incper.sdlog,
serint.scale,
serint.shape,
P.a,
xp,
env.decay.rate,
env.constant.duration,
env.infectiousness.type) {
integrate(function(l) {
model.gen.beta.s.div.by.RSorP(tau = tau - l,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp) *
p.el(l = l,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)
},
lower = 0, upper = tau)$value
}, vectorize.args = "tau")
})
# following model.gen.full.beta.div.by.RSorP
getModelGenFullBetaDivByRSorP <- reactive({
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
P.a <- getP.a()
xa <- getXa()
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
xp <- getXp()
env.decay.rate <- getEnvDecay()
env.constant.duration <- getEnvConst()
env.infectiousness.type <- getEnvInfType()
model.gen.beta.env.div.by.E.RSorP <- getModelGenBetaEnvDivByERSorP()
function(tau, serint.scale, serint.shape, P.a, xa, incper.meanlog,
incper.sdlog, xp, env.scale.constant, env.decay.rate,
env.constant.duration, env.infectiousness.type) {
serint(x = tau,
serint.scale = serint.scale,
serint.shape = serint.shape) *
(1 + P.a * xa / model.gen.f(tau = tau,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp)) +
env.scale.constant * # this is a free variable of this function
model.gen.beta.env.div.by.E.RSorP(tau = tau,
serint.scale = serint.scale,
serint.shape = serint.shape,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)
}
})
################################
# Build data frames of results
################################
# a reactive version of df.beta.p
getDFBetaP <- reactive({
# get parameters
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
P.a <- getP.a()
xp <- getXp()
RSorP <- getRSorP()
data.frame(tau = tau.test,
label = "pre-symptomatic",
beta = vapply(tau.test,
model.gen.beta.presym.tot,
numeric(1),
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp,
RSorP = RSorP))
})
# a reactive version of df.beta.s
getDFBetaS <- reactive({
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
P.a <- getP.a()
xp <- getXp()
RSorP <- getRSorP()
data.frame(tau = tau.test,
label = "symptomatic",
beta = vapply(tau.test,
model.gen.beta.sym.tot,
numeric(1),
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp,
RSorP = RSorP))
})
# a reactive version of df.beta.a
getDFBetaA <- reactive({
xp <- getXp()
xa <- getXa()
P.a <- getP.a()
RSorP <- getRSorP()
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
data.frame(tau = tau.test,
label = "asymptomatic",
beta = vapply(tau.test, function(tau) {
RSorP * P.a * xa * model.gen.beta.s.div.by.RSorP(tau = tau,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a, xp = xp)} , numeric(1)))
})
# a reactive version of df.beta.e
getDFBetaE <- reactive({
model.gen.beta.env.div.by.E.RSorP <- getModelGenBetaEnvDivByERSorP()
xp <- getXp()
P.a <- getP.a()
RSorP <- getRSorP()
serint.scale <- getSerIntScale()
serint.shape <- getSerIntShape()
incper.meanlog <- getIncperMeanlog()
incper.sdlog <- getIncperSdlog()
env.decay.rate <- getEnvDecay()
env.constant.duration <- getEnvConst()
env.infectiousness.type <- getEnvInfType()
env.scale.constant <- getEnvScaleConst()
df.beta.e <- data.frame(tau = tau.test,
label = "environmental",
beta = vapply(tau.test,
model.gen.beta.env.div.by.E.RSorP,
numeric(1),
serint.scale = serint.scale,
serint.shape = serint.shape,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)
)
df.beta.e$beta <- df.beta.e$beta * env.scale.constant * RSorP
df.beta.e
})
# collect each df.beta into a data frame, for plotting
getDF <- reactive({
validate(
need(try(df.beta.p <- getDFBetaP()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.s <- getDFBetaS()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.e <- getDFBetaE()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.a <- getDFBetaA()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
rbind(df.beta.p, df.beta.s, df.beta.e, df.beta.a) # if you change the order of these you also need to change the order of the legend labels!
})
################
# For plotting
################
# get ymax for plotting
getYmax <- reactive({
df.plot <- getDF()
df.plot.wide <- spread(df.plot, label, beta)
df.plot.wide$max <- df.plot.wide$`pre-symptomatic` + df.plot.wide$symptomatic +
df.plot.wide$environmental + df.plot.wide$asymptomatic
ymax <- max(df.plot.wide$max * 1.05)
})
# an example of how to set the colours; this can be a user input if desired
getCols <- reactive({
brewer.pal(8, "Paired")[c(1,3,5,7,2)] # palette which is colour-blind friendly but doesn't risk implying any pairing. Four colours for main plot, final colour for mini plots
})
# get the main plot using the latest data frame
getPlot <- reactive({
df.plot <- getDF()
ymax <- getYmax()
cols <- getCols()
RS <- getRs()
RP <- getRp()
RE <- getRe()
RA <- getRa()
R0 <- getR0()
mainPlot <- ggplot(df.plot, aes(x=tau, y=beta)) + #, color=label)) +
theme_bw(base_size = 18) +
geom_area(aes(fill=label)) +
labs(x = expression(paste("Days, ",tau)),
y = expression(paste("New infections per day, ",beta, "(", tau,")")),
fill = bquote(paste('R'['0'] * ' = ' * .(format(round(R0, 1), nsmall = 1)) * ":" ))
) +
coord_cartesian(xlim = c(0, max(tau.test)), ylim = c(0, ymax), expand = F) +
scale_fill_manual(values = cols,
labels = c(
bquote(paste('R'['p'] * ' = ' * .(format(round(RP, 1), nsmall = 1)) * " from pre-symptomatic")),
bquote(paste('R'['s'] * ' = ' * .(format(round(RS, 1), nsmall = 1)) * " from symptomatic")),
bquote(paste('R'['e'] * ' = ' * .(format(round(RE, 1), nsmall = 1)) * " from environmental")),
bquote(paste('R'['a'] * ' = ' * .(format(round(RA, 1), nsmall = 1)) * " from asymptomatic"))))
#scale_x_continuous(minor_breaks = minor_breaks, breaks = breaks)
grid.arrange(mainPlot, ncol=1)
})
# create four mini plots, decomposing the main plot into each category
getDecompositionPlot <- reactive({
validate(
need(try(df.beta.p <- getDFBetaP()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.s <- getDFBetaS()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.e <- getDFBetaE()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
validate(
need(try(df.beta.a <- getDFBetaA()), "Parameters are too extreme for the integral to converge, please adjust one or more of the sliders")
)
ymax <- getYmax()
cols <- getCols()
RS <- getRs()
RP <- getRp()
RE <- getRe()
RA <- getRa()
R0 <- getR0()
p <- ggplot(df.beta.p, aes(x=tau, y=beta)) +
theme_bw(base_size = 18) +
theme(legend.position="none") +
geom_area(aes(fill=label)) +
labs(x = expression(paste("Days, ",tau)),
y = expression(paste("Contribution to ", beta, "(", tau,")"))
) +
coord_cartesian(xlim = c(0, max(tau.test)), ylim = c(0, ymax), expand = F) +
scale_fill_manual(values = cols[[1]])
s <- ggplot(df.beta.s, aes(x=tau, y=beta)) +
theme_bw(base_size = 18) +
theme(legend.position="none") +
geom_area(aes(fill=label)) +
labs(x = expression(paste("Days, ",tau)),
y = ""
) +
coord_cartesian(xlim = c(0, max(tau.test)), ylim = c(0, ymax), expand = F) +
scale_fill_manual(values = cols[[2]])
e <- ggplot(df.beta.e, aes(x=tau, y=beta)) +
theme_bw(base_size = 18) +
theme(legend.position="none") +
geom_area(aes(fill=label)) +
labs(x = expression(paste("Days, ",tau)),
y = ""
) +
coord_cartesian(xlim = c(0, max(tau.test)), ylim = c(0, ymax), expand = F) +
scale_fill_manual(values = cols[[3]])
a <- ggplot(df.beta.a, aes(x=tau, y=beta)) +
theme_bw(base_size = 18) +
theme(legend.position="none") +
geom_area(aes(fill=label)) +
labs(x = expression(paste("Days, ",tau)),
y = ""
) +
coord_cartesian(xlim = c(0, max(tau.test)), ylim = c(0, ymax), expand = F) +
scale_fill_manual(values = cols[[4]])
grid.arrange(p,s,e,a, ncol=4)
})
# get the parameter summary to be written below the plot
getParameterSummary <- reactive({
validate(
need(try(theta <- getTheta()), "")
)
HTML(paste0("<h4>
<br/>
<br/> Proportion of infections which are not from direct contact with symptomatic individuals Θ = ", round(theta, 2),
"</h4>")) # using html "θ" to get symbol theta
})
### Make mini plot to demonstrate the user-defined incubation period distribution with
# the mean and sd they have specified.
getIncperDistributionPlot <- reactive({
req(getIncperMeanlog()) # can't plot it until these values have been calculated
req(getIncperSdlog())
m <- getIncperMeanlog()
s <- getIncperSdlog()
cols <- getCols()
# Make sure we capture at least 99% of the distribution in the plot
xmax <- 20 #max(qlnorm(0.99, meanlog = m, sdlog = s), 20)
x <- seq(0,xmax,by=0.01)
y <- sapply(x, function(x) plnorm(x,m,s))
ggplot(cbind.data.frame(x,y), aes(x=x, y=y)) +
geom_line(col=cols[[5]]) +
theme_classic() +
coord_cartesian(expand = F, xlim = c(0, xmax), ylim = c(0, 1.05 * max(y))) +
labs(x = "Incubation period (days)", y = "Probability density")
})
# mini plot for generation time distribution
getSerintDistributionPlot <- reactive({
req(getSerIntShape()) # can't plot it until these values have been calculated
req(getSerIntScale())
m <- getSerIntShape()
s <- getSerIntScale()
cols <- getCols()
xmax <- 20
x <- seq(0,xmax,by=0.01)
y <- sapply(x, function(x) dweibull(x,m,s))
ggplot(cbind.data.frame(x,y), aes(x=x, y=y)) +
geom_line(col=cols[[5]]) +
theme_classic() +
coord_cartesian(expand = F, xlim = c(0, xmax), ylim = c(0, 1.05 * max(y))) +
labs(x = "Generation time (days)", y = "Probability density")
})
###############################################
# Reactive sidebar input / outputs
###############################################
getEnvSliders <- reactive({
type <- input$env.type
if (type=="exp.decay") {
sliderInput("envDecayRate",
h6("Environmental infectiousness exponential decay rate (per day):"),
min = 0.05,
max = 5,
step = 0.01,
value = 2.3)
} else {
sliderInput("envConstant",
h6("Environmental infectiousness duration (days):"),
min = 1,
max = 10,
step = 0.01,
value = 3)
}
})
###########
# OUTPUTS
##########
# main plot
output$mainPlot <- renderPlot({
validate(need(getPlot(), ""))
})
# decomposed version of plot
output$decompositionPlot <- renderPlot({
validate(need(getDecompositionPlot(), ""))
})
# parameter summary below plot
output$parameterSummary <- renderUI({
validate(need(getParameterSummary(), ""))
getParameterSummary()
})
# SIDEBAR:
# mini plot to demonstrate incper distribution as chosen by user
output$IncperDistribPlot <- renderPlot({
getIncperDistributionPlot()
})
# mini plot to demonstrate serint distribution as chosen by user
output$SerintDistribPlot <- renderPlot({
getSerintDistributionPlot()
})
# environment sliders, depending on type
output$environment_sliders <- renderUI({
getEnvSliders()
})
#####################################################################################################
#####################
# Interventions tab
#####################
# define the reset button
observeEvent(input$reset_inputContour, {
shinyjs::reset("sidePanel2")
shinyjs::reset("mainPanel2")
})
# Largely a repeat of everything above, with "Contour" appended! Plus delay slider:
getDelay <- reactive({
input$delay
})
getIncperMedianlogContour <- reactive({
log(input$incperMedianContour)
})
getIncperSdlogContour <- reactive({
input$incperSdlogContour
})
getSerIntShapeContour <- reactive({
input$serIntShapeContour
})
getSerIntScaleContour <- reactive({
input$serIntScaleContour
})
getDoublingTimeContour <- reactive({
input$doublingTimeContour
})
getXpContour <- reactive({
1
})
getXaContour <- reactive({
input$xaContour # the relative infectiousness of asymptomatic c.f. symptomatic individuals
})
getP.aContour <- reactive({
input$P.aContour # the fraction of all infections that are asymptomatic
})
getFrac.ReContour <- reactive({
input$frac.ReContour # the fraction of all transmissions that are environmentally mediated
})
### Make mini plot to demonstrate the user-defined incubation period distribution with
# the mean and sd they have specified.
getIncperDistributionPlotContour <- reactive({
req(getIncperMedianlogContour()) # can't plot it until these values have been calculated
req(getIncperSdlogContour())
m <- getIncperMedianlogContour()
s <- getIncperSdlogContour()
cols <- getCols()
xmax <- 20
x <- seq(0,xmax,by=0.01)
y <- sapply(x, function(x) plnorm(x,m,s))
ggplot(cbind.data.frame(x,y), aes(x=x, y=y)) +
geom_line(col=cols[[5]]) +
theme_classic() +
coord_cartesian(expand = F, xlim = c(0, xmax), ylim = c(0, 1.05 * max(y))) +
labs(x = "Incubation period (days)", y = "Probability density")
})
# mini plot for generation time distribution
getSerintDistributionPlotContour <- reactive({
req(getSerIntShapeContour()) # can't plot it until these values have been calculated
req(getSerIntScaleContour())
m <- getSerIntShapeContour()
s <- getSerIntScaleContour()
cols <- getCols()
xmax <- 20
x <- seq(0,xmax,by=0.01)
y <- sapply(x, function(x) dweibull(x,m,s))
ggplot(cbind.data.frame(x,y), aes(x=x, y=y)) +
geom_line(col=cols[[5]]) +
theme_classic() +
coord_cartesian(expand = F, xlim = c(0, xmax), ylim = c(0, 1.05 * max(y))) +
labs(x = "Generation time (days)", y = "Probability density")
})
getFa <- reactive({
P.a <- getP.aContour()
x.a <- getXaContour()
fa = P.a*x.a / (P.a*x.a + (1 - P.a) )
})
# make interactive sliders for environmental decay type
getEnvSlidersContour <- reactive({
type <- input$env.typeContour
if (type=="exp.decay") {
sliderInput("envDecayRateContour",
h6("Environmental infectiousness exponential decay rate (per day):"),
min = 0.05,
max = 5,
step = 0.01,
value = 2.3)
} else {
sliderInput("envConstantContour",
h6("Environmental infectiousness duration (days):"),
min = 1,
max = 10,
step = 0.01,
value = 3)
}
})
getEnvInfTypeContour <- reactive({
input$env.typeContour
})
getEnvDecayContour <- reactive({
input$envDecayRateContour
})
getEnvConstContour <- reactive({
input$envConstantContour
})
# following "model.gen.beta.env.div.by.E.RSorP"
getModelGenBetaEnvDivByERSorPContour <- reactive({
incper.meanlog <- getIncperMedianlogContour()
incper.sdlog <- getIncperSdlogContour()
serint.scale <- getSerIntScaleContour()
serint.shape <- getSerIntShapeContour()
P.a <- getP.aContour()
xp <- getXpContour()
env.decay.rate <- getEnvDecayContour()
env.constant.duration <- getEnvConstContour()
env.infectiousness.type <- getEnvInfTypeContour()
# because of the way that "env.decay.rate" and "env.constant.duration" depend on
# "env.infectiousness.type", there is a delay when shiny is launched, when these are undefined
# so we use "req" to wait until they are defined before continuing the calculation
if (env.infectiousness.type == "constant") {
req(env.constant.duration)
} else {
req(env.decay.rate)
}
Vectorize(function(tau,
incper.meanlog,
incper.sdlog,
serint.scale,
serint.shape,
P.a,
xp,
env.decay.rate,
env.constant.duration,
env.infectiousness.type) {
integrate(function(l) {
model.gen.beta.s.div.by.RSorP(tau = tau - l,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp) *
p.el(l = l,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)
},
lower = 0, upper = tau)$value
}, vectorize.args = "tau")
})
# following model.gen.full.beta.div.by.RSorP
getModelGenFullBetaDivByRSorPContour <- reactive({
serint.scale <- getSerIntScaleContour()
serint.shape <- getSerIntShapeContour()
P.a <- getP.aContour()
xa <- getXaContour()
incper.meanlog <- getIncperMedianlogContour()
incper.sdlog <- getIncperSdlogContour()
xp <- getXpContour()
env.decay.rate <- getEnvDecayContour()
env.constant.duration <- getEnvConstContour()
env.infectiousness.type <- getEnvInfTypeContour()
model.gen.beta.env.div.by.E.RSorP <- getModelGenBetaEnvDivByERSorPContour()
function(tau, serint.scale, serint.shape, P.a, xa, incper.meanlog,
incper.sdlog, xp, env.scale.constant, env.decay.rate,
env.constant.duration, env.infectiousness.type) {
serint(x = tau,
serint.scale = serint.scale,
serint.shape = serint.shape) *
(1 + P.a * xa / model.gen.f(tau = tau,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp)) +
env.scale.constant * # this is a free variable of this function
model.gen.beta.env.div.by.E.RSorP(tau = tau,
serint.scale = serint.scale,
serint.shape = serint.shape,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
P.a = P.a,
xp = xp,
env.decay.rate = env.decay.rate,
env.constant.duration = env.constant.duration,
env.infectiousness.type = env.infectiousness.type)
}
})
# following "getDummy()" from the infectiousness tab, but slimmed down because we only need to get R0 out
getR0Contour <- reactive({
# get parameters
frac.Re <- getFrac.ReContour()
P.a <- getP.aContour()
doubling.time <- getDoublingTimeContour()
xp <- getXpContour()
xa <- getXaContour()
incper.meanlog <- getIncperMedianlogContour()
incper.sdlog <- getIncperSdlogContour()
serint.shape <- getSerIntShapeContour()
serint.scale <- getSerIntScaleContour()
theta.obs <- 0.83
env.decay.rate <- getEnvDecayContour()
env.constant.duration <- getEnvConstContour()
env.infectiousness.type <- getEnvInfTypeContour()
# get reactive functions
model.gen.beta.env.div.by.E.RSorP <- getModelGenBetaEnvDivByERSorPContour()
model.gen.full.beta.div.by.RSorP <- getModelGenFullBetaDivByRSorPContour()
# use "model.gen.solve" in a reactive way:
r <- log(2) / doubling.time # units of per day. This is r before interventions
integral.of.model.gen.beta.s.div.by.RSorP <-
integrate(model.gen.beta.s.div.by.RSorP,
lower = 0,
upper = Inf,
incper.meanlog = incper.meanlog,
incper.sdlog = incper.sdlog,
serint.scale = serint.scale,
serint.shape = serint.shape,
P.a = P.a,
xp = xp)$value