This code estimates incidence of new infections and the instantaneous reproduction number R in April-June across Upper Tier Local Authorities of England, as reported in our paper.
library(tidyverse)
library(EpiEstim)
library(incidence)
library(plotly)
library(ggplot2)From the working directory, fetch the Pillar 1 daily case data:
dat.UK.p1 <- read_csv("data/pillar1_case_data.csv")and the population data:
population.data <- read.csv("data/population_by_region.csv", stringsAsFactors = FALSE)# zeta is the distribution for the delay from infection to case confirmation
zeta.max <- 35 # hard-code that no delays are ever longer than that
delays.considered <- seq(0, zeta.max)
# incubation period
s.meanlog <- 1.58
s.sdlog <- 0.47
s <- function(tau) {dlnorm(tau, sdlog = s.sdlog, meanlog = s.meanlog)}
s.discrete <- vapply(delays.considered, s, numeric(1))
s.discrete <- s.discrete / sum(s.discrete)
symptom.to.swab.mean <- 5.14
symptom.to.swab.sd <- 4.2
symptom.to.swab.shape <- symptom.to.swab.mean^2 / symptom.to.swab.sd^2
symptom.to.swab.rate <- symptom.to.swab.shape / symptom.to.swab.mean
symptom.to.swab.pdf <- function(t) {
dgamma(t, shape = symptom.to.swab.shape, rate = symptom.to.swab.rate)
}
symptom.to.swab.discrete <- vapply(delays.considered, symptom.to.swab.pdf, numeric(1))
symptom.to.swab.discrete <- symptom.to.swab.discrete / sum(symptom.to.swab.discrete)
# Zeta is the convolution of s and symptom to swab time.
# Careful: we include day zero but vector indexing is 1-based
zeta <- rep(NA, zeta.max + 1)
for (t in delays.considered) {
convolution.sum.range <- seq(0, t)
zeta[[t + 1]] <- sum(s.discrete[convolution.sum.range + 1] *
symptom.to.swab.discrete[t - convolution.sum.range + 1])
}
zeta <- zeta / sum(zeta) # unit normaliseFilter to England (for consistent Pillar 1 reporting in this time period) and prepare global parameters of start date, last date and area names:
# just England:
dat.Eng.p1 <- dat.UK.p1 %>%
filter(Country == "England")
start.date <- as.Date("2020-04-01")
last.date <- as.Date(dat.Eng.p1$Date[[nrow(dat.Eng.p1)]])
areas.alphabetical <- sort(unique(dat.Eng.p1$Area)) # put areas in alphabetical order, for saving togetherExtract the daily new cases for each area:
area.incidence <- lapply(areas.alphabetical, function(area) {
dat.area <- dat.Eng.p1[dat.Eng.p1$Area==area,]
# remove any rows where "total cases" is NaN
if (any(is.na(dat.area$TotalCases))) dat.area <- dat.area[-which(is.na(dat.area$TotalCases)),]
dat.area$Date <- as.Date(as.character(dat.area$Date))
dat.area$Incidence <- rep(0,nrow(dat.area))
dat.area$Incidence[1] <- dat.area$TotalCases[1]
for(row in 2:nrow(dat.area)) dat.area$Incidence[row] <- dat.area$TotalCases[row] - dat.area$TotalCases[row-1]
area.linelist <- dat.area$Date[1]
for(row in 1:nrow(dat.area)){
if(dat.area$Incidence[row]>0){
for(case in 1:dat.area$Incidence[row]){
area.linelist <- c(area.linelist, dat.area$Date[row])
}
}
}
area.linelist <- area.linelist[-1]
incidence(area.linelist,
first_date = start.date,
last_date = last.date,
standard = FALSE)
})Compute the daily new infections for each area using the backcalculation:
area.incidence.backcalculation <- lapply(1:150, function(x) {
# Get the case counts time series, fill in any missing dates, and add in dates
# beforehand reaching back to the maximum possible delay (which induces NAs,
# all of which we set to zero).
df <- cbind.data.frame(
"dates" = area.incidence[[x]]$dates,
"counts" = area.incidence[[x]]$counts
)
df <- df %>%
complete(dates = seq.Date(min(dates) - zeta.max, max(dates), by="day")) %>%
replace_na(list(counts = 0))
df$infections <- 0
for (days.plus.1.since.start in seq(1, nrow(df))) {
integration.range <- seq(days.plus.1.since.start,
min(nrow(df), days.plus.1.since.start + zeta.max))
df$infections[[days.plus.1.since.start]] <-
sum(df$counts[integration.range] *
zeta[integration.range - days.plus.1.since.start + 1])
}
df
})And use these to estimate the instantaneous reproduction number R for each area:
t_start <- seq(2,as.numeric(last.date - start.date) + zeta.max - 5)
t_end <- t_start + 7 - 1
area.R.backcalculated <- lapply(1:150, function(i) {
df <- area.incidence.backcalculation[[i]]
# df contains data for the region: date, swab count, inferred new infections
# use inferred new infections to estimate R
area.incidence <- cbind.data.frame(
"dates" = df$dates,
"I" = df$infections
)
area.R.backcalculated <- estimate_R(area.incidence,
method="parametric_si",
config = make_config(list(
t_start = t_start,
t_end = t_end,
mean_si = 5.5, # NB now using the generation time distribution because we're using inferred times of infection, not cases
std_si = 2.14,
mean_prior = 1,
std_prior = 1))
)
# change to mode
area.R.backcalculated$R$`Mean(R)` <- area.R.backcalculated$R$`Mean(R)` - (area.R.backcalculated$R$`Std(R)`)^2 / area.R.backcalculated$R$`Mean(R)`
area.R.backcalculated
})
# rename for convenience
area.analysis <- area.R.backcalculatedFinally, extract the population data by area:
population.by.area <- sapply(areas.alphabetical, function(x) {
tmp <- population.data %>% filter(Name == x)
tmp$All.ages
})Simple manipulations of these results will reproduce the plots of our paper (using ggplot2) or our shiny app (using plotly). For example, this plot shows the nowcast for each area of England, with the Isle of Wight highlighted in red:
projected_cases <- cbind.data.frame(
"Dates" = unlist(lapply(1:150, function(area) area.R.backcalculated[[area]]$dates[-(1:7)] - 4)),
"Projection" = unlist(lapply(1:150, function(area) {
sapply(area.R.backcalculated[[area]]$R$t_end, function(i) { # the first t_end is 8
mean(area.R.backcalculated[[area]]$I[(i-6):i]) * # average incidence over that week
area.R.backcalculated[[area]]$R$`Mean(R)`[[which(area.R.backcalculated[[area]]$R$t_end == i)]] # last R value
})
}
)),
"Area"= unlist(lapply(1:150, function(area) rep(areas.alphabetical[[area]], nrow(area.R.backcalculated[[area]]$R))))
)
# scale per capita
projected_cases$scaled_per_capita <- sapply(1:nrow(projected_cases), function(x) {
area <- projected_cases[x,]$Area
pop.this.area <- population.by.area[which(areas.alphabetical == area)][[1]]
if(length(pop.this.area)==0) NA
else projected_cases$Projection[[x]] / pop.this.area * 100000
})
# correct the date formatting
projected_cases$Dates <- as.Date(projected_cases$Dates, origin = as.Date("1970-01-01"))
# plot setup
f1 <- list(
family = "Arial, sans-serif",
size = 22,
color = "darkgrey"
)
UTLAToHighlight <- "Isle of Wight"
nowcast.plot <- projected_cases %>%
group_by(Area) %>%
plot_ly(x=~Dates, y=~scaled_per_capita) %>%
add_lines(alpha=0.3,
color = I("lightgrey"),
hovertemplate = paste(
'<b>',projected_cases$Area,'</b><br>',
'<i>%{x|%d %B}</i><br>',
'%{y:.1f} infections per 100,000<extra></extra>')) %>%
layout(xaxis = list(
title = "",
titlefont = f1,
showticklabels = TRUE,
tickfont = f1,
exponentformat = "E",
range=c(start.date, last.date - 15)
),
yaxis = list(
title = "Expected hospital infections per day in the\nnear future per 100,000 population",
titlefont = f1,
showticklabels = TRUE,
tickfont = f1,
exponentformat = "E",
range = c(0,15)
), showlegend = FALSE) %>%
filter(Area == UTLAToHighlight) %>%
add_lines(color = I("red"),
line=list(width=4, alpha=1),
hovertemplate = paste(
'<b>',UTLAToHighlight,'</b><br>',
'<i>%{x|%d %B}</i><br>',
'%{y:.1f} infections per 100,000<extra></extra>')) We recommend viewing the plot interactively using plotly so that you can zoom in and find out information about each area by hovering the mouse over them:
nowcast.plotFor ease of viewing on GitHub we include a static version here:
