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05_figures.R
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###############################################################################
# Produce figures for manuscript and supporting information.
###############################################################################
library('data.table')
library('survey')
library('dplyr')
library('tidyr')
library('fields')
library('tidycensus')
library('doParallel')
library('colorspace')
library('RColorBrewer')
library('ggplot2')
library('ggpubr')
library('corrplot')
library('Hmisc')
source('functions.R')
###############################################################################
# Script.
###############################################################################
# Data.
states_w = fread('../data/supporting_data.csv')
# Keep a subset which is only rows with seroprevalence.
s = states_w[!is.na(seroprevalence), ]
# Reference model (no adjustment for waning).
m_ref_glm = readRDS('../output/main_glm.rds')
m_ref_glm_splines = readRDS('../output/main_glm_splines.rds')
# Models with cases adjusted for 3 different waning rates.
# This assumes that `seroprevalence_glm_3wanings.R` has been run, and that its
# outputs have been combined into one big data.table.
out_3w_glm = readRDS('../output/glm_3-wanings.rds')
# Best model by metric.
out_3w_glm[which.min(out_3w_glm$aic), ]
out_3w_glm[which.min(out_3w_glm$rmse), ]
(b = out_3w_glm[which.min(out_3w_glm$rmse_loov), ])
# We'll be using the best model below.
best = list(c('abbott' = b$waning_wks_abbott,
'ortho' = b$waning_wks_ortho,
'roche' = b$waning_wks_roche,
'lag' = b$detects))
###############################################################################
# Fig 1: Data
###############################################################################
s_sub = subset(s, survey_round == 24)
ord = order(s_sub$cases_cumulative_perc, decreasing = TRUE)
sero = s_sub$seroprevalence[ord] * 100
case = s_sub$cases_cumulative_perc[ord]
death = s_sub$deaths_cumulative_perc[ord]
vacc = s_sub$vaccinated_cumulative_perc[ord]
w_abb = which(s_sub$Abbott_Architect_p[ord] > 99)
pdf('../plots/figure1.pdf', width = 8.7/2.54, height = 11/2.54, pointsize = 6)
par(mar = c(2.5, 1.8, 1.5, 0.8), mgp = c(1.3, 0.3, 0), tcl = 0.20, las = 1)
split.screen(c(1, 4))
par(oma = c(0, 0, 0, 0.9))
screen(1)
plot(case, seq_along(ord), type = 'o', pch = 16, yaxt = 'n', ylim = c(1.7, 48.3),
xlab = '% cumul. cases', ylab = '')
abline(h = w_abb, col = adjustcolor('black', alpha = 0.07), lwd = 7)
axis(side = 2, at = seq_along(ord), labels = s_sub$state[ord])
axis(side = 4, at = seq_along(ord), labels = FALSE)
axis(side = 3, labels = FALSE)
mtext(expression(bold('a')), side = 3, adj = -0.02, line = 0.1, cex = 1.4)
screen(2)
plot(sero, seq_along(ord), type = 'o', pch = 16, yaxt = 'n', ylim = c(1.7, 48.3),
xlab = 'Seroprevalence', ylab = '')
abline(h = w_abb, col = adjustcolor('black', alpha = 0.07), lwd = 7)
lines(sero, seq_along(ord), type = 'o', pch = 16)
mgp.axis(side = 2, at = seq_along(ord), labels = s_sub$state[ord], hadj = 0.5,
mgp = c(1.3, 1.3, 0))
axis(side = 4, at = seq_along(ord), labels = FALSE)
axis(side = 3, labels = FALSE)
mtext(expression(bold('b')), side = 3, adj = -0.02, line = 0.1, cex = 1.4)
screen(3)
plot(death,seq_along(ord), type = 'o', pch = 16, yaxt = 'n', ylim = c(1.7, 48.3),
xlab = '% cumul. deaths', ylab = '')
mgp.axis(side = 2, at = seq_along(ord), labels = s_sub$state[ord], hadj = 0.5,
mgp = c(1.3, 1.3, 0))
axis(side = 4, at = seq_along(ord), labels = FALSE)
axis(side = 3, labels = FALSE)
mtext(expression(bold('c')), side = 3, adj = -0.02, line = 0.1, cex = 1.4)
screen(4)
plot(vacc, seq_along(ord), type = 'o', pch = 16, yaxt = 'n', ylim = c(1.7, 48.3),
xlab = '% vaccinated', ylab = '')
mgp.axis(side = 2, at = seq_along(ord), labels = s_sub$state[ord], hadj = 0.5,
mgp = c(1.3, 1.3, 0))
axis(side = 4, at = seq_along(ord), labels = s_sub$state[ord])
axis(side = 3, labels = FALSE)
mtext(expression(bold('d')), side = 3, adj = -0.02, line = 0.1, cex = 1.4)
close.screen(all.screens = TRUE)
dev.off()
################################################################################
# Fig 2: Model performance when cases are adjusted using three different waning
# rates (one per assay).
################################################################################
b_aic = out_3w_glm[which.min(out_3w_glm$aic), ]
b_loov_rmse = out_3w_glm[which.min(out_3w_glm$rmse_loov), ]
diag_df = data.frame(x = c(0, 2, 2, 2, 48, 48, 54, 0, 2, 2, 2, 24, 24, 54),
y = c(0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0),
w = factor(c(rep('Slower waning', 7), rep('Faster waning', 7)),
levels = c('Slower waning', 'Faster waning')))
cols = brewer.pal(n = 3, name = 'Dark2')
p1 = ggplot(diag_df, aes(x = x, y = y, colour = w)) +
geom_line(alpha = 0.5, size = 0.8) +
labs(x = 'Weeks',
y = 'Prob. seropos.',
tag = 'a') +
scale_y_continuous(limits = c(-0.1, 1.1), breaks = c(0, 0.5, 1)) +
scale_colour_brewer(type = 'qual', palette = 'Dark2') +
theme_bw() +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 7),
legend.position = 'none',
plot.tag = element_text(face = 'bold', size = 10),
plot.margin = unit(c(-0.2, 0, 0.2, 0), 'cm'))
# Compare three time series: cumulative cases (no waning), and cumulative cases,
# adjusted for two different times to seroreversion, faster and slower.
# No waning.
fl = subset(states_w, state == 'FL')$cases_cumulative
fl_0 = subset(states_w, state == 'FL')$cases
# Assuming 24 weeks of time to seroreversion.
fl_24 = GetWanedCases(x = fl_0, detect = 0,
ab_dur = c(abbott = 24, ortho = 60, roche = 97),
props_abbott = rep(1, length(fl)),
props_ortho = rep(0, length(fl)),
props_roche = rep(0, length(fl)))
# Assuming 60 weeks of time to seroreversion.
fl_60 = GetWanedCases(x = fl_0, detect = 0,
ab_dur = c(abbott = 24, ortho = 60, roche = 97),
props_abbott = rep(0, length(fl)),
props_ortho = rep(1, length(fl)),
props_roche = rep(0, length(fl)))
df = data.frame(x = rep(seq_along(fl), 3),
fl = c(fl, diag(as.matrix(fl_60)), diag(as.matrix(fl_24))),
w = factor(c(rep('No waning', length(fl)),
rep('Slower waning', nrow(fl_24)),
rep('Faster waning', nrow(fl_24))),
levels = c('No waning', 'Slower waning', 'Faster waning')))
p2 = ggplot(df, aes(x = x, y = fl / 1e6, colour = w)) +
geom_line(size = 0.8, alpha = 0.5) +
labs(x = 'Week',
y = 'Cases (millions)',
colour = NULL,
tag = 'b') +
scale_colour_manual(values = c('black', cols[1:2])) +
theme_bw() +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 7),
legend.position = 'bottom',
legend.direction = 'vertical',
legend.spacing.x = unit(0, 'lines'),
legend.key.height = unit(0.3, 'cm'),
legend.justification = c(-0.4, 1),
legend.margin = margin(-23, 0, 0, 0),
legend.text = element_text(margin = margin(r = -1.2, unit = 'cm'), size = 7),
plot.tag = element_text(face = 'bold', size = 10),
plot.margin = unit(c(-0.2, 0, 0, 0), 'cm'))
w_b_aic = quantile(out_3w_glm$aic, prob = 0.05) - m_ref_glm$aic
w_b_loov = quantile(out_3w_glm$rmse_loov, prob = 0.05) - m_ref_glm$rmse_loov
p3 = ggplot(subset(out_3w_glm, detects < 0 &
waning_wks_ortho == 49),
aes(x = waning_wks_roche, y = waning_wks_abbott)) +
geom_tile(aes(fill = aic - m_ref_glm$aic)) +
geom_line(data = data.frame(x = c(7.5, 70.5), y = c(7.5, 70.5)),
aes(x = x, y = y), colour = 'white', linetype = '33') +
geom_contour(aes(z = aic - m_ref_glm$aic), breaks = w_b_aic,
col = 'black', alpha = 0.6) +
geom_point(data = b_aic, size = 1, colour = 'green',
aes(x = waning_wks_roche, y = waning_wks_abbott, fill = NULL)) +
geom_text(data = data.frame(x = 95, y = 65), aes(x = x, y = y),
label = expression(Delta*'AIC'),
hjust = 1, size = 3) +
coord_fixed() +
labs(x = NULL,
y = NULL,
fill = NULL,
tag = 'c') +
scale_fill_continuous_diverging(palette = 'Blue-Red 3',
breaks = c(-5e2, 0, 5e2, 1e3)) +
theme_bw() +
theme(legend.position = 'top',
legend.text = element_text(size = 7),
legend.key.width = unit(1, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-0, -10, -5, -10),
plot.tag = element_text(face = 'bold', size = 10),
axis.title = element_text(size = 9),
axis.text = element_text(size = 8))
p4 = ggplot(subset(out_3w_glm, detects < 0 &
waning_wks_ortho == 10),
aes(x = waning_wks_roche, y = waning_wks_abbott)) +
geom_tile(aes(fill = rmse_loov - m_ref_glm$rmse_loov)) +
geom_line(data = data.frame(x = c(7.5, 70.5), y = c(7.5, 70.5)),
aes(x = x, y = y), colour = 'white', linetype = '33') +
geom_contour(aes(z = rmse_loov - m_ref_glm$rmse_loov), breaks = w_b_loov,
col = 'black', alpha = 0.6) +
geom_point(data = b_loov_rmse, size = 1, colour = 'green',
aes(x = waning_wks_roche, y = waning_wks_abbott, fill = NULL)) +
geom_text(data = data.frame(x = 95, y = 65), aes(x = x, y = y),
label = expression(Delta*'LOO RMSE'),
hjust = 1, size = 3) +
coord_fixed() +
labs(x = NULL,
y = NULL,
fill = NULL,
tag = 'd') +
scale_fill_continuous_diverging(palette = 'Blue-Red 3',
breaks = c(-0.002, -0.001, 0, 0.001, 0.002, 0.003)) +
theme_bw() +
theme(legend.position = 'top',
legend.text = element_text(size = 7),
legend.key.width = unit(1, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-0, -10, -5, -10),
plot.tag = element_text(face = 'bold', size = 10),
axis.title = element_text(size = 9),
axis.text = element_text(size = 8))
p34 = ggarrange(p3, p4, nrow = 1)
p34 = annotate_figure(p34,
bottom = text_grob('Roche time to seroreversion (weeks)',
size = 9),
left = text_grob('Abbott time to seroreversion (weeks)',
rot = 90, size = 9))
p = ggarrange(ggarrange(NULL, p1, NULL, p2, ncol = 1,
heights = c(0.03, 0.32, -0.03, 0.5), align = 'v'),
NULL, p34, nrow = 1, widths = c(0.26, 0.03, 0.7))
ggsave('../plots/figure2.pdf',
width = 17.8, height = 18 / 3, unit = 'cm')
################################################################################
# Fig 3: Example time series of seroprevalence and predicted incidence.
################################################################################
preds_glm_all = GetPredictions(d = states_w, ex = best, f = 'f_glm_3w')
ex_states = c('ID', 'NE', 'NJ', 'NY', 'OK', 'LA', 'GA')
vacc_start = s[, .(start = max(week[vaccinated_cumulative_perc < 1e-2])), by = 'state']
vacc_start = subset(vacc_start, state %in% ex_states)
vacc_start$state = factor(vacc_start$state, levels = ex_states)
preds_glm_all_ex = subset(preds_glm_all, state %in% ex_states &
week >= as.Date('2020-08-01') &
week <= as.Date('2022-01-15'))
preds_glm_all_ex = preds_glm_all_ex[, c('state', 'week', 'survey_round',
'seroprevalence',
'pred_seroprev', 'pred_incidence',
'pred_seroprev_lo', 'pred_seroprev_hi',
'pred_incidence_lo', 'pred_incidence_hi')]
# Uncertainty across models (to account for selection of times to seroreversion
# and lead/lag times).
#
# This assumes that `seroprevalence_get_overall_uncertainty.R` has been run, and
# that its outputs have been combined into one big data.table.
preds_glm_cis = readRDS('../output/cis_glm_3-wanings.rds')
preds_glm_cis_us = subset(preds_glm_cis, state == 'US-wide')
preds_glm_us = preds_glm_all[, .(state = 'US-wide',
week = mean(week),
seroprevalence = weighted.mean(seroprevalence,
w = state_population),
pred_seroprev = weighted.mean(pred_seroprev,
w = state_population),
pred_incidence = weighted.mean(pred_incidence,
w = state_population)),
by = 'survey_round']
preds_glm_us = merge(preds_glm_us, preds_glm_cis_us, all.x = TRUE)
preds_glm_all_ex = rbind(preds_glm_all_ex, preds_glm_us)
preds_glm_all_ex$state = factor(preds_glm_all_ex$state,
levels = c(ex_states, 'US-wide'))
# Label each facet.
panel_labels = data.frame(state = c(levels(vacc_start$state), 'US-wide'))
panel_labels$state = factor(panel_labels$state,
levels = c(ex_states, 'US-wide'))
panel_labels$label = letters[1:nrow(panel_labels)]
panel_labels$week = as.Date('2020-07-30')
panel_labels$y = 0.79
p = ggplot(preds_glm_all_ex) +
geom_vline(data = vacc_start,
aes(xintercept = start), linetype = '11', col = 'black',
alpha = 0.2, size = 1) +
geom_ribbon(data = subset(preds_glm_all_ex, !is.na(pred_seroprev)),
aes(x = week, ymin = pred_seroprev_lo, ymax = pred_seroprev_hi),
alpha = 0.1) +
geom_ribbon(data = subset(preds_glm_all_ex, !is.na(pred_incidence_lo)),
aes(x = week, ymin = pred_incidence_lo, ymax = pred_incidence_hi),
alpha = 0.2, fill = 'royalblue2') +
geom_point(aes(x = week, y = seroprevalence / 100, colour = 'Seroprevalence surveys'),
size = 0.8, alpha = 0.6) +
geom_line(data = subset(preds_glm_all_ex, !is.na(pred_seroprev)),
aes(x = week, y = pred_seroprev, colour = 'Fitted seroprevalence'),
size = 0.5, alpha = 1) +
geom_line(data = subset(preds_glm_all_ex, !is.na(pred_incidence)),
aes(x = week, y = pred_incidence,
colour = 'Estimated proportion infected'),
size = 0.5, alpha = 0.6) +
geom_text(data = panel_labels, aes(x = week, y = y, label = label),
fontface = 'bold',
size = 3.5) +
scale_x_date(date_breaks = '6 months', date_labels = '%b %Y') +
facet_wrap(~ state, ncol = 4) +
labs(x = NULL,
y = 'Seroprevalence and proportions infected') +
scale_colour_manual('',
breaks = c('Seroprevalence surveys',
'Fitted seroprevalence',
'Estimated proportion infected'),
values = c('Seroprevalence surveys' = 'black',
'Fitted seroprevalence' = 'darkgray',
'Estimated proportion infected' = 'royalblue2')) +
guides(colour = guide_legend(override.aes = list(shape = c(16, NA, NA),
linetype = c(NA, 1, 1),
alpha = c(0.6, 0.6, 0.6)))) +
theme_bw() +
theme(strip.background = element_blank(),
legend.position = 'bottom',
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-5, -15, -5, -10),
strip.text = element_text(margin = margin(0.05, 0, 0.05, 0, 'cm')),
panel.spacing = unit(0.1, 'cm'),
plot.margin = margin(0, 0.34, 0.1, 0.1, 'cm'),
axis.text = element_text(size = 8),
axis.title = element_text(size = 9))
ggsave('../plots/figure3.pdf', width = 17.8, height = 7.0, unit = 'cm')
################################################################################
# Fig 4: Maps of datastreams and predicted incidence (with and without
# vaccination), negative correlations between incidence and vaccination, and
# example time series for two states.
################################################################################
#' Plot maps of various metrics, for a specific round.
#'
#' @param preds_all :data.table: augmented data.table with predicted values
#' (output from `GetPredictions` above).
#' @param r :numeric: survey round for which to plot the maps.
#' @param zl :numeric: the maximum value to use in the plots for seroprevalence
#' and incidence maps (all of which have common ranges in values).
#'
#' @return Four maps,
#' (i) anti-N seroprevalence (as per the surveys);
#' (ii) estimated cumulative infections;
#' (iii) vaccination coverage;
#' (iv) estimated proportion infected and/or vaccinated.
#'
#' Plot of Pearson correlations between infections and vaccinations, per round.
#'
#' Example time series of anti-N seroprevalence, estimated cumulative
#' infections, and vaccination coverage, for two contrasting states.
PlotIncidenceVacc = function(preds_all, r = 26, zl = 0.7)
{
preds = subset(preds_all, !is.na(seroprevalence))
map = left_join(state_laea, unique(fips_codes[, c('state', 'state_code')]),
by = c('GEOID' = 'state_code'))
map = left_join(map, subset(preds, survey_round %in% r),
by = 'state')
pars = sort(unique(map$par))
pl1 = ggplot(map, aes(fill = seroprevalence / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, zl)) +
labs(fill = 'CDC\nseroprev. ') +
theme_void() +
theme(legend.position = 'top',
legend.title = element_text(size = 8),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, 'cm'),
legend.key.height = unit(0.3, 'cm'),
plot.title = element_text(hjust = 0.5))
pl2 = ggplot(map, aes(fill = pred_incidence)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, zl)) +
labs(fill = 'Estimated\nproportion \ninfected') +
theme_void() +
theme(legend.position = 'top',
legend.title = element_text(size = 8),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, 'cm'),
legend.key.height = unit(0.3, 'cm'),
plot.title = element_text(hjust = 0.5))
pl3 = ggplot(map, aes(fill = vaccinated_cumulative_perc / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Blues', rev = TRUE,
lim = c(0, zl)) +
labs(fill = 'Vaccinated ') +
theme_void() +
theme(legend.position = 'top',
legend.title = element_text(size = 8),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, 'cm'),
legend.key.height = unit(0.3, 'cm'),
plot.title = element_text(hjust = 0.5))
pl4 = ggplot(map, aes(fill = pred_epiv)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Blue-Yellow', rev = TRUE,
lim = c(0, zl)) +
labs(fill = 'EPIV ') +
theme_void() +
theme(legend.position = 'top',
legend.title = element_text(size = 8),
legend.text = element_text(size = 7),
legend.key.width = unit(0.5, 'cm'),
legend.key.height = unit(0.3, 'cm'),
plot.title = element_text(hjust = 0.5))
cors_i_v = preds[, .(week = mean(week),
r = cor.test(vaccinated_cumulative_perc,
pred_incidence)$estimate,
r_raw = cor.test(vaccinated_cumulative_perc,
seroprevalence)$estimate,
ci_lo = cor.test(vaccinated_cumulative_perc,
pred_incidence)$conf.int[1],
ci_hi = cor.test(vaccinated_cumulative_perc,
pred_incidence)$conf.int[2]),
by = 'survey_round']
cors_i_v = na.omit(cors_i_v)
pl6 = ggplot(cors_i_v, aes(x = week, y = r)) + geom_point(size = 1) + geom_line() +
geom_ribbon(aes(ymin = ci_lo, ymax = ci_hi), alpha = 0.1) +
geom_hline(yintercept = 0, linetype = '22', size = 0.3) +
scale_x_date(date_breaks = '3 months', date_labels = '%b %Y') +
labs(x = NULL, y = 'Pearson corr.') +
theme_bw() +
theme(axis.text = element_text(size = 7),
axis.title = element_text(size = 8))
w = max(preds$week[preds$survey_round == r])
pl7 = ggplot(subset(preds, state %in% c('WA', 'AL')),
aes(x = week, y = seroprevalence / 100)) +
geom_ribbon(aes(ymin = pred_incidence_lo, ymax = pred_incidence_hi),
alpha = 0.2, fill = 'royalblue2') +
geom_point(size = 0.5) +
geom_vline(xintercept = w, linetype = '22', alpha = 0.3) +
geom_line(aes(y = vaccinated_cumulative_perc / 100,
colour = 'Proportion vaccinated')) +
geom_line(data = subset(preds_all, state %in% c('WA', 'AL') &
week >= as.Date('2020-08-01') &
week <= as.Date('2022-01-15')),
aes(x = week, y = pred_incidence,
colour = 'Estimated proportion infected')) +
scale_x_date(date_breaks = '6 months', date_labels = '%b %Y') +
facet_wrap(~ state, nrow = 2, ncol = 1) +
theme_bw() +
labs(x = NULL, y = 'Seroprev., vacc.') +
scale_colour_manual('',
breaks = c('Estimated proportion infected',
'Proportion vaccinated'),
values = c('Estimated proportion infected' = 'royalblue2',
'Proportion vaccinated' = 'red')) +
theme(legend.position = 'none',
legend.spacing.y = unit(-0.2, 'cm'),
legend.key = element_blank(),
legend.background = element_blank(),
legend.key.width = unit(1, 'cm'),
strip.background = element_blank(),
strip.text = element_text(size = 7, margin = margin(0.15, 0, 0.05, 0, 'cm')),
panel.spacing = unit(0, 'cm'),
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 7),
axis.title = element_text(size = 8),
plot.margin = margin(0, 0.3, 0, 0, 'cm'))
pl = ggarrange(ggarrange(pl1, pl2, nrow = 1, ncol = 2,
font.label = list(size = 8, face = 'bold'),
common.legend = FALSE, labels = c('A', 'B')),
ggarrange(pl3, pl4, ncol = 2,
font.label = list(size = 8, face = 'bold'),
common.legend = FALSE, labels = c('C', 'D')),
ggarrange(pl6, pl7, ncol = 2,
font.label = list(size = 8, face = 'bold'),
common.legend = FALSE, widths = c(0.55, 0.45),
labels = c('E', 'F')),
nrow = 3, heights = c(0.35, 0.35, 0.3))
}
pl_glm = PlotIncidenceVacc(preds_all = preds_glm_all, r = 24, zl = 0.72)
ggsave('../plots/figure4.pdf', width = 11.4, height = 11.4, unit = 'cm')
###############################################################################
# Fig 5: Compare predicted incidences with blood donors dataset.
###############################################################################
don = fread('../data/blood_donors_surveys_processed.csv')
don$week = as.Date(don$week)
# Merge blood donors serosurvey with our own dataset. Need to do this manually
# to be able to have some flexibility in what dates are matched.
preds_glm_all = GetPredictions(d = states_w, ex = best, f = 'f_glm_3w')
preds_glm = subset(preds_glm_all, !is.na(seroprevalence))
preds_glm$blood_prevalence = as.numeric(NA)
preds_glm$blood_prevalence_no_vacc = as.numeric(NA)
for (i in seq_len(nrow(preds_glm)))
{
w_st = which(don$state == preds_glm$state[i])
w_wk = which(don$week[w_st] == preds_glm$week[i])
if (length(w_wk) > 0)
{
preds_glm$blood_prevalence[i] = don$blood_prevalence[w_st][w_wk]
preds_glm$blood_prevalence_no_vacc[i] = don$blood_prevalence_no_vacc[w_st][w_wk]
} else
{
# If dates for rounds do not match, merge rounds that are within a week of
# each other.
d = abs(don$week[w_st] - as.Date(preds_glm$week[i]))
if (any(d <= 7))
{
w_min = which(d == min(d))
if (length(w_min) > 1)
{
preds_glm$blood_prevalence[i] = mean(don$blood_prevalence[w_st][w_min])
preds_glm$blood_prevalence_no_vacc[i] = mean(don$blood_prevalence_no_vacc[w_st][w_min])
} else
{
preds_glm$blood_prevalence[i] = don$blood_prevalence[w_st][w_min]
preds_glm$blood_prevalence_no_vacc[i] = don$blood_prevalence_no_vacc[w_st][w_min]
}
}
}
}
# Get LOESS predictions to add nonlinear trends to plots.
plot_preds = subset(preds_glm, !is.na(blood_prevalence) & !is.na(pred_epiv))
l = loess(pred_epiv ~ blood_prevalence, data = plot_preds)
plot_preds$pred_epiv_l = predict(l)
l = loess(pred_epiv_d1 ~ blood_prevalence, data = plot_preds)
plot_preds$pred_epiv_d1_l = predict(l)
l = loess(pred_epiv_d1_neg ~ blood_prevalence, data = plot_preds)
plot_preds$pred_epiv_d1_neg_l = predict(l)
b = brewer.pal(3, 'Set1')
# Top-left panel:
# - No correlation between vaccination and incidence.
# - Only include individuals with a complete series of vaccination.
plot_preds$lab = 'Ind. prob., complete series'
p1 = ggplot(plot_preds, aes(x = blood_prevalence, y = pred_epiv)) +
geom_abline(intercept = 0, slope = 1, lty = '33') +
geom_linerange(aes(ymin = pred_epiv_lo,
ymax = pred_epiv_hi),
colour = b[2], size = 0.2, alpha = 0.3) +
geom_point(pch = 16, size = 0.6, alpha = 0.3, colour = b[2]) +
geom_line(aes(x = blood_prevalence, y = pred_epiv_l),
colour = b[2], size = 1) +
coord_fixed(clip = 'off') +
lims(x = c(0, 1), y = c(0, 1)) +
facet_wrap(~ lab) +
labs(x = NULL,
y = NULL,
tag = 'a') +
theme_bw() +
theme(strip.background = element_blank(),
axis.text = element_text(size = 8),
plot.tag = element_text(face = 'bold', size = 10),
plot.tag.position = c(0.03, 0.94))
# Top-right panel:
# - No correlation between vaccination and incidence.
# - Include individuals with at least one dose of a vaccine.
plot_preds$lab = 'Independent prob., dose 1'
p2 = ggplot(plot_preds, aes(x = blood_prevalence, y = pred_epiv_d1)) +
geom_abline(intercept = 0, slope = 1, lty = '33') +
geom_linerange(aes(ymin = pred_epiv_d1_lo,
ymax = pred_epiv_d1_hi),
colour = b[1], size = 0.2, alpha = 0.3) +
geom_point(pch = 16, size = 0.8, alpha = 0.3, colour = b[1]) +
geom_line(aes(x = blood_prevalence, y = pred_epiv_d1_l),
colour = b[1], size = 1) +
coord_fixed(clip = 'off') +
lims(x = c(0, 1), y = c(0, 1)) +
facet_wrap(~ lab) +
labs(x = NULL,
y = NULL,
tag = 'b') +
theme_bw() +
theme(strip.background = element_blank(),
axis.text = element_text(size = 8),
plot.tag = element_text(face = 'bold', size = 10),
plot.tag.position = c(0.03, 0.94))
# Bottom-left panel:
# - Negative correlation between vaccination and incidence.
# - Only include individuals with a complete series of vaccination.
plot_preds$lab = 'Negative correlation, dose 1'
p3 = ggplot(plot_preds, aes(x = blood_prevalence, y = pred_epiv_d1_neg)) +
geom_abline(intercept = 0, slope = 1, lty = '33') +
geom_linerange(aes(ymin = pmin(pred_epiv_d1_neg_lo),
ymax = pmin(pred_epiv_d1_neg_hi)),
colour = b[3], size = 0.2, alpha = 0.3) +
geom_point(pch = 16, size = 0.8, alpha = 0.3, colour = b[3]) +
geom_line(aes(x = blood_prevalence, y = pred_epiv_d1_neg_l),
colour = b[3], size = 1) +
coord_fixed(clip = 'off') +
lims(x = c(0, 1), y = c(0, 1)) +
facet_wrap(~ lab) +
labs(x = NULL,
y = NULL,
tag = 'c') +
theme_bw() +
theme(strip.background = element_blank(),
axis.text = element_text(size = 8),
plot.tag = element_text(face = 'bold', size = 10),
plot.tag.position = c(0.03, 0.94))
# Bottom-right panel:
# Compare LOESS curves for the other three panels.
plot_preds$lab = 'Comparison'
p4 = ggplot(plot_preds, aes(x = blood_prevalence, y = pred_epiv_l)) +
geom_abline(intercept = 0, slope = 1, lty = '33') +
geom_line(colour = b[2], size = 1, show.legend = TRUE) +
geom_line(aes(x = blood_prevalence, y = pred_epiv_d1_l),
colour = b[1], size = 1) +
geom_line(aes(x = blood_prevalence, y = pred_epiv_d1_neg_l),
colour = b[3], size = 1) +
coord_fixed(clip = 'off') +
lims(x = c(0, 1), y = c(0, 1)) +
facet_wrap(~ lab) +
labs(x = NULL,
y = NULL,
tag = 'd') +
theme_bw() +
theme(strip.background = element_blank(),
axis.text = element_text(size = 8),
plot.tag = element_text(face = 'bold', size = 10),
plot.tag.position = c(0.03, 0.94))
pl = ggarrange(p1, p2, p3, p4, nrow = 2, ncol = 2,
font.label = list(face = 'plain'),
common.legend = FALSE)
pl = annotate_figure(pl,
bottom = text_grob('Anti-S seroprevalence from the blood donors dataset',
size = 9),
left = text_grob('Estimated proportion infected and/or vaccinated',
rot = 90, size = 9))
ggsave('../plots/figure5.pdf', width = 12.5, height = 12.5, units = 'cm')
################################################################################
# Supplementary information plots
################################################################################
###############################################################################
# Fig S1: Data
###############################################################################
s_ex = subset(s, survey_round %in% c(4, 13, 24))
s_ex = rbind(s_ex, s_ex[(nrow(s_ex) + 1):(nrow(s_ex) + 3), ])
s_ex$state[(nrow(s_ex) - 2):nrow(s_ex)] = 'ND'
s_ex$survey_round[(nrow(s_ex) - 2):nrow(s_ex)] = c(4, 13, 24)
s_ex = s_ex[, `:=`(assay_cat = ifelse(Abbott_Architect_perc == 100, 'Abbott',
ifelse(Roche_Elecsys_perc == 100, 'Roche',
ifelse(Ortho_VITROS_perc == 100,
'Ortho', 'Abbott & Ortho'))))]
map = left_join(state_laea, unique(fips_codes[, c('state', 'state_code')]),
by = c('GEOID' = 'state_code'))
map = left_join(map, s_ex, by = 'state')
map$round = paste('Round', map$survey_round)
map$round = factor(map$survey_round, levels = c('Round 4', 'Round 13', 'Round 24'))
map = subset(map, state != 'DC')
cols = qualitative_hcl(4, palette = 'Dark')
pl1 = ggplot(map, aes(fill = assay_cat)) +
geom_sf(colour = 'black', size = 0.01) +
scale_fill_manual(values = cols, na.value = 'gray50',
limits = c('Abbott', 'Abbott & Ortho', 'Ortho', 'Roche')) +
facet_wrap(~ survey_round, nrow = 1) +
labs(fill = '') +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.8, 'cm'),
plot.title = element_text(hjust = 0.5))
pl2 = ggplot(map, aes(fill = seroprevalence / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE) +
facet_wrap(~ survey_round, nrow = 1) +
labs(fill = 'CDC seroprevalence') +
theme_void() +
theme(legend.position = 'top',
legend.title = element_text(size = 9))
s_sum = s[, .(week = mean(week),
Abbott = mean(Abbott_Architect_perc),
Ortho = mean(Ortho_VITROS_perc),
Roche = mean(Roche_Elecsys_perc)),
by = 'survey_round']
s_sum = melt(s_sum, id.vars = c('week', 'survey_round'), variable.name = 'Assay')
pl3 = ggplot(s_sum, aes(x = week, y = value, colour = Assay)) +
geom_line(show.legend = FALSE) + geom_point(size = 0.6, show.legend = FALSE) +
scale_colour_manual(values = cols[-2]) +
geom_vline(data = subset(s_sum, survey_round %in% c(4, 13, 24)),
aes(xintercept = week), alpha = 0.05, size = 3) +
scale_x_date(date_breaks = '6 months', date_labels = '%b %Y') +
labs(x = NULL, y = 'Mean % use of assay') +
theme_bw() +
theme(plot.margin = unit(c(0, 0.3, 0, 0), 'cm'))
s_glm = GetPredsPerAssay(d = states_w, ex = best[[1]], f = 'f_glm')
s_all_glm = GetPredTS(dat = s_glm)
pl4 = ggplot(subset(s_all_glm, assay_lab == 'Surveys'),
aes(x = week, y = values)) +
geom_line() +
geom_point(size = 0.7) +
geom_vline(data = subset(s_all_glm, survey_round %in% c(4, 13, 24)),
aes(xintercept = week), alpha = 0.05, size = 3) +
scale_x_date(date_breaks = '6 months', date_labels = '%b %Y') +
xlab(NULL) + ylab('Seroprevalence') +
theme_bw() +
theme(plot.margin = unit(c(0, 0.3, 0, 0), 'cm'))
pl = ggarrange(pl1, pl2, ggarrange(pl3, pl4, nrow = 1), ncol = 1, heights = c(1, 1.12, 0.8))
ggsave('../plots/figures1.pdf', width = 6, height = 5, unit = 'in')
###############################################################################
# SI Fig S2: %assay vs. seroprevalence.
###############################################################################
#' Jitter points on plot.
Jit = function(x)
{
w_x = which(x == 100 | x == 0)
x[w_x] = x[w_x] + runif(length(w_x), -2, 2)
return(x)
}
pdf(file = '../plots/figures2.pdf', width = 5, height = 1.5, pointsize = 8)
par(mar = c(2.2, 1.6, 0.5, 0.5), mgp = c(1.2, 0.3, 0), tcl = 0.15, las = 1)
split.screen(c(1, 3))
par(oma = c(0, 1.4, 0, 0))
screen(1)
plot(Jit(s$Abbott_Architect_perc), s$seroprevalence / 100, pch = 16,
cex = 0.6, col = adjustcolor('black', alpha = 0.2),
xlab = '% Abbott', ylab = '',
xlim = c(-3, 103))
mtext('(a)', side = 3, adj = 0.90, line = -1.2)
screen(2)
plot(Jit(s$Ortho_VITROS_perc), s$seroprevalence / 100, pch = 16,
cex = 0.6, col = adjustcolor('black', alpha = 0.2),
xlab = '% Ortho', ylab = '',
xlim = c(-3, 103))
mtext('(b)', side = 3, adj = 0.90, line = -1.2)
screen(3)
plot(Jit(s$Roche_Elecsys_perc), s$seroprevalence / 100, pch = 16,
cex = 0.6, col = adjustcolor('black', alpha = 0.2),
xlab = '% Roche', ylab = '',
xlim = c(-3, 103))
mtext('(c)', side = 3, adj = 0.90, line = -1.2)
close.screen(all.screens = TRUE)
par(las = 0)
mtext(text = 'Seroprevalence', side = 2, outer = TRUE, line = -1)
dev.off()
###############################################################################
# SI Fig S3: Map of assays used.
###############################################################################
round_max = 25
s_ex = subset(s, survey_round < (round_max + 1))
s_ex = s_ex[, `:=`(assay_cat = ifelse(Abbott_Architect_perc == 100, 'Abbott',
ifelse(Roche_Elecsys_perc == 100, 'Roche',
ifelse(Ortho_VITROS_perc == 100,
'Ortho', 'Abbott & Ortho'))))]
s_ex = subset(s_ex, survey_round <= 15 | survey_round == 24)
s_ex$survey_round[which(s_ex$survey_round == 15)] = '15-23'
map = left_join(state_laea, unique(fips_codes[, c('state', 'state_code')]),
by = c('GEOID' = 'state_code'))
map = left_join(map, s_ex, by = 'state')
map = subset(map, !state %in% c('DC', 'ND'))
map$survey_round = factor(map$survey_round, levels = unique(map$survey_round))
cols = qualitative_hcl(4, palette = 'Dark')
pl = ggplot(map, aes(fill = assay_cat)) +
geom_sf(colour = 'black', size = 0.1) +
scale_fill_manual(values = cols, na.value = 'gray50',
limits = c('Abbott', 'Abbott & Ortho', 'Ortho', 'Roche')) +
facet_wrap(~ survey_round, ncol = 4) +
labs(fill = 'Assay used') +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.8, 'cm'),
plot.title = element_text(hjust = 0.5))
ggsave('../plots/figures3.pdf', width = 8.5, height = 6.5)
###############################################################################
# SI Figs S4-S6: Comparing seroprevalence, incidence, vaccination for all
# rounds.
###############################################################################
map = left_join(state_laea, unique(fips_codes[, c('state', 'state_code')]),
by = c('GEOID' = 'state_code'))
map = left_join(map, subset(preds_glm, survey_round < 11), by = 'state')
map = map[!map$state %in% c('ND', 'DC'), ]
pl1 = ggplot(map, aes(fill = seroprevalence / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, 0.7)) +
labs(fill = 'Observed\nseroprev.') +
facet_wrap(~ survey_round, ncol = 1) +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_blank())
pl2 = ggplot(map, aes(fill = pred_incidence)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, 0.7)) +
labs(fill = 'Estimated\nproportion\ninfected') +
facet_wrap(~ survey_round, ncol = 1) +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_blank())
pl3 = ggplot(map, aes(fill = pred_incidence - seroprevalence / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_diverging(palette = 'Blue-Red 2', rev = TRUE) +
labs(fill = 'Infections -\nseroprev.') +
facet_wrap(~ survey_round, ncol = 1) +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_blank())
pl4 = ggplot(map, aes(fill = vaccinated_cumulative_perc / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Blues', rev = TRUE, lim = c(0, 0.79)) +
labs(fill = 'Proportion\nvaccinated') +
facet_wrap(~ survey_round, ncol = 1, strip.position = 'right') +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_text(size = 15))
pl = ggarrange(pl1, pl2, pl3, pl4, ncol = 4,
font.label = list(face = 'plain'),
common.legend = FALSE)
ggsave('../plots/figures4.png', width = 10, height = 14, unit = 'in')
map = left_join(state_laea, unique(fips_codes[, c('state', 'state_code')]),
by = c('GEOID' = 'state_code'))
map = left_join(map, subset(preds_glm, survey_round > 10 & survey_round < 21),
by = 'state')
map = map[!map$state %in% c('ND', 'DC'), ]
pl1 = ggplot(map, aes(fill = seroprevalence / 100)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, 0.7)) +
labs(fill = 'Observed\nseroprev.') +
facet_wrap(~ survey_round, ncol = 1) +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_blank())
pl2 = ggplot(map, aes(fill = pred_incidence)) +
geom_sf(colour = NA) +
scale_fill_continuous_sequential(palette = 'Greens', rev = TRUE,
lim = c(0, 0.7)) +
labs(fill = 'Estimated\nproportion\ninfected') +
facet_wrap(~ survey_round, ncol = 1) +
theme_void() +
theme(legend.position = 'top',
legend.key.width = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5),
strip.text = element_blank())