library(readxl)
library(tidyverse)
library(gtsummary)
library(patchwork)
library(lubridate)
library(stringr)
library(mgcv)
library(janitor)
library(predtools)
library(magrittr)
library(slider)
library(scam)
library(epitools)
library(sf)
library(stringi)
invisible(Sys.setlocale("LC_TIME", "English"))## data import
## case notification
df1 <- read_excel("D:/OUCRU/hfmd/data/TCM_full.xlsx",
col_types = c("date", "numeric", "text",
"text", "text", "date", "date", "date",
"text", "text", "text"))
colnames(df1) <- c("dob", "age", "gender", "commune", "district",
"reported_date", "onset_date","adm_date",
"medi_cen","inout","severity")
df1$dob <- df1$dob %>% as_date()
df1$adm_date <- df1$adm_date %>% as_date()
df1$age1 <- interval(df1$dob, df1$adm_date) / years(1)
df1$adm_week <- as.Date(floor_date(df1$adm_date, "week"))
df1$district <- df1$district %>% str_replace_all(
c( "Quận Gò vấp" = "Quận Gò Vấp"))
df1$district <- df1$district %>%
str_remove("Quận|Huyện|Thành phố") %>%
trimws(which = "both")
## serological data
apr_2023 <- read_excel("D:/OUCRU/hfmd/data/4_2023.xlsx")
aug_2023 <- read_excel("D:/OUCRU/hfmd/data/08_2023.xlsx")
dec_2022 <- read_excel("D:/OUCRU/hfmd/data/12_2022.xls")
dec_2023 <- read_excel("D:/OUCRU/hfmd/data/12_2023.xlsx")
t423 <- data.frame(apr_2023[-c(1,2),c(6,8,10:14)])
t423$pos <- replace(t423$...14,is.na(t423$...14),0) %>%
str_detect(regex(paste(2^(4:10), collapse = "|"))) %>%
as.integer(as.logical())
colnames(t423) <- c("id","age_gr","age","col_day","col_month","col_year","neutralization","pos")
t423$age <- as.numeric(t423$age)
t423$col_time <- rep("Apr 2023",nrow(t423))
t823 <- data.frame(aug_2023[-c(1,2),c(6,8,9,14:17)])
t823$pos <- str_detect(t823$...17,regex(paste(2^(4:10), collapse = "|"))) %>%
as.integer(as.logical())
colnames(t823) <- c("id","age_gr","age","col_day","col_month","col_year","neutralization","pos")
t823$age <- as.numeric(t823$age)
t823$col_time <- rep("Aug 2023",nrow(t823))
t1222 <- data.frame(dec_2022[-c(1,2),c(6,8,10:14)])
t1222$pos <- replace(t1222$...14,is.na(t1222$...14),0) %>%
str_detect(regex(paste(2^(4:10), collapse = "|"))) %>%
as.integer(as.logical())
colnames(t1222) <- c("id","age_gr","age","col_day","col_month","col_year","neutralization","pos")
t1222$age <- as.numeric(t1222$age)
t1222$col_time <- rep("Dec 2022",nrow(t1222))
t1223 <- data.frame(dec_2023[-c(1,2),c(6,8,9,14:17)])
t1223$pos <- replace(t1223$...17,is.na(t1223$...17),0) %>%
str_detect(regex(paste(2^(4:10), collapse = "|"))) %>%
as.integer(as.logical())
colnames(t1223) <- c("id","age_gr","age","col_day","col_month","col_year","neutralization","pos")
t1223$age <- as.numeric(t1223$age)
t1223$col_time <- rep("Dec 2023",nrow(t1223))
## data sero with address
cleaned <- read_csv("D:/OUCRU/HCDC/project phân tích sero quận huyện/cleaned.csv")
sero <- rbind(t1222,t1223,t423,t823)
sero_add <- full_join(cleaned,sero, by = c("id" = "id"))
data_pt <- sero_add %>% filter(!is.na(age)&!is.na(qhchuan)) %>%
dplyr::select(-c(add_mod,pxchuan,neutralization)) %>%
as.data.frame() %>%
mutate(col_date = make_date(col_year,col_month,col_day))
## census data
census2019 <- readRDS("D:/OUCRU/hfmd/data/census2019.rds")
### HCMC map
map_path <- "D:/OUCRU/HCDC/project phân tích sero quận huyện/"
vn_qh <- st_read(dsn = file.path(map_path, "gadm41_VNM.gpkg"), layer = "ADM_ADM_2")
vn_qh1 <- vn_qh %>%
clean_names() %>% ## cho thành chữ thường
filter(
str_detect(
name_1,
"Hồ Chí Minh"
)
)
qhtp <- vn_qh1[-c(14,21),]
qhtp$geom[qhtp$varname_2 == "Thu Duc"] <- vn_qh1[c("21","24","14"),] %>%
st_union()
qhtp <- qhtp %>% st_cast("MULTIPOLYGON")
qhtp$varname_2 <- stri_trans_general(qhtp$varname_2, "latin-ascii") %>%
tolower() %>%
str_remove("district") %>%
trimws(which = "both")
qhtp$nl_name_2 <- c("BC","BTân","BT","CG","CC","GV",
"HM","NB","PN","1","10","11","12",
"3","4","5","6","7","8","TB",
"TP","TĐ")# function
slide_age <- function(time1,age1,w1,s1){
df1 <- data.frame(time1,age1) %>% ## line listing data frame
filter(!is.na(time1) & !is.na(age1)) %>%
arrange(time1)
a_df1 <- df1 %>% count(time1) ## aggregate data frame
total1 <- nrow(a_df1)
spots1 <- seq(from = 1, to = (total1 - w1 + 1), by = s1)
out_total <- data.frame()
for (i in 1:length(spots1)){
range1 <- data.frame(a_df1[spots1[i]:(spots1[i] + w1 - 1),1])
result1 <- df1$age1[df1$time1 >= range1[1,] & df1$time1 <= range1[w1,]]
out <- data.frame(date = rep(as.character(range1[ceiling(w1/2),]),length(result1)),
age = result1)
out <- out[order(out$age),]
out_total <- rbind(out_total,out)
}
output <- list()
output$wdat <- out_total
output$adat <- out_total %>% count(date)
output$parms <- data.frame(w = w1,s =s1)
return(output)
}
# case_noti <- function(timee, agee,lab = FALSE){
#
# dat <- data.frame(timee,agee) %>%
# filter(!is.na(timee) & !is.na(agee)) %>%
# count(timee)
#
# ts <- ggplot(dat, aes(x =timee, y = n)) +
# geom_bar(stat = "identity") +
# labs(y = "Cases") +
# theme_minimal()
#
# if(lab == TRUE){
# ts
# } else {
# ts + theme(axis.title.x = element_blank(),
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank())
# }
#
# }library(paletteer)
df23 <- df1 %>% filter(year(adm_week) == 2023)
co <- data.frame()
for (i in 0:6){
gen <- seq(0,1,le=52) + i
co <- rbind(co,gen)
}
df23 <- df1 %>% filter(year(adm_date) == 2023)
wwww <- slide_age(time1 = df23$adm_date,
age1 = df23$age1,
w1 = 7, s1=7)
library(zoo)
ch <- data.frame(date = wwww$adat$date,
c0 = as.numeric(co[1,]),
c1 = as.numeric(co[2,]),
c2 = as.numeric(co[3,]),
c3 = as.numeric(co[4,]),
c4 = as.numeric(co[5,]),
c5 = as.numeric(co[6,]))
ch <- ch %>% mutate(
trend = c(rep("#80FFFFFF",26-3),
rep("#FFFFFFFF",26+3))
)
ch$group <- consecutive_id(ch$trend)
ch <- head(do.call(rbind, by(ch, ch$group, rbind, NA)), -1)
ch[, c("trend", "group")] <- lapply(ch[, c("trend", "group")], na.locf)
ch[] <- lapply(ch, na.locf, fromLast = T)
ch$trend <- factor(ch$trend,
levels = c("#80FFFFFF","#FFFFFFFF"))
leb_month <- c("Jan",rep("",3),"Feb",rep("",3),"Mar",rep("",4),"Apr",rep("",3),
"May",rep("",4),"Jun",rep("",3),"Jul",rep("",3),"Aug",rep("",4),
"Sep",rep("",3),"Oct",rep("",3),"Nov",rep("",4),"Dec",rep("",3))
ts <- data.frame(wwww$wdat$date,wwww$wdat$age) %>%
filter(!is.na(wwww$wdat$date) & !is.na(wwww$wdat$age)) %>%
count(wwww$wdat$date) %>%
set_colnames(c("time","n")) %>%
mutate(peak = c(rep("1st",36),rep("2nd",nrow(.)-36))) %>%
ggplot(aes(x = time, y = n,fill = peak)) +
geom_col(alpha = 0.6,color="black") +
geom_vline(xintercept = 36.5) +
theme_minimal()+
scale_y_continuous(name = "Cases",
breaks = seq(0,3000,by = 1000),
limit =c(0,3000),
minor_breaks = NULL)+
scale_fill_manual(values = c("#582C83FF","#FFC72CFF"))+
labs(tag = "C")+
annotate("rect", fill = "grey",
xmin = c("2023-01-04","2023-04-05","2023-08-02","2023-12-06"),
xmax = c("2023-01-11","2023-04-26","2023-08-30","2023-12-27"),
ymin = 0, ymax = Inf, alpha = .5)+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none",
plot.tag = element_text(face = "bold", size = 18),
axis.title.y = element_text(size = 18),
axis.text.y = element_text(size = 18))
hm <- ggplot(data=wwww$wdat, aes(x=date, y=age)) +
stat_density(
aes(fill = after_stat(density)),
geom = "raster",
position = "identity",
interpolate = TRUE
)+
scale_fill_paletteer_c("grDevices::Inferno")+
# scale_fill_gradient(low="#040404FF", high= "#FFFE9EFF")+
# scale_fill_distiller(palette = "Blues")+
theme_minimal()+
scale_y_reverse(name = "Age (years)",lim= rev(c(0,6)),breaks = seq(0,6))+
scale_x_discrete(name = "Admission week",labels = leb_month)+
labs(tag = "D",fill = "Density")+
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1,size = 8))+
geom_line(data = ch,aes(x = date,y = c0,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c1,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c2,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c3,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c4,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c5,col = trend),
group = 1,lwd = 0.25)+
theme(axis.title.y = element_text(size = 18),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "top",
plot.tag = element_text(face = "bold", size = 18),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))+
guides(fill=guide_colourbar(barwidth=20,
label.position="top"),
color = "none")
hm2 <- ggplot(data=wwww$wdat, aes(x=date, y=age)) +
stat_density(
aes(fill = after_stat(count)),
geom = "raster",
position = "identity",
interpolate = TRUE
)+
scale_fill_paletteer_c("grDevices::Inferno")+
# scale_fill_gradient(low="#040404FF", high= "#FFFE9EFF")+
# scale_fill_distiller(palette = "Blues")+
theme_minimal()+
scale_y_reverse(name = "Age (years)",lim= rev(c(0,6)),breaks = seq(0,6))+
scale_x_discrete(name = "Admission week",labels = leb_month)+
labs(tag = "D",fill = "Number of hospitalizations")+
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1,size = 8))+
geom_line(data = ch,aes(x = date,y = c0,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c1,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c2,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c3,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c4,col = trend),
group = 1,lwd = 0.25)+
geom_line(data = ch,aes(x = date,y = c5,col = trend),
group = 1,lwd = 0.25)+
theme(axis.title.y = element_text(size = 18),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "top",
plot.tag = element_text(face = "bold", size = 18),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))+
guides(fill=guide_colourbar(barwidth=20,
label.position="top"),
color = "none")
fi_peak <- df1 %>% filter(year(adm_date) == "2023") %>%
filter((adm_date <= as.Date("2023-09-03")&
!is.na(adm_date) & !is.na(age1)))
se_peak <- df1 %>% filter(year(adm_date) == "2023") %>%
filter((adm_date > as.Date("2023-09-03")) &
!is.na(adm_date) & !is.na(age1))
data <- data.frame(
peak = c( rep("1st",nrow(data.frame(se_peak$age1))),
rep("2nd",nrow(data.frame(fi_peak$age1)))),
age = c( fi_peak$age1, se_peak$age1 )
)
## density plot
ad <- ggplot(data=data, aes(x=age, group=peak, fill=peak)) +
geom_density(alpha = 0.6) +
scale_fill_manual(values = c("#582C83FF","#FFC72CFF")) +
scale_x_reverse(limit = c(6,0),
breaks = seq(0,6,by=1),
minor_breaks = NULL)+
scale_y_continuous(minor_breaks = NULL,position = "right")+
coord_flip()+
theme_minimal()+
labs(x = "Age", y ="Density",tag = "F",fill = "Wave")+
theme(axis.title.y = element_blank(),
axis.ticks.x = element_blank(),
# legend.position = "top",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_blank(),
legend.text = element_text(size = 18),
legend.title = element_text(size = 18))
library(ISOweek)
count_dangky_week <- readRDS("D:/OUCRU/hfmd/data/count_dangky_week.rds")
child <- count_dangky_week %>% filter(birth_year >= 2017) %>% group_by(birth_week, birth_year) %>%
summarise(n=sum(n)) %>% arrange(birth_year)
colnames(child) <- c("week","year","birth")
## combine week 52 and 53
child$week <- ifelse(child$week == 53,52,child$week)
child <- child %>% group_by(year) %>%
mutate(newn = ifelse(week == 52, sum(birth[week==52]), birth)) %>%
{data.frame(.$week, .$year, .$newn )} %>% unique() %>%
magrittr::set_colnames(c("week","year","birth"))
child$week2 <- seq(1:length(child$week))
time <- data.frame()
for (i in 2017:2022){
range <- child$week[child$year == i]
if (length(range) == 52){
time_add <- seq.int(i + 7/365 ,(i+1) - 7/365,
length.out = length(range)) %>% data.frame()
} else {
time_add <- seq.int(i + 7/365 ,(i+1) - 7/365,
length.out = 52)[1:length(range)] %>% data.frame()
}
time <- rbind(time,time_add)
}
child[,5] <- time %>%
magrittr::set_colnames(c("time"))
## model fitting
fit <- mgcv::gam(birth ~ s(week) + s(week2),method = "REML",data = child)
cutpoint <- function(point){
fitt <- mgcv::gam(birth ~ s(week) + s(week2),
method = "REML",data = child[-c(point:nrow(child)),])
new_data2 <- data.frame(week = rep(1:52,7))
new_data2$week2 <- seq(1,nrow(new_data2))
new_data2$year <- rep(2017:2023,each = 52)
time <- data.frame()
for (i in 2017:2023){
range <- new_data2$week[new_data2$year == i]
time_add <- data.frame(seq.int(i + 7/365 ,(i+1) - 7/365,
length.out = length(range)))
time <- rbind(time,time_add)
}
new_data2[4] <- time %>%
magrittr::set_colnames(c("time"))
est2 <- predict.gam(fitt,newdata = new_data2,
type="response",se.fit = TRUE)
new_data2 <- new_data2 %>% mutate(
fit = est2$fit,
lwr = est2$fit - qt(0.95,nrow(new_data2))*est2$se.fit,
upr = est2$fit + qt(0.95,nrow(new_data2))*est2$se.fit,
)
out <- list()
out$point <- point
out$df <- new_data2
return(out)
}
plot_cp <- function(model){
dta <- model$df %>%
mutate(time2 = sprintf("%04d-W%02d-1", year, week),
time2 = ISOweek2date(time2))
ggplot(data = dta) +
geom_line(aes(x = time2,y = fit),col = "blue")+
geom_ribbon(aes(x = time2,ymin = lwr, ymax = upr), fill = "royalblue",alpha = 0.4)+
geom_vline(xintercept = dta$time2[dta$week2 == model$point])+
labs(y = "Number of births",
x = "Year")+
theme_minimal()
}
model <- cutpoint(270)
b_ss <- plot_cp(model)+
geom_point(data = child[1:270,]%>%
mutate(time2 = sprintf("%04d-W%02d-1", year, week),
time2 = ISOweek2date(time2)) ,
aes(x = time2, y = birth),alpha = 0.5)+
# annotate("text", x= 2017, y=3500, label= "Fitting") +
# annotate("text", x = 2023.5, y=3500, label = "Estimation")+
scale_x_date(date_labels = "%Y",
breaks = seq(as.Date("2017-01-01"),
as.Date("2024-01-01"),
by = "1 year"),
limits = c(as.Date("2017-01-01"),
as.Date("2024-01-01")))+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))library(nortest)
ks.test(age~peak,data = data,altervative = "two.sided")
Asymptotic two-sample Kolmogorov-Smirnov test
data: age by peak
D = 0.083993, p-value < 2.2e-16
alternative hypothesis: two-sidedlibrary(fitdistrplus)
fit1 <- fitdist(fi_peak %>% filter(age1 >0) %>% pull(age1),"lnorm")
fit2 <- fitdist(se_peak %>% filter(age1 >0) %>% pull(age1),"lnorm")
x_vals <- seq(0, 7, le = 512)
fit_1p <- data.frame(peak = "1st",age = x_vals, density = dlnorm(x_vals,
meanlog = fit1$estimate[1],
sdlog = fit1$estimate[2]))
fit_2p <- data.frame(peak = "2nd",age = x_vals, density = dlnorm(x_vals,
meanlog = fit2$estimate[1],
sdlog = fit2$estimate[2]))
data %>%
ggplot(aes(age))+
geom_histogram(binwidth = 0.5,
color = "white",
fill = "black",
alpha = .5)+
geom_line(data = rbind(fit_1p,fit_2p),aes(x = age,y = density*10000))+
facet_wrap(~peak)+
scale_x_continuous(breaks = seq(0,7,by=1),limits = c(0,7))+
theme_minimal()+
scale_y_continuous(
name = "Cases count",
sec.axis = sec_axis( trans=~./10000, name="Density")
) 
detach("package:fitdistrplus", unload = TRUE)
detach("package:MASS", unload = TRUE)OUCRU serum bank from Children hospital 1, so we calculate the catchment area of CH1 by cases ratio method. (Zinszer et al. 2014)
The cumulative case ratio was defined as the ratio of the observed to the expected number of HFMD visits to CH1 from a district. The expected number of HFMD visits to CH1 from a particular district was calculated by multiplying the district’s population by the cumulative case rate for that hospital.
\[ \begin{align} Cummulative \ cases \ rate &= \frac{observed \ cases \ (districts)}{expected \ cases \ (districts)} \\ &= \frac{observed \ cases \ (districts)}{district \ population \times cummulative \ cases \ rate \ (hospitals)} \end{align} \]
\[ cummulative \ cases \ rate \ (hospitals)= \frac{cases \ observed \ (hospital)}{population \ of \ children \ under \ 15 \ in \ HCMC} \]
A district was included in the catchment area if the upper limit of the 95% confidence interval for the cumulative case ratio for that district was 1 or greater because a ratio less than 1 indicated that the district contributed significantly fewer measles visits than expected for its population
## cases district per age
table1 <- read_csv("D:/OUCRU/hfmd/data/table1.csv")
## total cases cases district
table2 <- read_csv("D:/OUCRU/hfmd/data/table2.csv")
## pop district
table3 <- read_csv("D:/OUCRU/hfmd/data/table3.csv")
pop_dis_hcm <- table3 %>%
mutate(age2 = word(age,1),
age2 = as.numeric(age2)) %>%
filter(age <= 16) %>%
mutate(district1 = district1 %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
group_by(district1) %>%
summarise(pop = sum(pop))
CH1_obs <- table1$CH1 %>% sum(na.rm = TRUE)
ch1_cum_case_rate <- CH1_obs/sum(pop_dis_hcm$pop)
tdnd1 <- data.frame(long = 106.6708,
lat = 10.7692) %>%
st_as_sf(coords = c("long", "lat"), crs = 4326)
cm_ch1_cases_ratio <- table1 %>% dplyr::select(district1,CH1) %>%
mutate(district1 = district1 %>%
str_replace_all(
c("Thanh pho Thu Duc" = "Thu Đuc")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
group_by(district1) %>%
summarise(cases = sum(CH1,na.rm = TRUE)) %>%
left_join(.,pop_dis_hcm, by = join_by(district1)) %>%
replace(is.na(.), 0) %>%
mutate(expected_cases = pop*ch1_cum_case_rate,
cum_case_ratio = cases/expected_cases,
lwr = pois.exact(cases)$lower/expected_cases,
upr = pois.exact(cases)$upper/expected_cases,
cut = cut(upr,
breaks = c(0,1,2,3,10),
labels = c("< 1",">= 1",">= 2",">= 3"),
right = F)
)cm_ch1_cases_ratio %>%
left_join(qhtp, ., by = join_by(varname_2 == district1)) %>%
ggplot() +
geom_sf(aes(fill = cut,geometry = geom),show.legend = T)+
scale_fill_manual(
values = c(
"< 1" = "#FFFFFFFF",
">= 1" = "#6BAED6FF",
">= 2" = "#2171B5FF",
">= 3" = "#08306BFF"
),
name = "95% CI Upper bound of \ncumulative case ratio of each districts"
)+
geom_sf_text(aes(label = nl_name_2,geometry = geom),size=2,color = "black")+
geom_sf(data = tdnd1, shape = 17,
color = "yellow", size = 1)+
theme_void()
library(ggsci)
data_pt %>% group_by(qhchuan) %>%
count() %>% as.data.frame() %>%
dplyr::mutate(pre = n / sum(n)) %>%
left_join(qhtp, ., by = join_by(varname_2 == qhchuan)) %>%
ggplot() +
geom_sf(aes(fill = pre),show.legend = T)+
# scale_fill_continuous(low="yellow", high="red",
# guide="colorbar",na.value="white",
# name = "Percentage",
# # limits = c(0,0.12),
# labels = scales::label_percent())+
scale_fill_gsea(
na.value = "white",
# breaks = c(0.1, 0.2, 0.3, 0.4, 0.5),
# limits = c(0, 0.5)
# rescaler = function(x, to = c(0, 1), from = NULL) {
# scales::rescale(x ^ 0.5)
# },
labels = scales::label_percent(),
name = "Percentage (%)"
)+
geom_sf_text(aes(label = nl_name_2),size=2)+
geom_sf(data = tdnd1, shape = 17,
color = "blue", size = 2)+
theme_void()
tdnd1 <- data.frame(long = 106.6702,
lat = 10.7686) %>%
st_as_sf(coords = c("long", "lat"), crs = 4326)
data_pt %>% group_by(qhchuan) %>%
count() %>% as.data.frame() %>%
# dplyr::mutate(pre = n / sum(n)) %>%
left_join(qhtp, ., by = join_by(varname_2 == qhchuan)) %>%
ggplot() +
geom_sf(aes(fill = n),show.legend = T)+
paletteer::scale_fill_paletteer_c("ggthemes::Classic Red",
na.value="white",
name = "Number of \nserum samples")+
# geom_sf_text(aes(label = nl_name_2),size=2)+
geom_sf(data = tdnd1, shape = 17,
color = "blue", size = 2)+
theme_void()
ggsave("./fig_manuscript/sero-distribution.svg",
width = 15,height = 8,dpi = 500)district_consider <- cm_ch1_cases_ratio %>%
filter(cut != "< 1") %>%
pull(district1) %>%
as.character()
## function to calculate age profile
hfmd <- data_pt %>%
as_tibble() |>
mutate(collection = id |>
str_remove(".*-") |>
as.numeric() |>
divide_by(1e4) |>
round(),
col_date2 = as.numeric(col_date),
across(pos, ~ .x > 0))
predict2 <- function(...) predict(..., type = "response") |> as.vector()
age_profile <- function(data, age_values = seq(0, 15, le = 512), ci = .95) {
model <- gam(pos ~ s(age), binomial, data)
link_inv <- family(model)$linkinv
df <- nrow(data) - length(coef(model))
p <- (1 - ci) / 2
model |>
predict(list(age = age_values), se.fit = TRUE) %>%
c(list(age = age_values), .) |>
as_tibble() |>
mutate(lwr = link_inv(fit + qt( p, df) * se.fit),
upr = link_inv(fit + qt(1 - p, df) * se.fit),
fit = link_inv(fit)) |>
select(- se.fit)
}
age_profile_unconstrained <- function(data, age_values = seq(0, 15, le = 512),
ci = .95) {
data |>
group_by(collection) |>
group_map(~ age_profile(.x, age_values, ci))
}
shift_right <- function(n, x) {
if (n < 1) return(x)
c(rep(NA, n), head(x, -n))
}
age_profile_constrained_cohort2 <- function(data, age_values = seq(0, 15, le = 512),
ci = .95, n = 100) {
dpy <- 365 # number of days per year
mean_collection_times <- data |>
group_by(collection) |>
summarise(mean_col_date = mean(col_date2)) |>
with(setNames(mean_col_date, collection))
cohorts <- cumsum(c(0, diff(mean_collection_times))) |>
divide_by(dpy * mean(diff(age_values))) |>
round() |>
map(shift_right, age_values)
age_time <- map2(mean_collection_times, cohorts,
~ tibble(collection_time = .x, cohort = .y))
age_time_inv <- age_time |>
map(~ cbind(.x, age = age_values)) |>
bind_rows() |>
na.exclude()
data |>
# Step 1:
group_by(collection) |>
group_modify(~ .x |>
age_profile(age_values, ci) |>
mutate(across(c(fit, lwr, upr), ~ map(.x, ~ rbinom(n, 1, .x))))) |>
group_split() |>
map2(age_time, bind_cols) |>
bind_rows() |>
unnest(c(fit, lwr, upr)) |>
pivot_longer(c(fit, lwr, upr), names_to = "line", values_to = "seropositvty") |>
# Step 2a:
filter(cohort < max(age) - diff(range(mean_collection_times)) / dpy) |>
group_by(cohort, line) |>
group_modify(~ .x %>%
scam(seropositvty ~ s(collection_time, bs = "mpi"), binomial, .) |>
predict2(list(collection_time = mean_collection_times)) %>%
tibble(collection_time = mean_collection_times,
seroprevalence = .)) |>
ungroup() |>
# Step 2b:
left_join(age_time_inv, c("cohort", "collection_time")) |>
group_by(collection_time, line) |>
group_modify(~ .x |>
right_join(tibble(age = age_values), "age") |> ### added
arrange(age) |> ### added
mutate(across(seroprevalence,
~ gam(.x ~ s(age), betar) |>
predict2(list(age = age_values))))) |> ### modified
ungroup() |>
pivot_wider(names_from = line, values_from = seroprevalence) |>
group_by(collection_time) |>
group_split()
}
hfmd_cm <- hfmd %>% filter(qhchuan %in% district_consider)
constrained_age_profiles_cohort2_cm <- age_profile_constrained_cohort2(hfmd_cm)
attack_rates <- map2(head(constrained_age_profiles_cohort2_cm, -1),
constrained_age_profiles_cohort2_cm[-1],
~ left_join(na.exclude(.x), na.exclude(.y), "cohort") |>
mutate(attack = (fit.y - fit.x) / (1 - fit.x)))
## population
age_structure <- census2019 |>
filter(province == "Thành phố Hồ Chí Minh") |>
mutate(district = district %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
filter(district %in% district_consider) %>%
group_by(age) |>
summarise(n = sum(n)) |>
mutate(across(age, ~ stringr::str_remove(.x, " tuổi| \\+") |> as.integer())) |>
arrange(age) |>
filter(age < 17)
mod <- lm(n ~ age, age_structure)
incidences <- map(attack_rates,
~ mutate(.x, incidence = (1 - fit.x) * attack *
predict(mod, list(age = cohort))))
incidence1 <- df1 %>%
filter(year(adm_date) == 2023 &
medi_cen %in% c("Bệnh viện Nhi đồng 1",
"Bênh viện Nhi Đồng 1",
"Bệnh viện Nhi Đồng 1")) %>%
mutate(district2 = district %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
filter(district2 %in% district_consider) %>%
mutate(adm_date2 = as.numeric(adm_date),
cohort = interval(dob, "2023-01-01") / years(1)) %>%
select(adm_date2,cohort,adm_week)
mean_collection_times <- hfmd |>
group_by(collection) |>
summarise(mean_col_date = mean(col_date2)) |>
with(setNames(mean_col_date, collection))
ouut <- list()
dat <- list()
for (i in 1:3){
dat[[i]] <- incidence1 %>%
filter(adm_date2 >= as.numeric(mean_collection_times[i]) &
adm_date2 <= as.numeric(mean_collection_times[i+1])) %>%
# mutate(age_gr = cut(cohort, breaks = seq(0,16),right = T)) %>%
# na.omit(age_gr) %>%
# group_by(age_gr) %>%
# count() %>%
# mutate(age_gr2 = as.numeric(age_gr)) %>%
# ungroup() %>%
# select(-age_gr)
{}
# ouut[[i]] <- scam(n ~ s(age_gr2,bs = "po"),data = dat[[i]]) %>%
# predict(list(age_gr2 = incidences[[i]]$cohort))%>%
# tibble(age = incidences[[i]]$cohort,
# incidence = .)
}
age_profile_sp_cm <- constrained_age_profiles_cohort2_cm %>%
bind_rows() %>%
ggplot(aes(x = age, y = fit)) +
geom_line(aes(x = age, fit))+
geom_point(data = data_pt %>%
mutate(collection_time = factor(col_time,
levels = c("Dec 2022","Apr 2023","Aug 2023","Dec 2023"))),
aes(x = age, y = pos),shape = "|")+
facet_wrap(~factor(collection_time,
labels = c("Dec 2022","Apr 2023","Aug 2023","Dec 2023")),
ncol = 4)+
geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "blue", alpha = 0.3) +
labs(y = "Seroprevalence (%)",x = "Age (years)",tag="B")+
scale_y_continuous(labels = scales::label_percent(scale = 100),limits = c(0,1))+
# coord_cartesian(ylim = c(0, 1))+
theme_bw()+
theme(axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
legend.position = "none",
plot.tag = element_text(face = "bold", size = 18),
axis.title.y = element_text(size = 18),
axis.text.y = element_text(size = 18),
strip.text.x = element_text(size = 18))
ex_incid_cm <- ggplot()+
geom_histogram(data = dat %>% bind_rows(.id = "id") %>% filter(cohort >0 ),
aes(cohort),binwidth = 0.5,
color = "white",fill = "black",alpha = 0.5)+
geom_line(data = incidences %>% bind_rows(.id = "id"),
aes(x = cohort, y = incidence/12))+
scale_y_continuous(name = "Reported data",
breaks = seq(0,1000,by=200),
sec.axis = sec_axis(~.*12,
name="Serological data",
breaks = seq(0,12000,by=2000)))+
facet_wrap(~factor(id,labels = c("12/2022 - 4/2023",
"4/2023 - 8/2023",
"8/2023 - 12/2023")))+
coord_cartesian(ylim=c(0,1000))+
labs(tag = "D",
y = "Total cases count",
x = "Age (years)")+
scale_x_continuous(breaks = seq(0,14,by = 2),
limits = c(0,14))+
theme_bw()+
theme(
legend.position = "top",
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 18),
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
legend.title = element_text(size = 18),
strip.text = element_text(size = 18),
panel.grid.minor.x = element_blank())
ts_cm <- incidence1 %>% group_by(adm_week) %>% count() %>%
ggplot()+
geom_bar(aes(x = as.Date(adm_week), y = n),stat = "identity",
alpha = 0.5) +
geom_point(aes(x = as.Date(col_date), y = pos*1000),
shape = "|",data = data_pt)+
labs(x = "Admission week",y = "Cases",tag = "B")+
annotate("rect", fill = "blue",
xmin = as.Date(c("2022-12-01","2023-04-01","2023-08-01","2023-12-01")),
xmax = as.Date(c("2023-01-04","2023-04-30","2023-08-30","2023-12-30")),
ymin = 0, ymax = Inf, alpha = .2)+
theme_classic()+
scale_y_continuous(breaks = seq(0,1000,by = 500),
limit =c(0-1,1000+1))+
scale_x_date(
breaks = as.Date(c("2022-12-15", "2023-04-15", "2023-08-15", "2023-12-15")),
labels = c("Dec 2022", "Apr 2023", "Aug 2023", "Dec 2023"),
limits = c(as.Date("2022-11-24"),as.Date("2024-01-01")))+
theme(
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 18),
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))library(grid)
# left_col <- age_profile_sp_cm / ts/ hm
#
# right_col <- plot_spacer() / b_ss /
# wrap_elements(full = ad + theme(plot.margin = margin(0, 250, 25, 0)))
#
# (left_col | right_col) +
# plot_layout(widths = c(2.5, 2))
#
# ggsave("./fig_manuscript/fig1_2.svg",
# width = 18,height = 10,dpi = 500)left_col2 <- age_profile_sp_cm / ts/ hm
right_col2 <- ex_incid_cm / b_ss /
wrap_elements(full = ad + theme(plot.margin = margin(-20, 250, 20*3.25, 0),
legend.position = "right"))
(left_col2 | right_col2) +
plot_layout(widths = c(2.5, 2))
ggsave("./fig_manuscript/fig1_2a.svg",
width = 18,height = 10,dpi = 500)ex_incid_cm/
ts_cm
ggsave("./fig_manuscript/fig2_2.svg",
width = 18,height = 10,dpi = 500)pop <- readRDS("D:/OUCRU/hfmd/data/count_dangky.rds")
pop_a <- pop %>% group_by(birth_month, birth_year) %>%
summarise(n=sum(n)) %>% arrange(birth_year)
colnames(pop_a) <- c("m","y","n")
pop_a$dob <- str_c(pop_a$y,pop_a$m,sep = "-") %>% ym()
time1 = df1$adm_date
age1 = df1$age1
dob = pop_a$dob
n = pop_a$n
dft <- data.frame(time1,age1) %>%
filter(!is.na(time1) & !is.na(age1)) %>%
arrange(time1)
dft$agr=as.factor(cut(dft$age1, c(0,0.5,
1,1.5,
2,2.5,
3,3.5,
4,4.5,5,5.5,6,100), right=TRUE ))
levels (dft$agr) = c("0-0.5", "0.5-1", "1.0-1.5",
"1.5-2","2-2.5","2.5-3",
"3-3.5","3.5-4","4-4.5",
"4.5-5","5-5.5","5.5-6","6+")
sus_pop <- data.frame(dob = dob, n = n)
out_total <- data.frame()
hfmd23 <- df1 %>% filter(year(adm_week) == "2023") %>%
filter(!is.na(adm_week) ) %>%
count(adm_week)
hfmd23$week <- 1:length(hfmd23$adm_week)
hfmd23$week <- ifelse(hfmd23$week == 53,52,hfmd23$week)
hfmd23$n2 <- ifelse(hfmd23$week == 52, sum(hfmd23$n[hfmd23$week==52]), hfmd23$n)
hfmd23 <- hfmd23[-53,]
dateaa <- hfmd23$adm_week+3
sus_pop <- data.frame(dob = dob, n = n)
for (i in 1:52){
sus_pop$age <- interval(sus_pop$dob, dateaa[i]) / years(1)
sus_pop$agr=as.factor(cut(sus_pop$age, c(0,0.5,
1,1.5,
2,2.5,
3,3.5,
4,4.5,5,5.5,6,100), right=TRUE ))
levels (sus_pop$agr) = c("0-0.5", "0.5-1", "1.0-1.5",
"1.5-2","2-2.5","2.5-3",
"3-3.5","3.5-4","4-4.5",
"4.5-5","5-5.5","5.5-6","6+")
outcum <- sus_pop %>% group_by(agr) %>%
summarise(n = sum(n)) %>%
as.data.frame()
outcum$date <- rep(dateaa[i],nrow(outcum))
out_total <- rbind(out_total,outcum)
}
deno <- out_total %>%
pivot_wider(names_from = agr, values_from = n) %>% as.data.frame()
casss <- wwww$wdat
casss$agr=as.factor(cut(casss$age, c(0,0.5,1,1.5,2,2.5,
3,3.5,4,4.5,5,5.5,6,100), right=TRUE ))
levels (casss$agr) = c("0-0.5", "0.5-1", "1.0-1.5",
"1.5-2","2-2.5","2.5-3",
"3-3.5","3.5-4","4-4.5",
"4.5-5","5-5.5","5.5-6","6+")
casss <- casss %>% group_by(date,agr) %>%
count() %>% pivot_wider(names_from = agr, values_from = n) %>% as.data.frame()
casss <- casss[,-ncol(casss)]
casss <- replace(casss,is.na(casss), 0)
casss <- casss[,c(1:10,13,11,14,12)]
atkr <- data.frame()
atkr <- rbind(atkr,as.numeric(casss[1,-1])/as.numeric(deno[1,-1]))
for (i in 1:51){
new <- as.numeric(casss[i+1,-1])/(as.numeric(deno[i+1,-1]) - as.numeric(casss[i,-1]))
atkr <- rbind(atkr,new)
}
atkr <- cbind(deno$date,atkr)
colnames(atkr) <- colnames(deno)
atkr <- replace(atkr,is.na(atkr), 0)
atk_plot <- atkr %>% pivot_longer(cols=colnames(atkr)[-1],
names_to= 'agr',
values_to='atk') %>% as.data.frame()
atk <- ggplot(atk_plot, aes(x=as.character(date), y=agr, fill = atk)) +
geom_raster()+
scale_fill_paletteer_c("grDevices::Inferno",
breaks = c(0.01, 0.02, 0.03, 0.04, 0.05))+
scale_y_discrete(limits=rev)+
scale_x_discrete(name = "Admission week",labels = leb_month)+
theme_minimal()+
labs(tag = "B",fill = "Attack rate",y = "Age group (years)")+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))+
guides(fill=guide_colourbar(barwidth=20,label.position="bottom"))
ts/
atk
b_ss
ggsave("./fig_manuscript/birth_seasonality.svg",
width = 15,height = 9,dpi = 500)birth_23 <- model$df %>%
mutate(time2 = sprintf("%04d-W%02d-1", year, week),
time2 = ISOweek2date(time2)) %>%
filter(year(time2) >= 2023) %>%
ggplot() +
geom_line(aes(x = time2,y = fit),col = "blue")+
geom_ribbon(aes(x = time2,ymin = lwr, ymax = upr), fill = "royalblue",alpha = 0.4)+
scale_x_date(date_breaks = "1 month",
date_labels = "%b",
limits = as.Date(c("2023-01-01","2023-12-25")),
expand = c(0, 0))+
labs(y = "Number of births",
x = "Month",
tag = "E")+
theme_minimal()+
theme(
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 18),
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))birth_matrix <- model$df %>%
bind_rows(
model$df %>%
filter(year == 2020, week == 52) %>%
mutate(week = 53,
fit = 1945.271)
) %>%
arrange(year, week) %>%
mutate(birth = sprintf("%04d-W%02d-1", year, week),
birth = ISOweek2date(birth))
date_consider <- birth_matrix %>%
filter(year(birth) == 2023) %>%
pull(birth)
age_dis_fn <- data.frame()
for (i in 1:length(date_consider)){
age_dis_week <- birth_matrix %>%
mutate(age = difftime(date_consider[i],birth, units = "weeks") %>%
as.numeric(),
age = age*(1/52),
date = date_consider[i]) %>%
# filter(age > 0) %>%
select(-birth)
age_dis_fn <- rbind(age_dis_fn,age_dis_week)
}
ggplot(age_dis_fn,
aes(x=date, y=age, fill = fit)) +
theme_minimal()+
geom_raster()+
scale_y_reverse(name = "Age (years)",lim= rev(c(0,6)),breaks = seq(0,6))+
scale_x_date(date_breaks = "1 month",date_labels = "%b")+
labs(
# tag = "C",
fill = "Number of births")+
scale_fill_paletteer_c("grDevices::Inferno")+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))+
guides(fill=guide_colourbar(barwidth=20,label.position="bottom"),
color = "none") 
I filter number of children admitted in CH1 from HCDC data for the numerator, and population of each age and each district from 2019 census data combined with birth data from hep B registry data (age 0) for the denominator.
\[ P(CH1 \ hospitalization_{[a,d]}) = \frac{cases_{[a,d]}}{population_{[a,d]}} \]
Then I fitted the GAM for \(P(CH1 \ hospitalization_{[a,d]})\) as a function of age for each district to predict the continuous age from figure in 4.4.2.
c_ad <- df1 %>%
filter(year(adm_date) == 2023 &
medi_cen %in% c("Bệnh viện Nhi đồng 1",
"Bênh viện Nhi Đồng 1",
"Bệnh viện Nhi Đồng 1")) %>%
mutate(district2 = district %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
select(age,district2) %>%
group_by(district2,age) %>%
filter(age < 17) %>%
count() %>%
ungroup()
N_ad <- census2019 |>
filter(province == "Thành phố Hồ Chí Minh") |>
mutate(district = district %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
group_by(district,age) |>
summarise(n = sum(n),.groups = "drop") |>
mutate(across(age, ~ stringr::str_remove(.x, " tuổi| \\+") |> as.integer())) |>
arrange(age) |>
filter(age < 17)
birth_2019 <- count_dangky_week %>%
filter(birth_year == 2019) %>%
mutate(district = district_reg %>%
str_replace_all(
c("Quận 2" = "Thủ Đức",
"Quận 9" = "Thủ Đức")) %>%
str_remove("Quận|Huyện") %>%
trimws(which = "both") %>%
stri_trans_general("latin-ascii") %>%
tolower()) %>%
filter(district %in% unique(N_ad$district)) %>%
group_by(district) %>%
summarise(n=sum(n),.groups = "drop") %>%
mutate(age = 0)
prob_a_d <- left_join(c_ad,rbind(birth_2019,N_ad),
by = join_by(age == age,
district2 == district)) %>%
mutate(prob_hos=n.x/n.y)
# ggplot(prob_a_d, aes(x = age,y=prob_hos,color = district2))+
# geom_line()age_new <- age_dis_fn %>% filter(date == range(date)[2]) %>%
select(age,fit) %>% pull(age)
pred_prob <- prob_a_d %>%
group_by(district2) %>%
group_modify(~ {
mod <- gam(prob_hos ~ s(age,bs = "bs"), data = .x, method = "REML")
pred <- predict(mod, newdata = tibble(age = age_new), type = "response")
tibble(age = age_new, fit = pred)
}) %>%
ungroup()
ggplot()+
geom_line(data = pred_prob, aes(x = age,y = fit))+
geom_point(data = prob_a_d,aes(x = age,y = prob_hos))+
labs(y = "Hospitalizations probability")+
facet_wrap(~district2)+
theme_bw()+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18)) 
Then I used predicted hospitalization probability multiply the age distribution at the end 2023 estimated from section 4.4.2 to calculate the expected age distribution caught by CH1.
exp_age_23 <- age_dis_fn %>%
filter(date == range(date)[2]) %>%
select(age,fit) %>%
right_join(.,pred_prob,by = join_by(age)) %>%
arrange(district2) %>%
mutate(expected_admission = fit.x*fit.y)
exp_age_23 %>%
ggplot(aes(x = age,y = expected_admission))+
geom_line()+
scale_x_continuous(limits = c(0,7),
breaks = seq(0,7,by=1))+
labs(y = "Expected CH1 admission")+
facet_wrap(~district2)+
theme_bw()+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))
In this step, I filtered district in catchment area estimated in 4.2.1 and sum it by age, this is \(N_a\).
expected_age_cm <- exp_age_23 %>%
# filter(district2 %in% district_consider) %>%
group_by(age) %>%
summarise(n = sum(expected_admission))
expected_age_cm %>%
ggplot(aes(x = age,y = n))+
geom_line()+
theme_minimal()+
theme(axis.title.y = element_text(size = 18),
axis.ticks.x = element_blank(),
legend.position = "bottom",
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.text.x = element_text(size = 18),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18))
Then applied constrained algorithm to estimated age profile and used the formula to calculated expected incidence
\[ (seroprevalence_{t,a}-seroprevalence_{t - \Delta t,a- \Delta t}) \times N_a \]
age_pro5 <- age_profile_constrained_cohort2(hfmd_cm,age_values = expected_age_cm$age)
attack_rates2 <- map2(head(age_pro5, -1),
age_pro5[-1],
~ left_join(na.exclude(.x), na.exclude(.y), "cohort")|>
mutate(sp_gap = (fit.y - fit.x)))
exp_in_ch1 <- map(attack_rates2, ~ left_join(.x,expected_age_cm, by = join_by(cohort ==age))) %>%
bind_rows(.id = "id") %>%
mutate(exp_in = sp_gap*n) %>%
ggplot()+
geom_line(aes(x = cohort,y = exp_in))+
geom_histogram(data = dat %>% bind_rows(.id = "id") %>% filter(cohort >0),
aes(cohort),binwidth = 0.5,
color = "white",fill = "black",alpha = 0.2)+
facet_wrap(~factor(id,labels = c("12/2022 - 4/2023",
"4/2023 - 8/2023",
"8/2023 - 12/2023")))+
labs(y = "Expected incidence",x = "Age (years)",tag = "A")+
theme_bw()+
theme(
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 18),
legend.text = element_text(size = 18),
plot.tag = element_text(face = "bold", size = 18),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
legend.title = element_text(size = 18),
strip.text = element_text(size = 18),
panel.grid.minor.x = element_blank())width_ratio <- c(2.5, 1)
row0 <- (exp_in_ch1 | plot_spacer())+
plot_layout(widths = width_ratio)
row1 <- (age_profile_sp_cm | plot_spacer())+
plot_layout(widths = width_ratio)
row2 <- ((ts/hm2) | (plot_spacer()/
wrap_elements(full = ad +
theme(plot.margin = margin(-15,0, 7, 0)))))+
plot_layout(widths = width_ratio)
row3 <- (birth_23 | plot_spacer())+
plot_layout(widths = width_ratio)
(row0 /row1 /row2 / row3) +
plot_layout(heights = c(1,1,3,1))
ggsave("./fig_manuscript/fig2_3.svg",
width = 15,height = 15,dpi = 500)
ggsave("./fig_manuscript/fig2_3.png",
width = 15,height = 15,dpi = 500)