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analysis_VOCs_GISAID records_Australia.R
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# ANALYSIS OF GROWTH ADVANTAGE OF DIFFERENT VOCs IN AUSTRALIA (GISAID RECORDS)
# T. Wenseleers
# last update 7 OCTOBER 2021
library(nnet)
# devtools::install_github("melff/mclogit",subdir="pkg") # install latest development version of mclogit, to add emmeans support
library(mclogit)
# remotes::install_github("rvlenth/emmeans", dependencies = TRUE, force = TRUE)
library(emmeans)
library(readr)
library(ggplot2)
library(ggthemes)
library(scales)
library(stringr)
library(lubridate)
today = as.Date(Sys.time()) # we use the file date version as our definition of "today"
today = as.Date("2021-10-08")
today_num = as.numeric(today)
plotdir = "Australia_GISAID"
suppressWarnings(dir.create(paste0(".//plots//",plotdir)))
# import GISAID records for Australia
d1 = read_tsv(file(".//data//GISAID//Australia//gisaid_hcov-19_2021_10_08_08_subm_jan_aug_2020.tsv"), col_types = cols(.default = "c"))
d2 = read_tsv(file(".//data//GISAID//Australia//gisaid_hcov-19_2021_10_08_08_subm_sept_dec_2020.tsv"), col_types = cols(.default = "c"))
d3 = read_tsv(file(".//data//GISAID//Australia//gisaid_hcov-19_2021_10_08_08_subm_jan_aug_2021.tsv"), col_types = cols(.default = "c"))
d4 = read_tsv(file(".//data//GISAID//Australia//gisaid_hcov-19_2021_10_08_08_subm_sept_oct_2021.tsv"), col_types = cols(.default = "c"))
GISAID = as.data.frame(rbind(d1, d2, d3, d4))
colnames(GISAID) = c("Virus name","Accession ID","date","Location","host",
"Additional location information","Sampling strategy","Gender",
"Patient age","Patient status","Last vaccinated","Passage","Specimen",
"Additional host information","pango_lineage","Clade","AA Substitutions")
date_isvalid = sapply(GISAID$date, function (s) str_count(s, pattern = "-")==2)
GISAID = GISAID[date_isvalid,]
GISAID$date = as.Date(GISAID$date)
GISAID = GISAID[!is.na(GISAID$date),]
GISAID = GISAID[GISAID$host=="Human",]
GISAID = GISAID[GISAID$date>=as.Date("2021-01-01"),]
range(GISAID$date) # "2021-01-01" "2021-10-03"
GISAID$Week = lubridate::week(GISAID$date)
GISAID$Year = lubridate::year(GISAID$date)
GISAID$Year_Week = interaction(GISAID$Year,GISAID$Week)
GISAID$floor_date = as.Date(as.character(cut(GISAID$date, "week")))+3.5 # week midpoint date
GISAID$DATE_NUM = as.numeric(GISAID$date)
GISAID = GISAID[GISAID$pango_lineage!="None",]
GISAID$pango_lineage[grepl("B.1.177",GISAID$pango_lineage,fixed=T)] = "B.1.177+"
GISAID$pango_lineage[grepl("B.1.621",GISAID$pango_lineage,fixed=T)] = "B.1.621+"
GISAID$pango_lineage[grepl("B.1.36\\>",GISAID$pango_lineage)] = "B.1.36+"
# GISAID$pango_lineage[grepl("B.1.617.2|AY",GISAID$pango_lineage)] = "Delta (B.1.617.2 & AY.X)"
# sel_target_VOC = "Delta (B.1.617.2 & AY.X)"
sel_target_VOC = "B.1.617.2"
GISAID$LINEAGE = GISAID$pango_lineage
nrow(GISAID) # 16496
# ANALYSIS OF VOCs IN AUSTRALIA ####
# sel_countries = "Australia"
# GISAID[GISAID$country %in% sel_countries,]
table(GISAID$Location)
GISAID_sel = GISAID
sum(GISAID_sel$LINEAGE==sel_target_VOC) # 4386
table(GISAID_sel$LINEAGE)
main_lineages = names(table(GISAID_sel$LINEAGE))[100*table(GISAID_sel$LINEAGE)/sum(table(GISAID_sel$LINEAGE)) > 1]
main_lineages
# "AY.23" "AY.30" "AY.4" "B.1.1.7" "B.1.617.2"
VOCs = c("B.1.617.1","B.1.617.2","B.1.617+","B.1.618","B.1.1.7","B.1.351","P.1","B.1.1.318","B.1.1.207","B.1.429",
"B.1.525","B.1.526","B.1.1.519","B.1.1.318","B.1.621+",sel_target_VOC)
main_lineages = union(main_lineages, VOCs)
GISAID_sel$LINEAGE[!(GISAID_sel$LINEAGE %in% main_lineages)] = "other" # minority lineages & non-VOCs
remove = names(table(GISAID_sel$LINEAGE))[table(GISAID_sel$LINEAGE)/sum(table(GISAID_sel$LINEAGE)) < 0.01]
remove = remove[!(remove %in% c("B.1.351","B.1.1.7","P.1","B.1.617.2","B.1.617.1","B.1.1.519","B.1.621+",sel_target_VOC,"B.1.351","P.1","AY.23","AY.30","AY.4"))]
GISAID_sel$LINEAGE[(GISAID_sel$LINEAGE %in% remove)] = "other" # minority VOCs
table(GISAID_sel$LINEAGE)
GISAID_sel$LINEAGE = factor(GISAID_sel$LINEAGE)
GISAID_sel$LINEAGE = relevel(GISAID_sel$LINEAGE, ref="B.1.1.7") # we code UK strain as the reference level
levels(GISAID_sel$LINEAGE)
# [1] "B.1.1.7" "AY.23" "AY.30" "AY.4" "B.1.1.519" "B.1.351" "B.1.617.1"
# [8] "B.1.617.2" "other" "P.1"
levels_LINEAGE = c("B.1.1.7","B.1.351","B.1.1.519","P.1","B.1.617.1",
sel_target_VOC,"AY.4","AY.23","AY.30","other")
GISAID_sel$LINEAGE = factor(GISAID_sel$LINEAGE, levels=levels_LINEAGE)
# aggregated data to make Muller plots of raw data
# aggregate by day to identify days on which INSA performed (days with a lot of sequences)
# we subset the data to just those days to avoid sampling biases (delta infection clusters etc)
data_agbyday = as.data.frame(table(GISAID_sel$date, GISAID_sel$LINEAGE))
colnames(data_agbyday) = c("date", "LINEAGE", "count")
data_agbyday_sum = aggregate(count ~ date, data=data_agbyday, sum)
data_agbyday$total = data_agbyday_sum$count[match(data_agbyday$date, data_agbyday_sum$date)]
sum(data_agbyday[data_agbyday$LINEAGE=="P.1","total"]) == nrow(GISAID_sel) # correct
data_agbyday$date = as.Date(as.character(data_agbyday$date))
data_agbyday$LINEAGE = factor(data_agbyday$LINEAGE, levels=levels_LINEAGE)
data_agbyday$date_num = as.numeric(data_agbyday$date)
data_agbyday$prop = data_agbyday$count/data_agbyday$total
data_agbyday$floor_date = NULL
# qplot(data=data_agbyday, x=date, y=total, colour=total>20, fill=total>20, geom="col")
GISAID_sel$total_sequenced_on_that_day = data_agbyday$total[match(GISAID_sel$date, data_agbyday$date)]
# GISAID_sel = GISAID_sel[GISAID_sel$total_sequenced_on_that_day>20,] # dates on which was performed
# nrow(GISAID_sel) # 5710
# aggregated by week for selected variant lineages
data_agbyweek = as.data.frame(table(GISAID_sel$floor_date, GISAID_sel$LINEAGE))
colnames(data_agbyweek) = c("floor_date", "LINEAGE", "count")
data_agbyweek_sum = aggregate(count ~ floor_date, data=data_agbyweek, sum)
data_agbyweek$total = data_agbyweek_sum$count[match(data_agbyweek$floor_date, data_agbyweek_sum$floor_date)]
sum(data_agbyweek[data_agbyweek$LINEAGE=="P.1","total"]) == nrow(GISAID_sel) # correct
data_agbyweek$collection_date = as.Date(as.character(data_agbyweek$floor_date))
data_agbyweek$LINEAGE = factor(data_agbyweek$LINEAGE, levels=levels_LINEAGE)
data_agbyweek$collection_date_num = as.numeric(data_agbyweek$collection_date)
data_agbyweek$prop = data_agbyweek$count/data_agbyweek$total
data_agbyweek$floor_date = NULL
# MULLER PLOT (RAW DATA)
n2 = length(levels(GISAID_sel$LINEAGE))
lineage_cols2 = hcl(h = seq(15, 320, length = n2), l = 65, c = 200)
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="B.1.1.7")] = "#0085FF"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="B.1.351")] = "#9A9D00"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="B.1.177+")] = "grey55"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="P.1")] = "cyan3"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="B.1.617.1")] = muted("magenta")
lineage_cols2[which(levels(GISAID_sel$LINEAGE)==sel_target_VOC)] = "magenta"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="AY.4")] = muted("red",c=150,l=60)
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="AY.23")] = muted("red",c=150,l=40)
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="AY.30")] = "red"
lineage_cols2[which(levels(GISAID_sel$LINEAGE)=="other")] = "grey75"
muller_australia_raw2 = ggplot(data=data_agbyweek, aes(x=collection_date, y=count, group=LINEAGE)) +
# facet_wrap(~ STATE, ncol=1) +
# geom_col(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE1), width=1, position="fill") +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE, group=LINEAGE), position="fill") +
scale_fill_manual("", values=lineage_cols2) +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01"))),1,1),
limits=as.Date(c("2021-01-01",NA)),
expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=2, byrow=T), fill = guide_legend(reverse=F, nrow=2, byrow=T)) +
theme_hc() +
# labs(title = "MAIN SARS-CoV2 VARIANT LINEAGES IN THE UK") +
ylab("Share") +
theme(legend.position="right",
axis.title.x=element_blank()) +
labs(title = "SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN AUSTRALIA\n(GISAID data)")
# +
# coord_cartesian(xlim=c(1,max(GISAID_sel$Week)))
muller_australia_raw2
ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots_raw data.png"), width=8, height=6)
# ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots_raw data.pdf"), width=8, height=6)
# multinomial fits
data_agbyweek$LINEAGE = relevel(data_agbyweek$LINEAGE, ref=sel_target_VOC)
data_agbyweek$DATE_NUM = as.numeric(data_agbyweek$collection_date)
library(nnet)
library(splines)
set.seed(1)
fit1_australia_multi = nnet::multinom(LINEAGE ~ scale(DATE_NUM), weights=count, data=data_agbyweek, maxit=1000)
fit2_australia_multi = nnet::multinom(LINEAGE ~ ns(DATE_NUM, df=2), weights=count, data=data_agbyweek, maxit=1000)
BIC(fit1_australia_multi, fit2_australia_multi)
# df BIC
# fit1_australia_multi 18 30807.01
# fit2_australia_multi 27 28840.00
# growth rate advantage compared to Delta (difference in growth rate per day)
emtraustralia = emtrends(fit2_australia_multi, trt.vs.ctrl ~ LINEAGE,
var="DATE_NUM", mode="latent",
at=list(DATE_NUM=max(GISAID_sel$DATE_NUM)),
adjust="none", df=NA)
delta_r_australia = data.frame(confint(emtraustralia,
adjust="none", df=NA)$contrasts,
p.value=as.data.frame(emtraustralia$contrasts,
adjust="none", df=NA)$p.value)
delta_r_australia
# contrast estimate SE df asymp.LCL asymp.UCL p.value
# 1 B.1.1.7 - B.1.617.2 -0.062383012 0.004413618 NA -0.07103354 -0.053732480 2.337313e-45
# 2 B.1.351 - B.1.617.2 -0.055159407 0.008318062 NA -0.07146251 -0.038856305 3.327854e-11
# 3 B.1.1.519 - B.1.617.2 -0.789011949 0.548473515 NA -1.86400028 0.285976387 1.502753e-01
# 4 P.1 - B.1.617.2 -0.558959377 0.364074476 NA -1.27253224 0.154613484 1.247130e-01
# 5 B.1.617.1 - B.1.617.2 -0.212719200 0.021300577 NA -0.25446756 -0.170970836 1.745579e-23
# 6 AY.4 - B.1.617.2 -0.017346955 0.002797842 NA -0.02283062 -0.011863286 5.642002e-10
# 7 AY.23 - B.1.617.2 -0.133656464 0.012901184 NA -0.15894232 -0.108370608 3.769018e-25
# 8 AY.30 - B.1.617.2 -0.048074542 0.001599227 NA -0.05120897 -0.044940116 1.562397e-198
# 9 other - B.1.617.2 -0.008125312 0.002174769 NA -0.01238778 -0.003862844 1.868419e-04
# AY.30 25% less infectious than B1.1.617.2 with GT of 4.7 days
exp(0.048074542*4.7) # = 1.25
# fitted prop of different LINEAGES in the australia today
multinom_preds_today_avg = data.frame(emmeans(fit2_australia_multi, ~ LINEAGE|1,
at=list(DATE_NUM=today_num),
mode="prob", df=NA))
multinom_preds_today_avg
# LINEAGE prob SE df asymp.LCL asymp.UCL
# 1 B.1.617.2 6.792678e-01 1.210544e-02 NA 6.555415e-01 7.029940e-01
# 2 B.1.1.7 1.406711e-04 5.946915e-05 NA 2.411375e-05 2.572285e-04
# 3 B.1.351 4.326675e-05 3.767890e-05 NA -3.058254e-05 1.171160e-04
# 4 B.1.1.519 9.579088e-40 5.774791e-38 NA -1.122259e-37 1.141417e-37
# 5 P.1 1.587975e-32 7.081808e-31 NA -1.372129e-30 1.403889e-30
# 6 B.1.617.1 3.391984e-10 6.736848e-10 NA -9.811996e-10 1.659596e-09
# 7 AY.4 2.305436e-02 3.242839e-03 NA 1.669851e-02 2.941021e-02
# 8 AY.23 6.658428e-05 4.101148e-05 NA -1.379675e-05 1.469653e-04
# 9 AY.30 2.638120e-01 1.177760e-02 NA 2.407283e-01 2.868957e-01
# 10 other 3.361536e-02 3.940503e-03 NA 2.589212e-02 4.133861e-02
# PLOT MULTINOMIAL FIT
# extrapolate = 30
date.from = as.numeric(as.Date("2021-01-01"))
date.to = as.numeric(as.Date("2021-20-14")) # max(GISAID_sel$DATE_NUM)+extrapolate
# multinomial model predictions (fastest, but no confidence intervals)
predgrid = expand.grid(list(DATE_NUM=seq(date.from, date.to)))
fit_australia_multi_preds = data.frame(predgrid, as.data.frame(predict(fit2_australia_multi, newdata=predgrid, type="prob")),check.names=F)
library(tidyr)
library(tidyselect)
fit_australia_multi_preds = gather(fit_australia_multi_preds, LINEAGE, prob, all_of(levels_LINEAGE), factor_key=TRUE)
fit_australia_multi_preds$collection_date = as.Date(fit_australia_multi_preds$DATE_NUM, origin="1970-01-01")
fit_australia_multi_preds$LINEAGE = factor(fit_australia_multi_preds$LINEAGE, levels=levels_LINEAGE)
muller_australia_mfit = ggplot(data=fit_australia_multi_preds,
aes(x=collection_date, y=prob, group=LINEAGE)) +
# facet_wrap(~ STATE) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE, group=LINEAGE), position="stack") +
scale_fill_manual("", values=lineage_cols2) +
annotate("rect", xmin=max(GISAID_sel$DATE_NUM)+1,
xmax=as.Date(date.to, origin="1970-01-01"), ymin=0, ymax=1, alpha=0.4, fill="white") + # extrapolated part
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01"))),1,1),
limits=as.Date(c("2021-01-01",NA)), expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=1, byrow=T), fill = guide_legend(reverse=F, nrow=1, byrow=T)) +
theme_hc() + theme(legend.position="right",
axis.title.x=element_blank()) +
ylab("Share") +
ggtitle("SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN AUSTRALIA\n(GISAID data, multinomial fit)")
muller_australia_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots_multinom fit.png"), width=10, height=6)
# ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots_multinom fit.pdf"), width=10, height=6)
library(ggpubr)
ggarrange(muller_australia_raw2 + coord_cartesian(xlim=c(as.Date("2021-01-01"),as.Date(date.to, origin="1970-01-01")))+
theme(legend.background = element_rect(fill = alpha("white", 0)),
legend.key = element_rect(fill = alpha("white", 0)),
legend.text=element_text(color = "white")) +
guides(colour = guide_legend(override.aes = list(alpha = 0)),
fill = guide_legend(override.aes = list(alpha = 0))),
muller_australia_mfit+ggtitle("Multinomial fit"), ncol=1)
ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots multipanel_multinom fit.png"), width=10, height=10)
# ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_muller plots multipanel_multinom fit.pdf"), width=10, height=10)
# PLOT MODEL FIT WITH DATA & CONFIDENCE INTERVALS
# multinomial model predictions with confidence intervals (but slower)
fit_australia_multi_preds_withCI = data.frame(emmeans(fit2_australia_multi,
~ LINEAGE,
by=c("DATE_NUM"),
at=list(DATE_NUM=seq(date.from, date.to, by=1)), # by=XX to speed up things a bit
mode="prob", df=NA))
fit_australia_multi_preds_withCI$collection_date = as.Date(fit_australia_multi_preds_withCI$DATE_NUM, origin="1970-01-01")
fit_australia_multi_preds_withCI$LINEAGE = factor(fit_australia_multi_preds_withCI$LINEAGE, levels=levels_LINEAGE)
fit_australia_multi_preds2 = fit_australia_multi_preds_withCI
# on logit scale:
ymin = 0.001
ymax = 0.999
fit_australia_multi_preds2$asymp.LCL[fit_australia_multi_preds2$asymp.LCL<ymin] = ymin
fit_australia_multi_preds2$asymp.UCL[fit_australia_multi_preds2$asymp.UCL<ymin] = ymin
fit_australia_multi_preds2$asymp.UCL[fit_australia_multi_preds2$asymp.UCL>ymax] = ymax
fit_australia_multi_preds2$prob[fit_australia_multi_preds2$prob<ymin] = ymin
plot_australia_mfit_logit = qplot(data=fit_australia_multi_preds2, x=collection_date, y=prob, geom="blank") +
# facet_wrap(~ STATE) +
geom_ribbon(aes(y=prob, ymin=asymp.LCL, ymax=asymp.UCL, colour=NULL,
fill=LINEAGE
), alpha=I(0.3)) +
geom_line(aes(y=prob,
colour=LINEAGE
), alpha=I(1)) +
ylab("Share (%)") +
theme_hc() + xlab("") +
ggtitle("SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN AUSTRALIA\n(GISAID data, multinomial fit)") +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01"))),1,1),
limits=as.Date(c("2021-01-01",NA)), expand=c(0,0)) +
scale_y_continuous( trans="logit", breaks=c(10^seq(-5,0),0.5,0.9,0.99,0.999),
labels = c("0.001","0.01","0.1","1","10","100","50","90","99","99.9")) +
scale_fill_manual("variant", values=lineage_cols2) +
scale_colour_manual("variant", values=lineage_cols2) +
geom_point(data=data_agbyweek,
aes(x=collection_date, y=prop, size=total,
colour=LINEAGE
),
alpha=I(1)) +
scale_size_continuous("total number\nsequenced", trans="identity",
range=c(0.1, 4), limits=c(1,max(data_agbyweek$total)), breaks=c(10,100,1000,10000)) +
# guides(fill=FALSE) +
# guides(colour=FALSE) +
theme(legend.position = "right") +
xlab("Collection date")+
coord_cartesian(xlim=c(as.Date("2021-01-01"),as.Date(date.to, origin="1970-01-01")), ylim=c(0.001, 0.9901), expand=c(0,0))
plot_australia_mfit_logit
ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_multinom fit_logit scale.png"), width=10, height=6)
# ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_multinom fit_logit scale.pdf"), width=10, height=6)
# on response scale:
plot_australia_mfit = qplot(data=fit_australia_multi_preds2, x=collection_date, y=100*prob, geom="blank") +
# facet_wrap(~ STATE) +
geom_ribbon(aes(y=100*prob, ymin=100*asymp.LCL, ymax=100*asymp.UCL, colour=NULL,
fill=LINEAGE
), alpha=I(0.3)) +
geom_line(aes(y=100*prob,
colour=LINEAGE
), alpha=I(1)) +
ylab("Share (%)") +
theme_hc() + xlab("") +
ggtitle("SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN AUSTRALIA\n(GISAID data, multinomial fit)") +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01","2021-10-01"))),1,1),
limits=as.Date(c("2021-01-01",NA)), expand=c(0,0)) +
# scale_y_continuous( trans="logit", breaks=c(10^seq(-5,0),0.5,0.9,0.99,0.999),
# labels = c("0.001","0.01","0.1","1","10","100","50","90","99","99.9")) +
coord_cartesian(xlim=as.Date(c("2021-01-01",NA)),
ylim=c(0,100), expand=c(0,0)) +
scale_fill_manual("variant", values=lineage_cols2) +
scale_colour_manual("variant", values=lineage_cols2) +
geom_point(data=data_agbyweek,
aes(x=collection_date, y=100*prop, size=total,
colour=LINEAGE
),
alpha=I(1)) +
scale_size_continuous("total number\nsequenced", trans="identity",
range=c(0.5, 5), limits=c(1,max(data_agbyweek$total)), breaks=c(100,1000,10000)) +
# guides(fill=FALSE) +
# guides(colour=FALSE) +
theme(legend.position = "right") +
xlab("Collection date")
plot_australia_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_multinom fit_response scale.png"), width=10, height=6)
# ggsave(file=paste0(".\\plots\\",plotdir,"\\australia_multinom fit_response scale.pdf"), width=10, height=6)
# PLOTS OF NEW CASES PER DAY BY VARIANT & EFFECTIVE REPRODUCTION NUMBER BY VARIANT THROUGH TIME ####
# load case data
library(covidregionaldata)
library(dplyr)
library(ggplot2)
library(scales)
cases_tot = as.data.frame(get_national_data(countries = "Australia"))
cases_tot = cases_tot[cases_tot$date>=as.Date("2020-08-01"),]
cases_tot$DATE_NUM = as.numeric(cases_tot$date)
# cases_tot$BANKHOLIDAY = bankholiday(cases_tot$date)
cases_tot$WEEKDAY = weekdays(cases_tot$date)
cases_tot = cases_tot[cases_tot$date<=(max(cases_tot$date)-3),] # cut off data from last 3 days (incomplete)
range(cases_tot$date)
# smooth out weekday effects in case nrs using GAM (if testing data is available one could correct for testing intensity as well)
library(mgcv)
k=25
fit_cases = gam(cases_new ~ s(DATE_NUM, bs="cs", k=k, m=c(2), fx=F) +
WEEKDAY, # +
# BANKHOLIDAY,
# s(TESTS_ALL, bs="cs", k=8, fx=F),
family=poisson(log), data=cases_tot,
method = "REML",
knots = list(DATE_NUM = c(min(cases_tot$DATE_NUM)-14,
seq(min(cases_tot$DATE_NUM)+0.7*diff(range(cases_tot$DATE_NUM))/(k-2),
max(cases_tot$DATE_NUM)-0.7*diff(range(cases_tot$DATE_NUM))/(k-2), length.out=k-2),
max(cases_tot$DATE_NUM)+14))
)
BIC(fit_cases)
# STACKED AREA CHART OF NEW CASES BY VARIANT (MULTINOMIAL FIT MAPPED ONTO CASE DATA) ####
fit_australia_multi_preds_withCI$totcases = cases_tot$cases_new[match(round(fit_australia_multi_preds_withCI$DATE_NUM),cases_tot$DATE_NUM)]
fit_australia_multi_preds_withCI$cases = fit_australia_multi_preds_withCI$totcases * fit_australia_multi_preds_withCI$prob
fit_australia_multi_preds_withCI$cases[fit_australia_multi_preds_withCI$cases<=0.001] = NA
cases_emmeans = as.data.frame(emmeans(fit_cases, ~ DATE_NUM, at=list(DATE_NUM=seq(date.from, date.to, by=0.5), BANHOLIDAY="no"), type="response"))
fit_australia_multi_preds_withCI$smoothed_totcases = cases_emmeans$rate[match(fit_australia_multi_preds_withCI$DATE_NUM,cases_emmeans$DATE_NUM)]
fit_australia_multi_preds_withCI$smoothed_cases = fit_australia_multi_preds_withCI$smoothed_totcases * fit_australia_multi_preds_withCI$prob
fit_australia_multi_preds_withCI$smoothed_cases[fit_australia_multi_preds_withCI$smoothed_cases<=0.001] = NA
fit_australia_multi_preds_withCI$LINEAGE = factor(fit_australia_multi_preds_withCI$LINEAGE, levels=levels_LINEAGE)
ggplot(data=fit_australia_multi_preds_withCI[fit_australia_multi_preds_withCI$collection_date>=as.Date("2021-01-01"),],
aes(x=collection_date, y=cases, group=LINEAGE)) +
# facet_wrap(~ REGION, scale="free", ncol=3) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE, group=LINEAGE), position="stack") +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01"))),1,1),
# limits=c(as.Date("2021-03-01"),max(cases_tot$date)),
expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=1, byrow=T), fill = guide_legend(reverse=F, nrow=1, byrow=T)) +
theme_hc() + theme(legend.position="right") +
ylab("New confirmed cases per day") + xlab("Date of diagnosis") +
ggtitle("NEW CONFIRMED SARS-CoV2 CASES PER DAY BY VARIANT\nIN AUSTRALIA\n(case data & multinomial fit to GISAID data)") +
scale_fill_manual("variant", values=lineage_cols2) +
scale_colour_manual("variant", values=lineage_cols2) +
coord_cartesian(xlim=c(as.Date("2021-01-01"),NA))
ggsave(file=paste0(".\\plots\\",plotdir,"\\cases per day_stacked area multinomial fit raw case data.png"), width=8, height=6)
ggplot(data=fit_australia_multi_preds_withCI[fit_australia_multi_preds_withCI$collection_date>=as.Date("2021-01-01")&
fit_australia_multi_preds_withCI$collection_date<=max(cases_tot$date),],
aes(x=collection_date-7, y=smoothed_cases, group=LINEAGE)) +
# facet_wrap(~ REGION, scale="free", ncol=3) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE, group=LINEAGE), position="stack") +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01"))),1,1),
# limits=c(as.Date("2021-03-01"),today),
expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=1, byrow=T), fill = guide_legend(reverse=F, nrow=1, byrow=T)) +
theme_hc() + theme(legend.position="right") +
ylab("New confirmed cases per day (smoothed)") + xlab("Date of infection") +
ggtitle("NEW CONFIRMED SARS-CoV2 CASES PER DAY BY VARIANT\nIN AUSTRALIA\n(case data & multinomial fit to GISAID data)") +
scale_fill_manual("variant", values=lineage_cols2) +
scale_colour_manual("variant", values=lineage_cols2) +
coord_cartesian(xlim=c(as.Date("2021-01-01"),max(cases_tot$date)))
ggsave(file=paste0(".\\plots\\",plotdir,"\\cases per day_smoothed_stacked area multinomial fit case data.png"), width=8, height=6)
# EFFECTIVE REPRODUCTION NUMBER BY VARIANT THROUGH TIME ####
# Function to calculate Re values from intrinsic growth rate
# cf. https://github.com/epiforecasts/EpiNow2/blob/5015e75f7048c2580b2ebe83e46d63124d014861/R/utilities.R#L109
# https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0144
# (assuming gamma distributed gen time)
Re.from.r <- function(r, gamma_mean=4.7, gamma_sd=2.9) { # Nishiura et al. 2020, or use values from Ferretti et al. 2020 (gamma_mean=5.5, gamma_sd=1.8)
k <- (gamma_sd / gamma_mean)^2
R <- (1 + k * r * gamma_mean)^(1 / k)
return(R)
}
# calculate average instantaneous growth rates & 95% CLs using emtrends ####
# based on the slope of the GAM fit on a log link scale
avg_r_cases = as.data.frame(emtrends(fit_cases, ~ DATE_NUM, var="DATE_NUM",
at=list(DATE_NUM=seq(date.from,
date.to)#,
# BANKHOLIDAY="no"
), # weekday="Wednesday",
type="link"))
colnames(avg_r_cases)[2] = "r"
colnames(avg_r_cases)[5] = "r_LOWER"
colnames(avg_r_cases)[6] = "r_UPPER"
avg_r_cases$DATE = as.Date(avg_r_cases$DATE_NUM, origin="1970-01-01") # -7 TO CALCULATE BACK TO INFECTION DATE
avg_r_cases$Re = Re.from.r(avg_r_cases$r)
avg_r_cases$Re_LOWER = Re.from.r(avg_r_cases$r_LOWER)
avg_r_cases$Re_UPPER = Re.from.r(avg_r_cases$r_UPPER)
avg_r_cases = avg_r_cases[complete.cases(avg_r_cases),]
qplot(data=avg_r_cases, x=DATE-7, y=Re, ymin=Re_LOWER, ymax=Re_UPPER, geom="ribbon", alpha=I(0.5), fill=I("steelblue")) +
# facet_wrap(~ REGION) +
geom_line() + theme_hc() + xlab("Date of infection") +
scale_x_continuous(breaks=as.Date(c("2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01")),
labels=c("M","A","M","J","J","A","S","O","N","D","J","F","M","A","M","J","J","A","S")) +
# scale_y_continuous(limits=c(1/2, 2), trans="log2") +
geom_hline(yintercept=1, colour=I("red")) +
ggtitle("Re IN AUSTRALIA AT MOMENT OF INFECTION BASED ON NEW CASES") +
# labs(tag = tag) +
# theme(plot.margin = margin(t = 20, r = 10, b = 20, l = 0)) +
theme(plot.tag.position = "bottomright",
plot.tag = element_text(vjust = 1, hjust = 1, size=8)) # +
# coord_cartesian(xlim=c(as.Date("2020-01-01"),NA))
# calculate above-average intrinsic growth rates per day of each variant over time based on multinomial fit using emtrends weighted effect contrasts ####
# for best model fit3_sanger_multi
above_avg_r_variants0 = do.call(rbind, lapply(seq(date.from,
date.to),
function (d) {
wt = as.data.frame(emmeans(fit2_australia_multi, ~ LINEAGE , at=list(DATE_NUM=d), type="response"))$prob # important: these should sum to 1
# wt = rep(1/length(levels_VARIANTS), length(levels_VARIANTS)) # this would give equal weights, equivalent to emmeans:::eff.emmc(levs=levels_LINEAGE)
cons = lapply(seq_along(wt), function (i) { con = -wt; con[i] = 1 + con[i]; con })
names(cons) = seq_along(cons)
EMT = emtrends(fit2_australia_multi, ~ LINEAGE , by=c("DATE_NUM"),
var="DATE_NUM", mode="latent",
at=list(DATE_NUM=d))
out = as.data.frame(confint(contrast(EMT, cons), adjust="none", df=NA))
# sum(out$estimate*wt) # should sum to zero
return(out) } ))
above_avg_r_variants = above_avg_r_variants0
above_avg_r_variants$contrast = factor(above_avg_r_variants$contrast,
levels=1:length(levels_LINEAGE),
labels=levels_LINEAGE)
above_avg_r_variants$variant = above_avg_r_variants$contrast # gsub(" effect|\\(|\\)","",above_avg_r_variants$contrast)
above_avg_r_variants$collection_date = as.Date(above_avg_r_variants$DATE_NUM, origin="1970-01-01")
range(above_avg_r_variants$collection_date) # "2021-01-04" "2021-07-30"
above_avg_r_variants$avg_r = avg_r_cases$r[match(above_avg_r_variants$collection_date,
avg_r_cases$DATE)] # average growth rate of all lineages calculated from case nrs
above_avg_r_variants$r = above_avg_r_variants$avg_r+above_avg_r_variants$estimate
above_avg_r_variants$r_LOWER = above_avg_r_variants$avg_r+above_avg_r_variants$asymp.LCL
above_avg_r_variants$r_UPPER = above_avg_r_variants$avg_r+above_avg_r_variants$asymp.UCL
above_avg_r_variants$Re = Re.from.r(above_avg_r_variants$r)
above_avg_r_variants$Re_LOWER = Re.from.r(above_avg_r_variants$r_LOWER)
above_avg_r_variants$Re_UPPER = Re.from.r(above_avg_r_variants$r_UPPER)
df = data.frame(contrast=NA,
DATE_NUM=avg_r_cases$DATE_NUM, # -7 to calculate back to time of infection
# REGION=avg_r_cases$REGION,
estimate=NA,
SE=NA,
df=NA,
asymp.LCL=NA,
asymp.UCL=NA,
# p.value=NA,
collection_date=avg_r_cases$DATE,
variant="avg",
avg_r=avg_r_cases$r,
r=avg_r_cases$r,
r_LOWER=avg_r_cases$r_LOWER,
r_UPPER=avg_r_cases$r_UPPER,
Re=avg_r_cases$Re,
Re_LOWER=avg_r_cases$Re_LOWER,
Re_UPPER=avg_r_cases$Re_UPPER)
# df = df[df$DATE_NUM<=max(above_avg_r_variants$DATE_NUM)&df$DATE_NUM>=(min(above_avg_r_variants$DATE_NUM)+7),]
above_avg_r_variants = rbind(above_avg_r_variants, df)
above_avg_r_variants$variant = factor(above_avg_r_variants$variant, levels=c(levels_LINEAGE,"avg"))
above_avg_r_variants$prob = fit_australia_multi_preds_withCI$prob[match(interaction(above_avg_r_variants$DATE_NUM,
above_avg_r_variants$variant),
interaction(fit_australia_multi_preds_withCI$DATE_NUM,
fit_australia_multi_preds_withCI$LINEAGE))]
above_avg_r_variants2 = above_avg_r_variants
ymax = 3
ymin = 1/3
above_avg_r_variants2$Re[above_avg_r_variants2$Re>=ymax] = NA
above_avg_r_variants2$Re[above_avg_r_variants2$Re<=ymin] = NA
above_avg_r_variants2$Re_LOWER[above_avg_r_variants2$Re_LOWER>=ymax] = ymax
above_avg_r_variants2$Re_LOWER[above_avg_r_variants2$Re_LOWER<=ymin] = ymin
above_avg_r_variants2$Re_UPPER[above_avg_r_variants2$Re_UPPER>=ymax] = ymax
above_avg_r_variants2$Re_UPPER[above_avg_r_variants2$Re_UPPER<=ymin] = ymin
above_avg_r_variants2$Re[above_avg_r_variants2$prob<0.01] = NA
above_avg_r_variants2$Re_LOWER[above_avg_r_variants2$prob<0.01] = NA
above_avg_r_variants2$Re_UPPER[above_avg_r_variants2$prob<0.01] = NA
qplot(data=above_avg_r_variants2[!((above_avg_r_variants2$variant %in% c("other"))|above_avg_r_variants2$collection_date>max(cases_tot$DATE)),],
x=collection_date-7, # -7 to calculate back to date of infection
y=Re, ymin=Re_LOWER, ymax=Re_UPPER, geom="ribbon", colour=variant, fill=variant, alpha=I(0.5),
group=variant, linetype=I(0)) +
# facet_wrap(~ REGION) +
# geom_ribbon(aes(fill=variant, colour=variant), alpha=I(0.5))
geom_line(aes(colour=variant), lwd=I(0.72)) + theme_hc() + xlab("Date of infection") +
scale_x_continuous(breaks=as.Date(c("2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01","2021-08-01","2021-09-01")),
labels=c("M","A","M","J","J","A","S","O","N","D","J","F","M","A","M","J","J","A","S")) +
# scale_y_continuous(limits=c(1/ymax,ymax), trans="log2") +
geom_hline(yintercept=1, colour=I("red")) +
ggtitle("Re VALUES OF SARS-CoV2 VARIANTS IN AUSTRALIA\nAT MOMENT OF INFECTION\n(based on case data & multinomial fit to GISAID data)") +
# labs(tag = tag) +
# theme(plot.margin = margin(t = 20, r = 10, b = 20, l = 0)) +
theme(plot.tag.position = "bottomright",
plot.tag = element_text(vjust = 1, hjust = 1, size=8)) +
coord_cartesian(xlim=c(as.Date("2021-01-01"),max(cases_tot$date))) +
scale_fill_manual("variant", values=c(head(lineage_cols2,-1),"black")) +
scale_colour_manual("variant", values=c(head(lineage_cols2,-1),"black")) +
theme(legend.position="right")
ggsave(file=paste0(".\\plots\\",plotdir,"\\Re values per variant_avgRe_from_cases_with clipping.png"), width=8, height=6)