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analysis_VOCs_GISAID genomic epidemiology metadata_Netherlands.R
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# ANALYSIS OF GROWTH ADVANTAGE OF DIFFERENT VOCs IN SELECTED AFRICAN COUNTRIES (GISAID METADATA+GENOMIC EPIDEMIOLOGY METADATA)
# T. Wenseleers
# last update 23 JUNE 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)
today = as.Date(Sys.time()) # we use the file date version as our definition of "today"
today = as.Date("2021-06-23")
today_num = as.numeric(today)
today # "2021-06-23"
plotdir = "VOCs_GISAID"
suppressWarnings(dir.create(paste0(".//plots//",plotdir)))
# import GISAID metadata
GISAID = read_tsv(gzfile(".//data//GISAID_genomic_epidemiology//metadata_2021-06-21_05-59.tsv.gz"), col_types = cols(.default = "c"))
# GISAID = read_tsv(gzfile(".//data//GISAID//metadata.tsv"), col_types = cols(.default = "c")) # using metadata_tsv_2021_06_18.tar.xz
GISAID = as.data.frame(GISAID)
colnames(GISAID)
# [1] "Virus name" "Type" "Accession ID" "Collection date"
# [5] "Location" "Additional location information" "Sequence length" "Host"
# [9] "Patient age" "Gender" "Clade" "Pango lineage"
# [13] "Pangolin version" "Variant" "AA Substitutions" "Submission date"
# [17] "Is reference?" "Is complete?" "Is high coverage?" "Is low coverage?"
# [21] "N-Content" "GC-Content"
## SELECTED COUNTRIES
# sel_countries = c("Democratic Republic of the Congo","Uganda","Malawi","Kenya") # "Angola","Senegal" "Botswana"
# GISAID = GISAID[grepl(paste0(sel_countries,collapse="|"), GISAID[,"Location"]),]
# nrow(GISAID) # 1956
# library(stringr)
# date_isvalid = sapply(GISAID[,"Collection date"], function (s) str_count(s, pattern = "-")==2)
# sum(date_isvalid) # 1914
GISAID$date = as.Date(GISAID$date) # as.Date(GISAID[,"Collection date"])
GISAID = GISAID[!is.na(GISAID$date),]
# GISAID$host = GISAID$Host
unique(GISAID$host)
# [1] "Human" "Environment" "Feline" "unknown"
# [5] "Rhinolophus shameli" "Rhinolophus malayanus" "Rhinolophus pusillus" "Rhinolophus sinicus"
# [9] "Rhinolophus stheno" "Rhinolophus affinis" "Felis catus" "Canis lupus familiaris"
# [13] "Gorilla gorilla gorilla" "Mesocricetus auratus" "Prionailurus bengalensis euptilurus" "Panthera leo"
# [17] "Mink" "Mustela putorius furo" "Chlorocebus sabaeus" "Mus musculus"
# [21] "Mus musculus (BALB/c mice)" "Manis javanica" "Manis pentadactyla" "Panthera tigris jacksoni"
GISAID[GISAID$host!="Human","strain"]
GISAID = GISAID[GISAID$host=="Human",]
GISAID = GISAID[GISAID$date>=as.Date("2020-01-01"),]
range(GISAID$date) # "2020-01-01" "2021-06-16"
firstdetB16172 = GISAID[GISAID$pango_lineage=="B.1.617.2",]
firstdetB16172 = firstdetB16172[!is.na(firstdetB16172$date),]
firstdetB16172 = firstdetB16172[firstdetB16172$date==min(firstdetB16172$date),]
firstdetB16172 # 7 sept 63r old male from Madhya Pradesh
# GISAID = GISAID[grepl("2021-", GISAID$date),]
sum(is.na(GISAID$purpose_of_sequencing)) == nrow(GISAID) # field purpose_of_sequencing left blank unfortunately
nrow(GISAID) # 1901769
GISAID$Week = lubridate::week(GISAID$date)
GISAID$Year = lubridate::year(GISAID$date)
GISAID$Year_Week = interaction(GISAID$Year,GISAID$Week)
library(lubridate)
GISAID$floor_date = as.Date(as.character(cut(GISAID$date, "week")))+3.5 # week midpoint date
GISAID$DATE_NUM = as.numeric(GISAID$date)
colnames(GISAID)
unique(GISAID$region)
# "Asia" "Europe" "Africa" "South America" "Oceania" "North America"
unique(GISAID$country)
unique(GISAID$division) # = city or province or region, sometimes just country
unique(GISAID$location) # = city
length(unique(GISAID$country[grepl("B.1.617",GISAID$pango_lineage,fixed=T)])) # B.1.617+ now found in 67 countries
table(GISAID$pango_lineage[grepl("B.1.617",GISAID$pango_lineage,fixed=T)])
# B.1.617 B.1.617.1 B.1.617.2 B.1.617.3
# 2 4388 51068 147
GISAID$pango_lineage[grepl("B.1.177",GISAID$pango_lineage,fixed=T)] = "B.1.177+"
GISAID$pango_lineage[grepl("B.1.36\\>",GISAID$pango_lineage)] = "B.1.36+"
sel_target_VOC = "B.1.617"
GISAID$LINEAGE1 = GISAID$pango_lineage
GISAID$LINEAGE2 = GISAID$pango_lineage
GISAID[grepl(sel_target_VOC, GISAID$LINEAGE1, fixed=TRUE),"LINEAGE1"] = paste0(sel_target_VOC,"+") # in LINEAGE1 we recode B.1.617.1,2&3 all as B.1.617+
table_country_lineage = as.data.frame(table(GISAID$country, GISAID$LINEAGE1))
colnames(table_country_lineage) = c("Country","Lineage","Count")
tblB1617 = table_country_lineage[grepl(sel_target_VOC, table_country_lineage$Lineage, fixed=T)&table_country_lineage$Count>10,]
tblB1617
# Country Lineage Count
# 170497 Australia B.1.617+ 252
# 170501 Bahrain B.1.617+ 24
# 170502 Bangladesh B.1.617+ 44
# 170505 Belgium B.1.617+ 247
# 170513 Brazil B.1.617+ 16
# 170519 Spain B.1.617+ 346
# 170530 Czech Republic B.1.617+ 17
# 170532 Denmark B.1.617+ 121
# 170542 Finland B.1.617+ 19
# 170543 France B.1.617+ 141
# 170548 Germany B.1.617+ 836
# 170562 India B.1.617+ 7435
# 170563 Indonesia B.1.617+ 75
# 170564 Iran B.1.617+ 11
# 170566 Ireland B.1.617+ 299
# 170567 Israel B.1.617+ 63
# 170568 Italy B.1.617+ 184
# 170570 Japan B.1.617+ 170
# 170581 Luxembourg B.1.617+ 58
# 170583 Malawi B.1.617+ 26
# 170584 Malaysia B.1.617+ 12
# 170588 Mexico B.1.617+ 48
# 170596 Nepal B.1.617+ 34
# 170597 Netherlands B.1.617+ 85
# 170598 New Zealand B.1.617+ 17
# 170602 Norway B.1.617+ 69
# 170611 Poland B.1.617+ 71
# 170612 Portugal B.1.617+ 126
# 170613 Qatar B.1.617+ 23
# 170615 Romania B.1.617+ 19
# 170616 Russia B.1.617+ 278
# 170627 Singapore B.1.617+ 762
# 170632 South Africa B.1.617+ 21
# 170633 South Korea B.1.617+ 32
# 170635 Spain B.1.617+ 264
# 170638 Sweden B.1.617+ 42
# 170639 Switzerland B.1.617+ 113
# 170641 Thailand B.1.617+ 94
# 170651 United Kingdom B.1.617+ 40092
# 170653 USA B.1.617+ 2859
# 170656 Vietnam B.1.617+ 54
sel_countries_target = unique(as.character(table_country_lineage[grepl(sel_target_VOC, table_country_lineage$Lineage)&table_country_lineage$Count>100,]$Country))
sel_countries_target
# [1] "Australia" "Belgium" "Spain" "Denmark" "France" "Germany" "India" "Ireland"
# [9] "Italy" "Japan" "Portugal" "Russia" "Singapore" "Spain" "Switzerland" "United Kingdom"
# [17] "USA"
sel_ref_lineage = "B.1.1.7"
sel_countries_ref = as.character(table_country_lineage[table_country_lineage$Lineage==sel_ref_lineage&table_country_lineage$Count>10&table_country_lineage$Country %in% sel_countries_target,]$Country)
sel_countries_ref
# [1] "Australia" "Belgium" "Spain" "Denmark" "France" "Germany" "India" "Ireland"
# [9] "Italy" "Japan" "Portugal" "Russia" "Singapore" "Spain" "Switzerland" "United Kingdom"
# [17] "USA"
sel_countries = intersect(sel_countries_target, sel_countries_ref)
sel_countries
# [1] "Australia" "Belgium" "Spain" "Denmark" "France" "Germany" "India" "Ireland"
# [9] "Italy" "Japan" "Portugal" "Russia" "Singapore" "Spain" "Switzerland" "United Kingdom"
# [17] "USA"
# sel_countries = sel_countries[!(sel_countries %in% c("Japan","USA"))] # Japan is almost only import & for USA we do separate analysis by state
# ANALYSIS OF VOCs IN SELECTED AFRICAN COUNTRIES ####
sel_countries = c("Netherlands") # "Democratic Republic of the Congo","Uganda","Malawi"," "Angola","Senegal" "Botswana"
levels_countries = sel_countries
tblB117 = table_country_lineage[table_country_lineage$Lineage==sel_ref_lineage&table_country_lineage$Count>10&table_country_lineage$Country %in% sel_countries,]
tblB117
GISAID_sel = GISAID[GISAID$country %in% sel_countries,]
nrow(GISAID_sel) # 38216
unique(GISAID_sel$country)
rowSums(table(GISAID_sel$LINEAGE1,GISAID_sel$country))
# GISAID_sel = GISAID_sel[GISAID_sel$country_exposure=="India"&GISAID_sel$country!="India",]
# nrow(GISAID_sel[is.na(GISAID_sel$LINEAGE1),]) # 0 unknown pango clade
GISAID_sel = GISAID_sel[!is.na(GISAID_sel$LINEAGE1),]
nrow(GISAID_sel) # 2619
GISAID_sel = GISAID_sel[GISAID_sel$country==GISAID_sel$country_exposure,] # we remove travel-related cases
nrow(GISAID_sel) # 2619
sum(GISAID_sel$LINEAGE1=="B.1.617+") # 81
unique(GISAID_sel$country[GISAID_sel$LINEAGE1=="B.1.1.7"])
sum(GISAID_sel$LINEAGE1=="B.1.1.7") # 392
sum(GISAID_sel$LINEAGE1=="B.1.1.519") # 0
sum(GISAID_sel$LINEAGE1=="B.1.351") # 477
table(GISAID_sel$LINEAGE1)
table(GISAID_sel$LINEAGE2)
main_lineages = names(table(GISAID_sel$LINEAGE1))[100*table(GISAID_sel$LINEAGE1)/sum(table(GISAID_sel$LINEAGE1)) > 3]
main_lineages
# "A.23.1" "B.1" "B.1.1" "B.1.1.7" "B.1.351" "B.1.416" "C.16"
VOCs = c("B.1.617.1","B.1.617.2","B.1.617+","B.1.618","B.1.1.7","B.1.351","P.1","B.1.1.318","B.1.1.207","B.1.429",
"B.1.525","B.1.526","B.1.1.519")
main_lineages = union(main_lineages, VOCs)
GISAID_sel$LINEAGE1[!(GISAID_sel$LINEAGE1 %in% main_lineages)] = "other" # minority lineages & non-VOCs
GISAID_sel$LINEAGE2[!(GISAID_sel$LINEAGE2 %in% main_lineages)] = "other" # minority lineages & non-VOCs
remove1 = names(table(GISAID_sel$LINEAGE1))[table(GISAID_sel$LINEAGE1)/sum(table(GISAID_sel$LINEAGE1)) < 0.01]
remove1 = remove1[!(remove1 %in% c("B.1.351","B.1.1.7","P.1","B.1.617+","B.1.1.519"))]
remove2 = names(table(GISAID_sel$LINEAGE2))[table(GISAID_sel$LINEAGE2)/sum(table(GISAID_sel$LINEAGE2)) < 0.01]
remove2 = remove2[!(remove2 %in% c("B.1.351","B.1.1.7","P.1","B.1.617.2","B.1.617.1","B.1.1.519"))]
GISAID_sel$LINEAGE1[(GISAID_sel$LINEAGE1 %in% remove1)] = "other" # minority VOCs
GISAID_sel$LINEAGE2[(GISAID_sel$LINEAGE2 %in% remove2)] = "other" # minority VOCs
table(GISAID_sel$LINEAGE1)
GISAID_sel$LINEAGE1 = factor(GISAID_sel$LINEAGE1)
GISAID_sel$LINEAGE1 = relevel(GISAID_sel$LINEAGE1, ref="B.1.1.7") # we code UK strain as the reference level
levels(GISAID_sel$LINEAGE1)
levels_LINEAGE1 = c("B.1.1.7",levels(GISAID_sel$LINEAGE1)[!levels(GISAID_sel$LINEAGE1) %in% c("B.1.1.7","B.1.617+","B.1.617.1","B.1.617.2","other")],
"B.1.617+","other")
GISAID_sel$LINEAGE1 = factor(GISAID_sel$LINEAGE1, levels=levels_LINEAGE1)
GISAID_sel$LINEAGE2 = factor(GISAID_sel$LINEAGE2)
GISAID_sel$LINEAGE2 = relevel(GISAID_sel$LINEAGE2, ref="B.1.1.7") # we code UK strain as the reference level
levels(GISAID_sel$LINEAGE2)
# "B.1.1.7" "B.1" "B.1.1" "B.1.160" "B.1.177+" "B.1.351" "B.1.617.1" "B.1.617.2" "B.1.91" "other" "P.1"
levels_LINEAGE2 = c("B.1.1.7",levels(GISAID_sel$LINEAGE2)[!levels(GISAID_sel$LINEAGE2) %in% c("B.1.1.7","B.1.617+","B.1.617.1","B.1.617.2","other")],
"B.1.617.1","B.1.617.2","other")
GISAID_sel$LINEAGE2 = factor(GISAID_sel$LINEAGE2, levels=levels_LINEAGE2)
# GISAID_sel = GISAID_sel[GISAID_sel$division!="India",]
table(GISAID_sel$country)
GISAID_sel$country = factor(GISAID_sel$country, levels=levels_countries)
# B.1.617+ cases before Apr 14 are likely mostly imported cases, so we remove those
# GISAID_sel = GISAID_sel[-which(grepl("B.1.617", GISAID_sel$pango_lineage, fixed=TRUE)&GISAID_sel$date<=as.Date("2021-04-14")),]
table(GISAID_sel$LINEAGE2)
range(GISAID_sel$date) # "2020-01-24" "2021-06-08"
GISAID_sel = GISAID_sel[GISAID_sel$date>="2020-11-01",]
# aggregated data to make Muller plots of raw data
# aggregated by week for selected variant lineages
data_agbyweek2 = as.data.frame(table(GISAID_sel$floor_date, GISAID_sel$LINEAGE2))
colnames(data_agbyweek2) = c("floor_date", "LINEAGE2", "count")
data_agbyweek2_sum = aggregate(count ~ floor_date, data=data_agbyweek2, sum)
data_agbyweek2$total = data_agbyweek2_sum$count[match(data_agbyweek2$floor_date, data_agbyweek2_sum$floor_date)]
sum(data_agbyweek2[data_agbyweek2$LINEAGE2=="B.1.617.1","total"]) == nrow(GISAID_sel) # correct
data_agbyweek2$collection_date = as.Date(as.character(data_agbyweek2$floor_date))
data_agbyweek2$LINEAGE2 = factor(data_agbyweek2$LINEAGE2, levels=levels_LINEAGE2)
data_agbyweek2$collection_date_num = as.numeric(data_agbyweek2$collection_date)
data_agbyweek2$prop = data_agbyweek2$count/data_agbyweek2$total
data_agbyweek2$floor_date = NULL
# aggregated by week & country for selected variant lineages
data_agbyweek_bycountry2 = as.data.frame(table(GISAID_sel$floor_date, GISAID_sel$country, GISAID_sel$LINEAGE2))
colnames(data_agbyweek_bycountry2) = c("floor_date", "country", "LINEAGE2", "count")
data_agbyweek_bycountry2_sum = aggregate(count ~ floor_date+country, data=data_agbyweek_bycountry2, sum)
data_agbyweek_bycountry2$total = data_agbyweek_bycountry2_sum$count[match(interaction(data_agbyweek_bycountry2$floor_date,data_agbyweek_bycountry2$country),
interaction(data_agbyweek_bycountry2_sum$floor_date,data_agbyweek_bycountry2_sum$country))]
sum(data_agbyweek_bycountry2[data_agbyweek_bycountry2$LINEAGE2=="B.1.617.1","total"]) == nrow(GISAID_sel) # correct
data_agbyweek_bycountry2$collection_date = as.Date(as.character(data_agbyweek_bycountry2$floor_date))
data_agbyweek_bycountry2$LINEAGE2 = factor(data_agbyweek_bycountry2$LINEAGE2, levels=levels_LINEAGE2)
data_agbyweek_bycountry2$collection_date_num = as.numeric(data_agbyweek_bycountry2$collection_date)
data_agbyweek_bycountry2$prop = data_agbyweek_bycountry2$count/data_agbyweek_bycountry2$total
data_agbyweek_bycountry2$floor_date = NULL
data_agbyweek_bycountry2$country = factor(data_agbyweek_bycountry2$country, levels=levels_countries)
# MULLER PLOT (RAW DATA)
library(scales)
n1 = length(levels(GISAID_sel$LINEAGE1))
lineage_cols1 = hcl(h = seq(15, 320, length = n1), l = 65, c = 200)
lineage_cols1[which(levels(GISAID_sel$LINEAGE1)=="B.1.617+")] = "magenta"
lineage_cols1[which(levels(GISAID_sel$LINEAGE1)=="other")] = "grey75"
n2 = length(levels(GISAID_sel$LINEAGE2))
lineage_cols2 = hcl(h = seq(15, 320, length = n2), l = 65, c = 200)
lineage_cols2[which(levels(GISAID_sel$LINEAGE2)=="B.1.617.1")] = muted("magenta")
lineage_cols2[which(levels(GISAID_sel$LINEAGE2)=="B.1.617.2")] = "magenta"
lineage_cols2[which(levels(GISAID_sel$LINEAGE2)=="other")] = "grey75"
# muller plot, overall
muller_netherlands_raw2 = ggplot(data=data_agbyweek2, aes(x=collection_date, y=count, group=LINEAGE2)) +
# facet_wrap(~ STATE, ncol=1) +
# geom_col(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2), width=1, position="fill") +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2, group=LINEAGE2), position="fill") +
scale_fill_manual("", values=lineage_cols2) +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01"))),1,1),
limits=as.Date(c("2020-11-01",NA)), expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=2, byrow=T), fill = guide_legend(reverse=F, nrow=2, byrow=T)) +
theme_hc() +
# labs(title = "MAIN SARS-CoV2 VARIANT LINEAGES IN THE UK") +
ylab("Share") +
theme(legend.position="right",
axis.title.x=element_blank()) +
labs(title = "SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN NETHERLANDS\n(GISAID data)")
# +
# coord_cartesian(xlim=c(1,max(GISAID_sel$Week)))
muller_netherlands_raw2
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plots_raw data.png"), width=8, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plots_raw data.pdf"), width=8, height=6)
# multinomial fits
data_agbyweek2$LINEAGE2 = relevel(data_agbyweek2$LINEAGE2, ref="B.1.1.7")
data_agbyweek2$DATE_NUM = as.numeric(data_agbyweek2$collection_date)
library(nnet)
library(splines)
set.seed(1)
fit1_netherlands_multi = nnet::multinom(LINEAGE2 ~ scale(DATE_NUM), weights=count, data=data_agbyweek2, maxit=1000)
fit2_netherlands_multi = nnet::multinom(LINEAGE2 ~ ns(DATE_NUM, df=2), weights=count, data=data_agbyweek2, maxit=1000)
BIC(fit1_netherlands_multi, fit2_netherlands_multi)
# df BIC
# fit1_netherlands_multi 16 47798.80
# fit2_netherlands_multi 24 47557.48
# growth rate advantage compared to UK type B.1.1.7 (difference in growth rate per day)
emtrnetherlands = emtrends(fit1_netherlands_multi, trt.vs.ctrl ~ LINEAGE2,
var="DATE_NUM", mode="latent",
at=list(DATE_NUM=max(GISAID_sel$DATE_NUM)))
delta_r_netherlands = data.frame(confint(emtrnetherlands,
adjust="none", df=NA)$contrasts,
p.value=as.data.frame(emtrnetherlands$contrasts)$p.value)
delta_r_netherlands
# contrast estimate SE df asymp.LCL asymp.UCL p.value
# 1 B.1.1.519 - B.1.1.7 -0.036041027 0.0044327258 NA -0.044729010 -0.02735304 3.270422e-06
# 2 (B.1.177+) - B.1.1.7 -0.062294381 0.0008005285 NA -0.063863388 -0.06072537 1.976197e-14
# 3 B.1.221 - B.1.1.7 -0.069617194 0.0009588780 NA -0.071496560 -0.06773783 1.976197e-14
# 4 B.1.351 - B.1.1.7 -0.021122758 0.0011159209 NA -0.023309922 -0.01893559 1.642919e-11
# 5 P.1 - B.1.1.7 0.009092908 0.0014664763 NA 0.006218667 0.01196715 9.032013e-05
# 6 B.1.617.1 - B.1.1.7 0.019809350 0.0106787833 NA -0.001120681 0.04073938 3.560908e-01
# 7 B.1.617.2 - B.1.1.7 0.061661661 0.0052721386 NA 0.051328459 0.07199486 2.209800e-08
# 8 other - B.1.1.7 -0.062501631 0.0008983339 NA -0.064262333 -0.06074093 1.976197e-14
exp(delta_r_netherlands$estimate*4.7)
# delta 34% more transmissible
# fitted prop of different LINEAGES in the netherlands today
multinom_preds_today_avg = data.frame(emmeans(fit1_netherlands_multi, ~ LINEAGE2|1,
at=list(DATE_NUM=today_num),
mode="prob", df=NA))
multinom_preds_today_avg
# LINEAGE2 prob SE df asymp.LCL asymp.UCL
# 1 B.1.1.7 8.407361e-01 2.322188e-02 NA 7.952221e-01 8.862501e-01
# 2 B.1.1.519 3.707140e-05 2.104123e-05 NA -4.168655e-06 7.831145e-05
# 3 B.1.177+ 9.354752e-05 1.033094e-05 NA 7.329926e-05 1.137958e-04
# 4 B.1.221 1.535608e-05 2.173373e-06 NA 1.109635e-05 1.961581e-05
# 5 B.1.351 3.310576e-03 4.117276e-04 NA 2.503604e-03 4.117547e-03
# 6 P.1 3.184633e-02 3.530607e-03 NA 2.492646e-02 3.876619e-02
# 7 B.1.617.1 1.449225e-03 1.017565e-03 NA -5.451665e-04 3.443617e-03
# 8 B.1.617.2 1.224616e-01 2.396560e-02 NA 7.548989e-02 1.694333e-01
# 9 other 5.019055e-05 6.434885e-06 NA 3.757840e-05 6.280269e-05
# % non-B.1.1.7
colSums(multinom_preds_today_avg[-1, c("prob","asymp.LCL","asymp.UCL")])
# prob asymp.LCL asymp.UCL
# 0.1592639 0.1024926 0.2160352
# PLOT MULTINOMIAL FIT
# extrapolate = 30
date.from = as.numeric(as.Date("2020-11-01"))
date.to = as.numeric(as.Date("2021-07-31")) # max(GISAID_sel$DATE_NUM)+extrapolate
# multinomial model predictions
fit_netherlands_multi_preds_withCI = data.frame(emmeans(fit1_netherlands_multi,
~ LINEAGE2,
by=c("DATE_NUM"),
at=list(DATE_NUM=seq(date.from, date.to, by=7)), # by=7 to speed up things a bit
mode="prob", df=NA))
fit_netherlands_multi_preds_withCI$collection_date = as.Date(fit_netherlands_multi_preds_withCI$DATE_NUM, origin="1970-01-01")
fit_netherlands_multi_preds_withCI$LINEAGE2 = factor(fit_netherlands_multi_preds_withCI$LINEAGE2, levels=levels_LINEAGE2)
muller_netherlands_mfit = ggplot(data=fit_netherlands_multi_preds_withCI,
aes(x=collection_date, y=prob, group=LINEAGE2)) +
# facet_wrap(~ country) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2, group=LINEAGE2), 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")),
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"))),1,1),
limits=as.Date(c("2020-11-01",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 NETHERLANDS\n(GISAID data, multinomial fit)")
muller_netherlands_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plot_multinom fit avg.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plot_multinom fit avg.pdf"), width=10, height=6)
library(ggpubr)
ggarrange(muller_netherlands_raw2 + coord_cartesian(xlim=c(as.Date("2020-11-01"),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_netherlands_mfit+ggtitle("Multinomial fit"), ncol=1)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plot multipanel_multinom fit avg.png"), width=10, height=10)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_muller plot multipanel_multinom fit avg.pdf"), width=10, height=10)
# PLOT MODEL FIT WITH DATA & CONFIDENCE INTERVALS
# overall average multinomial model predictions over all selected countries with confidence intervals
fit_netherlands_multi_preds_withCI[fit_netherlands_multi_preds_withCI$collection_date==(as.Date("2021-06-22")-2)&fit_netherlands_multi_preds_withCI$LINEAGE2=="B.1.617.2",]
# LINEAGE2 DATE_NUM prob SE df asymp.LCL asymp.UCL collection_date
# B.1.617.2 18798 0.6641877 0.1072788 NA 0.453925 0.8744504 2021-06-20
# fit_netherlands_multi_preds2 = fit_netherlands_multi_preds_bystate # without CIs
# fit_netherlands_multi_preds2$asymp.LCL = NA
# fit_netherlands_multi_preds2$asymp.UCL = NA
# on logit scale:
fit_netherlands_multi_preds2 = fit_netherlands_multi_preds_withCI
ymin = 0.001
ymax = 0.999
fit_netherlands_multi_preds2$asymp.LCL[fit_netherlands_multi_preds2$asymp.LCL<ymin] = ymin
fit_netherlands_multi_preds2$asymp.UCL[fit_netherlands_multi_preds2$asymp.UCL<ymin] = ymin
fit_netherlands_multi_preds2$asymp.UCL[fit_netherlands_multi_preds2$asymp.UCL>ymax] = ymax
fit_netherlands_multi_preds2$prob[fit_netherlands_multi_preds2$prob<ymin] = ymin
plot_netherlands_mfit_logit = qplot(data=fit_netherlands_multi_preds2, x=collection_date, y=prob, geom="blank") +
# facet_wrap(~ country) +
geom_ribbon(aes(y=prob, ymin=asymp.LCL, ymax=asymp.UCL, colour=NULL,
fill=LINEAGE2
), alpha=I(0.3)) +
geom_line(aes(y=prob,
colour=LINEAGE2
), alpha=I(1)) +
ylab("Share (%)") +
theme_hc() + xlab("") +
ggtitle("SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN NETHERLANDS\n(GISAID data, multinomial fit)") +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01","2021-07-01"))),1,1),
limits=as.Date(c("2020-11-01",NA)), expand=c(0,0)) +
scale_y_continuous( trans="logit", breaks=c(10^seq(-5,0),0.5,0.9,0.99,0.999),
labels = c("0.001","0.01","0.1","1","10","100","50","90","99","99.9")) +
scale_fill_manual("variant", values=lineage_cols2) +
scale_colour_manual("variant", values=lineage_cols2) +
geom_point(data=data_agbyweek2,
aes(x=collection_date, y=prop, size=total,
colour=LINEAGE2
),
alpha=I(1)) +
scale_size_continuous("total number\nsequenced", trans="sqrt",
range=c(0.5/2, 3/2), limits=c(1,max(data_agbyweek2$total)), breaks=c(100,1000,10000)) +
# guides(fill=FALSE) +
# guides(colour=FALSE) +
theme(legend.position = "right") +
xlab("Collection date")+
coord_cartesian(xlim=c(as.Date("2020-11-01"),as.Date(date.to, origin="1970-01-01")), ylim=c(0.001, 0.9901), expand=c(0,0))
plot_netherlands_mfit_logit
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_multinom fit avg_logit scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_multinom fit avg_logit scale.pdf"), width=10, height=6)
# on response scale:
plot_netherlands_mfit = qplot(data=fit_netherlands_multi_preds2, x=collection_date, y=100*prob, geom="blank") +
# facet_wrap(~ country) +
geom_ribbon(aes(y=100*prob, ymin=100*asymp.LCL, ymax=100*asymp.UCL, colour=NULL,
fill=LINEAGE2
), alpha=I(0.3)) +
geom_line(aes(y=100*prob,
colour=LINEAGE2
), alpha=I(1)) +
ylab("Share (%)") +
theme_hc() + xlab("") +
ggtitle("SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN NETHERLANDS\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")),
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"))),1,1),
limits=as.Date(c("2020-11-01",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("2020-11-01",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_agbyweek2,
aes(x=collection_date, y=100*prop, size=total,
colour=LINEAGE2
),
alpha=I(1)) +
scale_size_continuous("total number\nsequenced", trans="sqrt",
range=c(0.5/2, 3/2), limits=c(1,max(data_agbyweek2$total)), breaks=c(100,1000,10000)) +
# guides(fill=FALSE) +
# guides(colour=FALSE) +
theme(legend.position = "right") +
xlab("Collection date")
plot_netherlands_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_multinom fit avg_response scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\netherlands_multinom fit avg_response scale.pdf"), width=10, height=6)
# project multinomial fit onto cases ####
# TO DO: still need to finish this part
us_cases_by_state = read.csv("https://github.com/nytimes/covid-19-data/raw/master/us-states.csv")
us_data_by_state$date = as.Date(us_data_by_state$date)
us_data_by_state$state = factor(us_data_by_state$state,
levels=c("Washington","Illinois","California",
"Arizona","Massachusetts","Wisconsin",
"Texas","Nebraska","Utah","Oregon",
"Florida","New York","Rhode Island",
"Georgia","New Hampshire","North Carolina",
"New Jersey","Colorado","Maryland","Nevada",
"Tennessee","Hawaii","Indiana","Kentucky","Minnesota",
"Oklahoma","Pennsylvania","South Carolina","District of Columbia",
"Kansas","Missouri","Vermont","Virginia","Connecticut",
"Iowa","Louisiana","Ohio","Michigan","South Dakota",
"Arkansas","Delaware","Mississippi","New Mexico","North Dakota",
"Wyoming","Alaska","Maine","Alabama","Idaho","Montana",
"Puerto Rico","Virgin Islands","Guam","West Virginia","Northern Mariana Islands"))
data_florida = us_data_by_state[us_data_by_state$state=="Florida",]
data_florida$newcases = c(0,diff(data_florida$cases))
data_florida$newcases[data_florida$newcases<0] = 0
cases_india_bystate$Date = as.Date(cases_india_bystate$Date)
cases_india_bystate = cases_india_bystate[cases_india_bystate$Date >= as.Date("2020-06-01"),]
head(cases_india_bystate)
levels_STATES
# [1] "Maharashtra" "Chhattisgarh" "Gujarat" "Delhi" "Andhra Pradesh" "Telangana" "Karnataka" "West Bengal" "Odisha"
# [10] "Jharkhand"
cases_india_bystate = do.call(rbind,lapply(unique(cases_india_bystate$State), function (state) { df = cases_india_bystate[cases_india_bystate$State==state,]
df$newcases = c(NA, diff(df$Confirmed))
return(df)
} ))
cases_india_bystate = cases_india_bystate[cases_india_bystate$State!="State Unassigned",]
# plot new cases per day by state
ggplot(data=cases_india_bystate,
aes(x=Date, y=newcases,
group=State)) +
facet_wrap(~ State, scale="free", ncol=5) +
geom_smooth(aes(lwd=I(1), colour=State), method="loess", span=0.3, se=FALSE) +
# geom_line(aes(lwd=I(1), colour=State)) +
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")),
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"))),1,1),
limits=as.Date(c("2020-06-14",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("New confirmed cases per day") +
ggtitle("NEW CONFIRMED SARS-CoV2 CASES PER DAY BY STATE IN INDIA") +
scale_y_log10() +
theme(legend.position = "none") # +
# coord_cartesian(ylim=c(1,NA)) # +
# coord_cartesian(xlim=c(as.Date("2021-01-01"),max(fit_india_multi_predsbystate2$collection_date)-20))
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_cases per day by state.png"), width=12, height=12)
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_cases per day by state.pdf"), width=12, height=12)
cases_india_bystate2 = cases_india_bystate[cases_india_bystate$State %in% levels_STATES,]
colnames(cases_india_bystate2)[2]="STATE"
newdat = expand.grid(DATE_NUM=seq(as.numeric(min(cases_india_bystate2$Date)),as.numeric(max(cases_india_bystate2$Date))),
division=unique(as.character(cases_india_bystate2$STATE)))
fit_india_multi_predsbystate = data.frame(newdat,
predict(fit5_india_multi,
newdata = newdat,
type = "prob"), check.names=F)
fit_india_multi_predsbystate = gather(fit_india_multi_predsbystate, LINEAGE2, prob, all_of(levels_LINEAGE2))
fit_india_multi_predsbystate$collection_date = as.Date(fit_india_multi_predsbystate$DATE_NUM, origin="1970-01-01")
fit_india_multi_predsbystate$LINEAGE2 = factor(fit_india_multi_predsbystate$LINEAGE2, levels=levels_LINEAGE2)
colnames(fit_india_multi_predsbystate)[2] = "STATE"
fit_india_multi_predsbystate$STATE = factor(fit_india_multi_predsbystate$STATE, levels=c("Maharashtra","Chhattisgarh","Gujarat","Delhi",
"Karnataka", "West Bengal", "Odisha", "Andhra Pradesh", "Telangana", "Jharkhand"))
fit_india_multi_predsbystate$totnewcases = cases_india_bystate2$newcases[match(interaction(fit_india_multi_predsbystate$STATE,fit_india_multi_predsbystate$collection_date),
interaction(cases_india_bystate2$STATE,cases_india_bystate2$Date))]
fit_india_multi_predsbystate$cases = fit_india_multi_predsbystate$totnewcases*fit_india_multi_predsbystate$prob
fit_india_multi_predsbystate$cases[fit_india_multi_predsbystate$cases==0] = NA
fit_india_multi_predsbystate$STATE = factor(fit_india_multi_predsbystate$STATE,
levels=c("Maharashtra","Chhattisgarh","Gujarat","Delhi",
"Karnataka", "West Bengal", "Odisha", "Andhra Pradesh", "Telangana", "Jharkhand"))
fit_india_multi_predsbystate2 = fit_india_multi_predsbystate
fit_india_multi_predsbystate2$cases[fit_india_multi_predsbystate2$cases==0] = NA
fit_india_multi_predsbystate2$cases[fit_india_multi_predsbystate2$cases<=1] = NA
fit_india_multi_predsbystate2$STATE = factor(fit_india_multi_predsbystate2$STATE,
levels=c("Maharashtra","Chhattisgarh","Gujarat","Delhi",
"Karnataka", "West Bengal", "Odisha", "Andhra Pradesh", "Telangana", "Jharkhand"))
cases_india_bystate2$STATE = factor(cases_india_bystate2$STATE,
levels=c("Maharashtra","Chhattisgarh","Gujarat","Delhi",
"Karnataka", "West Bengal", "Odisha", "Andhra Pradesh", "Telangana", "Jharkhand"))
# sorted by date of introduction of B.1.617.2
ggplot(data=fit_india_multi_predsbystate2,
aes(x=collection_date, y=cases)) +
facet_wrap(~ STATE, scale="free", ncol=2) +
geom_smooth(aes(lwd=I(1), colour=LINEAGE2, group=LINEAGE2), method="loess", span=0.3, se=FALSE) +
geom_smooth(data=cases_india_bystate2, aes(x=Date, y=newcases, lwd=I(1.5)), method="loess", span=0.3, se=FALSE, colour=alpha("black",0.6)) +
# geom_line(aes(lwd=I(1), colour=LINEAGE2, group=LINEAGE2)) +
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")),
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"))),1,1),
limits=as.Date(c("2020-05-31",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("New confirmed cases per day") +
ggtitle("NEW CONFIRMED SARS-CoV2 CASES PER DAY BY VARIANT IN INDIA\n(multinomial fit)") +
scale_colour_manual("lineage", values=lineage_cols2) +
scale_y_log10() +
coord_cartesian(ylim=c(1,NA)) # +
# coord_cartesian(xlim=c(as.Date("2021-01-01"),max(fit_india_multi_predsbystate2$collection_date)-20))
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_confirmed cases multinomial fit.png"), width=8, height=10)
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_confirmed cases multinomial fit.pdf"), width=8, height=10)
# TO DO: group together some strains in category other
ggplot(data=fit_india_multi_predsbystate2,
aes(x=collection_date, y=cases, group=LINEAGE2)) +
facet_wrap(~ STATE, scale="free", ncol=2) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2, group=LINEAGE2), position="stack") +
scale_fill_manual("", values=lineage_cols2) +
annotate("rect", xmin=max(GISAID_india$DATE_NUM)+1,
xmax=as.Date("2021-05-31"), ymin=0, ymax=1, alpha=0.3, 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")),
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"))),1,1),
limits=as.Date(c("2020-06-01",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("New confirmed cases per day") +
ggtitle("NEW CONFIRMED SARS-CoV2 CASES BY VARIANT IN INDIA\n(multinomial fit)")
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_confirmed cases stacked area multinomial fit.png"), width=8, height=10)
ggsave(file=paste0(".\\plots\\",plotdir,"\\india_confirmed cases stacked area multinomial fit.pdf"), width=8, height=10)