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analysis_VOCs_GISAID genomic epidemiology metadata_Bangladesh.R
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# ANALYSIS OF GROWTH ADVANTAGE OF DIFFERENT VOCs IN PORTUGAL (GISAID GENOMIC EPIDEMIOLOGY METADATA)
# T. Wenseleers
# last update 21 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("2021-06-20")
today_num = as.numeric(today)
today # "2021-06-20"
plotdir = "VOCs_GISAID"
suppressWarnings(dir.create(paste0(".//plots//",plotdir)))
# import GISAID genomic epidemiology metadata (file version metadata_2021-06-18_05-50.tsv.gz)
GISAID = read_tsv(gzfile(".//data//GISAID_genomic_epidemiology//metadata_2021-06-18_05-50.tsv.gz"), col_types = cols(.default = "c"))
GISAID = as.data.frame(GISAID)
GISAID$date = as.Date(GISAID$date)
GISAID = GISAID[!is.na(GISAID$date),]
unique(GISAID$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" "2021-06-11"
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,]$Country)
sel_countries_ref
# [1] "Australia" "Belgium" "Spain" "Denmark" "France" "Germany" "India" "Ireland"
# [9] "Italy" "Japan" "Portugal" "Russia" "Singapore" "Spain" "Switzerland" "United Kingdom"
# [17] "USA"
sel_countries = intersect(sel_countries_target, sel_countries_ref)
sel_countries
# [1] "Australia" "Belgium" "Spain" "Denmark" "France" "Germany" "India" "Ireland"
# [9] "Italy" "Japan" "Portugal" "Russia" "Singapore" "Spain" "Switzerland" "United Kingdom"
# [17] "USA"
# sel_countries = sel_countries[!(sel_countries %in% c("Japan","USA"))] # Japan is almost only import & for USA we do separate analysis by state
# ANALYSIS OF VOCs IN PORTUGAL ####
sel_countries = "Bangladesh"
tblB117 = table_country_lineage[table_country_lineage$Lineage==sel_ref_lineage&table_country_lineage$Count>10&table_country_lineage$Country %in% sel_countries,]
tblB117
GISAID_sel = GISAID[GISAID$country %in% sel_countries,]
nrow(GISAID_sel) # 1688
unique(GISAID_sel$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) # 3080
GISAID_sel = GISAID_sel[GISAID_sel$country==GISAID_sel$country_exposure,] # we remove travel-related cases
nrow(GISAID_sel) # 1962
sum(GISAID_sel$LINEAGE1=="B.1.617+") # 559
unique(GISAID_sel$country[GISAID_sel$LINEAGE1=="B.1.1.7"])
sum(GISAID_sel$LINEAGE1=="B.1.1.7") # 11199
sum(GISAID_sel$LINEAGE1=="B.1.1.519") # 0
table(GISAID_sel$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.2" "B.1" "B.1.1.7" "B.1.177+"
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$country)
# B.1.617+ cases before Apr 14 are likely mostly imported cases, so we remove those
# GISAID_sel = GISAID_sel[-which(grepl("B.1.617", GISAID_sel$pango_lineage, fixed=TRUE)&GISAID_sel$date<=as.Date("2021-04-14")),]
table(GISAID_sel$LINEAGE2)
range(GISAID_sel$date) # "2020-01-24" "2021-06-02"
GISAID_sel = GISAID_sel[GISAID_sel$date>="2020-11-01",]
# aggregated data to make Muller plots of raw data
# aggregated by week for selected variant lineages
data_agbyweek1 = as.data.frame(table(GISAID_sel$floor_date, GISAID_sel$LINEAGE1))
colnames(data_agbyweek1) = c("floor_date", "LINEAGE1", "count")
data_agbyweek1_sum = aggregate(count ~ floor_date, data=data_agbyweek1, sum)
data_agbyweek1$total = data_agbyweek1_sum$count[match(data_agbyweek1$floor_date, data_agbyweek1_sum$floor_date)]
sum(data_agbyweek1[data_agbyweek1$LINEAGE1=="B.1.617+","total"]) == nrow(GISAID_sel) # correct
data_agbyweek1$collection_date = as.Date(as.character(data_agbyweek1$floor_date))
data_agbyweek1$LINEAGE1 = factor(data_agbyweek1$LINEAGE1, levels=levels_LINEAGE1)
data_agbyweek1$collection_date_num = as.numeric(data_agbyweek1$collection_date)
data_agbyweek1$prop = data_agbyweek1$count/data_agbyweek1$total
data_agbyweek1$floor_date = NULL
data_agbyweek2 = as.data.frame(table(GISAID_sel$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
# 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$LINEAGE2)=="other")] = "grey75"
muller_bangladesh_raw2 = ggplot(data=data_agbyweek2, aes(x=collection_date, y=count, group=LINEAGE2)) +
# facet_wrap(~ STATE, ncol=1) +
# geom_col(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE1), width=1, position="fill") +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2, group=LINEAGE2), position="fill") +
scale_fill_manual("", values=lineage_cols2) +
scale_x_continuous(breaks=as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01")),
labels=substring(months(as.Date(c("2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01","2021-02-01","2021-03-01","2021-04-01","2021-05-01","2021-06-01"))),1,1),
limits=as.Date(c("2020-11-01",NA)), expand=c(0,0)) +
# guides(color = guide_legend(reverse=F, nrow=2, byrow=T), fill = guide_legend(reverse=F, nrow=2, byrow=T)) +
theme_hc() +
# labs(title = "MAIN SARS-CoV2 VARIANT LINEAGES IN THE UK") +
ylab("Share") +
theme(legend.position="right",
axis.title.x=element_blank()) +
labs(title = "SPREAD OF SARS-CoV2 VARIANTS OF CONCERN IN BANGLADESH\n(GISAID data)")
# +
# coord_cartesian(xlim=c(1,max(GISAID_sel$Week)))
muller_bangladesh_raw2
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_muller plots_raw data.png"), width=8, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_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_bangladesh_multi = nnet::multinom(LINEAGE2 ~ scale(DATE_NUM), weights=count, data=data_agbyweek2, maxit=1000)
fit2_bangladesh_multi = nnet::multinom(LINEAGE2 ~ ns(DATE_NUM, df=2), weights=count, data=data_agbyweek2, maxit=1000)
BIC(fit1_bangladesh_multi, fit2_bangladesh_multi)
# df BIC
# fit1_bangladesh_multi 22 2064.954
# fit2_bangladesh_multi 33 2069.697
# growth rate advantage compared to UK type B.1.1.7 (difference in growth rate per day)
emtrbangladesh = emtrends(fit1_bangladesh_multi, trt.vs.ctrl ~ LINEAGE2,
var="DATE_NUM", mode="latent",
at=list(DATE_NUM=max(GISAID_sel$DATE_NUM)))
delta_r_bangladesh = data.frame(confint(emtrbangladesh,
adjust="none", df=NA)$contrasts,
p.value=as.data.frame(emtrbangladesh$contrasts)$p.value)
delta_r_bangladesh
# contrast estimate SE df asymp.LCL asymp.UCL p.value
# 1 B.1 - B.1.1.7 -0.015760582 0.005588905 NA -0.02671463 -0.0048065282 7.566629e-02
# 2 B.1.1 - B.1.1.7 -0.025255533 0.008620075 NA -0.04215057 -0.0083604967 6.030154e-02
# 3 B.1.1.25 - B.1.1.7 -0.036967264 0.003958733 NA -0.04472624 -0.0292082900 4.305119e-08
# 4 B.1.1.318 - B.1.1.7 0.046776477 0.010112530 NA 0.02695628 0.0665966716 1.239353e-03
# 5 B.1.351 - B.1.1.7 0.039217075 0.008375764 NA 0.02280088 0.0556332701 1.083073e-03
# 6 B.1.351.3 - B.1.1.7 0.035065165 0.004511982 NA 0.02622184 0.0439084881 9.789453e-07
# 7 (B.1.36+) - B.1.1.7 -0.043928528 0.009918480 NA -0.06336839 -0.0244886651 1.978623e-03
# 8 P.1 - B.1.1.7 -0.008822198 0.024879085 NA -0.05758431 0.0399399134 9.975844e-01
# 9 B.1.617.1 - B.1.1.7 -0.018648275 7.173050533 NA -14.07756898 14.0402724288 1.000000e+00
# 10 B.1.617.2 - B.1.1.7 0.161924548 0.022826249 NA 0.11718592 0.2066631736 4.171027e-06
# 11 other - B.1.1.7 -0.008863306 0.005008406 NA -0.01867960 0.0009529889 4.547713e-01
# fitted prop of different LINEAGES in the bangladesh today
multinom_preds_today_avg = data.frame(emmeans(fit1_bangladesh_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 2.753713e-04 2.431541e-04 NA -2.012019e-04 7.519445e-04
# 2 B.1 1.272144e-05 1.359463e-05 NA -1.392355e-05 3.936644e-05
# 3 B.1.1 1.139971e-06 1.732180e-06 NA -2.255038e-06 4.534981e-06
# 4 B.1.1.25 4.756038e-06 4.507721e-06 NA -4.078933e-06 1.359101e-05
# 5 B.1.1.318 3.710757e-03 3.671173e-03 NA -3.484609e-03 1.090612e-02
# 6 B.1.351 3.234426e-03 3.021596e-03 NA -2.687794e-03 9.156645e-03
# 7 B.1.351.3 2.600578e-02 1.995600e-02 NA -1.310727e-02 6.511882e-02
# 8 B.1.36+ 5.747289e-08 1.096374e-07 NA -1.574125e-07 2.723582e-07
# 9 P.1 1.187656e-06 3.895864e-06 NA -6.448098e-06 8.823410e-06
# 10 B.1.617.1 3.573058e-12 3.724473e-09 NA -7.296260e-09 7.303407e-09
# 11 B.1.617.2 9.667089e-01 2.538959e-02 NA 9.169462e-01 1.016472e+00
# 12 other 4.490368e-05 4.393845e-05 NA -4.121409e-05 1.310215e-04
# % non-B.1.1.7
colSums(multinom_preds_today_avg[-1, c("prob","asymp.LCL","asymp.UCL")])
# prob asymp.LCL asymp.UCL
# 0.9997246 0.8975985 1.1018508
# PLOT MULTINOMIAL FIT
# extrapolate = 30
date.from = as.numeric(as.Date("2020-06-01"))
date.to = as.numeric(as.Date("2021-07-31")) # max(GISAID_sel$DATE_NUM)+extrapolate
# multinomial model predictions (fastest, but no confidence intervals)
predgrid = expand.grid(list(DATE_NUM=seq(date.from, date.to)))
fit_bangladesh_multi_preds = data.frame(predgrid, as.data.frame(predict(fit1_bangladesh_multi, newdata=predgrid, type="prob")),check.names=F)
library(tidyr)
library(tidyselect)
fit_bangladesh_multi_preds = gather(fit_bangladesh_multi_preds, LINEAGE2, prob, all_of(levels_LINEAGE2), factor_key=TRUE)
fit_bangladesh_multi_preds$collection_date = as.Date(fit_bangladesh_multi_preds$DATE_NUM, origin="1970-01-01")
fit_bangladesh_multi_preds$LINEAGE2 = factor(fit_bangladesh_multi_preds$LINEAGE2, levels=levels_LINEAGE2)
muller_bangladesh_mfit = ggplot(data=fit_bangladesh_multi_preds,
aes(x=collection_date, y=prob, group=LINEAGE2)) +
# facet_wrap(~ STATE) +
geom_area(aes(lwd=I(1.2), colour=NULL, fill=LINEAGE2, group=LINEAGE2), position="stack") +
scale_fill_manual("", values=lineage_cols2) +
annotate("rect", xmin=max(GISAID_sel$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 BANGLADESH\n(GISAID data, multinomial fit)")
muller_bangladesh_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_muller plots_multinom fit.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_muller plots_multinom fit.pdf"), width=10, height=6)
library(ggpubr)
ggarrange(muller_bangladesh_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_bangladesh_mfit+ggtitle("Multinomial fit"), ncol=1)
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_muller plots multipanel_multinom fit.png"), width=10, height=10)
ggsave(file=paste0(".\\plots\\",plotdir,"\\bangladesh_muller plots multipanel_multinom fit.pdf"), width=10, height=10)
# PLOT MODEL FIT WITH DATA & CONFIDENCE INTERVALS
# multinomial model predictions by state with confidence intervals (but slower)
fit_spain_multi_preds_bystate_withCI = data.frame(emmeans(fit3_spain_multi,
~ LINEAGE2,
by=c("DATE_NUM","STATE"),
at=list(DATE_NUM=seq(date.from, date.to, by=7)), # by=7 to speed up things a bit
mode="prob", df=NA))
fit_spain_multi_preds_bystate_withCI$collection_date = as.Date(fit_spain_multi_preds_bystate_withCI$DATE_NUM, origin="1970-01-01")
fit_spain_multi_preds_bystate_withCI$LINEAGE2 = factor(fit_spain_multi_preds_bystate_withCI$LINEAGE2, levels=levels_LINEAGE2)
fit_spain_multi_preds2 = fit_spain_multi_preds_bystate_withCI
fit_spain_multi_preds_bystate_withCI[fit_spain_multi_preds_bystate_withCI$collection_date==as.Date("2021-06-21")&fit_spain_multi_preds_bystate_withCI$LINEAGE2=="B.1.617.2",]
# LINEAGE2 DATE_NUM STATE prob SE df asymp.LCL asymp.UCL collection_date
# 615 B.1.617.2 18799 Arizona 0.3415259 0.04045846 NA 0.26222877 0.4208230 2021-06-21
# 1286 B.1.617.2 18799 California 0.5298582 0.02065233 NA 0.48938040 0.5703360 2021-06-21
# 1957 B.1.617.2 18799 Colorado 0.5735638 0.02395763 NA 0.52660767 0.6205198 2021-06-21
# 2628 B.1.617.2 18799 Florida 0.2797337 0.02345015 NA 0.23377227 0.3256952 2021-06-21
# 3299 B.1.617.2 18799 Illinois 0.2938156 0.02675727 NA 0.24137234 0.3462589 2021-06-21
# 3970 B.1.617.2 18799 Indiana 0.4238384 0.03151071 NA 0.36207853 0.4855982 2021-06-21
# 4641 B.1.617.2 18799 Kansas 0.5822103 0.02959658 NA 0.52420206 0.6402185 2021-06-21
# 5312 B.1.617.2 18799 Maryland 0.3372405 0.03817511 NA 0.26241864 0.4120623 2021-06-21
# 5983 B.1.617.2 18799 Massachusetts 0.4400967 0.02592195 NA 0.38929066 0.4909028 2021-06-21
# 6654 B.1.617.2 18799 Minnesota 0.1770255 0.02348268 NA 0.13100029 0.2230507 2021-06-21
# 7325 B.1.617.2 18799 Missouri 0.7127919 0.02808481 NA 0.65774667 0.7678371 2021-06-21
# 7996 B.1.617.2 18799 Nebraska 0.2327069 0.06940555 NA 0.09667449 0.3687392 2021-06-21
# 8667 B.1.617.2 18799 New Jersey 0.5641522 0.03021256 NA 0.50493669 0.6233677 2021-06-21
# 9338 B.1.617.2 18799 New York 0.4297980 0.02844750 NA 0.37404198 0.4855541 2021-06-21
# 10009 B.1.617.2 18799 Oregon 0.1345187 0.03766101 NA 0.06070446 0.2083329 2021-06-21
# 10680 B.1.617.2 18799 Texas 0.4653436 0.02431382 NA 0.41768935 0.5129978 2021-06-21
# 11351 B.1.617.2 18799 Utah 0.7575435 0.02195991 NA 0.71450290 0.8005842 2021-06-21
# 12022 B.1.617.2 18799 Virginia 0.4180612 0.04118245 NA 0.33734505 0.4987773 2021-06-21
# 12693 B.1.617.2 18799 Washington 0.4248052 0.02174424 NA 0.38218725 0.4674231 2021-06-21
# 13364 B.1.617.2 18799 Wisconsin 0.3118342 0.04667627 NA 0.22035044 0.4033180 2021-06-21
# fit_spain_multi_preds2 = fit_spain_multi_preds_bystate # without CIs
# fit_spain_multi_preds2$asymp.LCL = NA
# fit_spain_multi_preds2$asymp.UCL = NA
# on logit scale:
ymin = 0.001
ymax = 0.999
fit_spain_multi_preds2$asymp.LCL[fit_spain_multi_preds2$asymp.LCL<ymin] = ymin
fit_spain_multi_preds2$asymp.UCL[fit_spain_multi_preds2$asymp.UCL<ymin] = ymin
fit_spain_multi_preds2$asymp.UCL[fit_spain_multi_preds2$asymp.UCL>ymax] = ymax
fit_spain_multi_preds2$prob[fit_spain_multi_preds2$prob<ymin] = ymin
plot_spain_mfit_logit = qplot(data=fit_spain_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=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 THE spain\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_agbyweekregion2,
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_agbyweekregion2$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_spain_mfit_logit
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit by state_logit scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit by state_logit scale.pdf"), width=10, height=6)
# on response scale:
plot_spain_mfit = qplot(data=fit_spain_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=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 THE spain\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")),
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)) +
# 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_agbyweekregion2,
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_agbyweekregion2$total)), breaks=c(100,1000,10000)) +
# guides(fill=FALSE) +
# guides(colour=FALSE) +
theme(legend.position = "right") +
xlab("Collection date")
plot_spain_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit by state_response scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit by state_response scale.pdf"), width=10, height=6)
# overall multinomial model predictions with confidence intervals
fit_spain_multi_preds_withCI = data.frame(emmeans(fit3_spain_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_spain_multi_preds_withCI$collection_date = as.Date(fit_spain_multi_preds_withCI$DATE_NUM, origin="1970-01-01")
fit_spain_multi_preds_withCI$LINEAGE2 = factor(fit_spain_multi_preds_withCI$LINEAGE2, levels=levels_LINEAGE2)
fit_spain_multi_preds3 = fit_spain_multi_preds_withCI
fit_spain_multi_preds_withCI[fit_spain_multi_preds_withCI$collection_date==as.Date("2021-06-21")&fit_spain_multi_preds_withCI$LINEAGE2=="B.1.617.2",]
# LINEAGE2 DATE_NUM STATE prob SE df asymp.LCL asymp.UCL collection_date
# 615 B.1.617.2 18799 0.4215232 0.01522866 NA 0.3916756 0.4513708 2021-06-21
# fit_spain_multi_preds2 = fit_spain_multi_preds # without CIs
# fit_spain_multi_preds2$asymp.LCL = NA
# fit_spain_multi_preds2$asymp.UCL = NA
# on logit scale:
ymin = 0.001
ymax = 0.999
fit_spain_multi_preds3$asymp.LCL[fit_spain_multi_preds3$asymp.LCL<ymin] = ymin
fit_spain_multi_preds3$asymp.UCL[fit_spain_multi_preds3$asymp.UCL<ymin] = ymin
fit_spain_multi_preds3$asymp.UCL[fit_spain_multi_preds3$asymp.UCL>ymax] = ymax
fit_spain_multi_preds3$prob[fit_spain_multi_preds3$prob<ymin] = ymin
plot_spain_avg_mfit_logit = qplot(data=fit_spain_multi_preds3, x=collection_date, y=prob, geom="blank") +
# facet_wrap(~ STATE) +
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 THE spain\n(GISAID data 20 states, 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, 3), 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_spain_avg_mfit_logit
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit_logit scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit_logit scale.pdf"), width=10, height=6)
# on response scale:
plot_spain_avg_mfit = qplot(data=fit_spain_multi_preds3, 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=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 THE spain\n(GISAID data 20 states, 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")),
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)) +
# 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, 3), 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_spain_avg_mfit
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit_response scale.png"), width=10, height=6)
ggsave(file=paste0(".\\plots\\",plotdir,"\\spain_multinom fit_response scale.pdf"), width=10, height=6)
# plot new cases by variant & state ####
# 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)