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rsv-net-trends.R
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# explore RSV-net cumulative attack rate question
# Mike Famulare
# Dec 9 2022
library(tidyverse)
d = read.csv('Weekly_Rates_of_Laboratory-Confirmed_RSV_Hospitalizations_from_the_RSV-NET_Surveillance_System.csv',
header=TRUE)
names(d)
d$week=as.numeric(d$MMWR.Week)
d$Week.ending.date=as.Date(d$Week.ending.date,'%m/%d/%Y')
# look at whole country
pd = d %>%
filter(State == 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(Age.Category=='Overall') %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
filter(Week.ending.date>as.Date('2018-09-01'))
ggplot(pd) +
geom_line(aes(x=Week.ending.date,y=Rate,group=Season,color=Season)) +
facet_wrap('Season',scale='free_x')
ggplot(pd) +
geom_line(aes(x=Week.ending.date,y=Rate,group=Season,color=Season))
ggplot(pd) +
geom_line(aes(x=Week.ending.date,y=cumsum(Rate),color=Season))
# under 5s
pd = d %>%
filter(State == 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(Age.Category=='0-4 years') %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
filter(Week.ending.date>as.Date('2018-09-01')) %>%
mutate(cumulative_rate=cumsum(Rate))
# fit seasonal ends from pre-covid
m = lm(cumulative_rate ~ Week.ending.date,
data=pd %>%
# filter(MMWR.Week==18) %>%
filter(Week.ending.date<as.Date('2020-06-01')))
pd2=data.frame(Week.ending.date=seq.Date(as.Date('2018-05-01'),as.Date('2023-05-07'),by='week'))
pd2$pre_covid_trend=predict(m,newdata=pd2)
pd2 = pd2 %>% filter(pre_covid_trend>0)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate,color=Season))+
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
xlab('') + ylab('cumulative attack rate per 100k') +
ggtitle('0-4 years old')
ggsave('rsv-net_cumulative_hospitalization_under5.png',units='in',width=6,height=4)
# overall
pd = d %>%
filter(State == 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(Age.Category=='Overall') %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
filter(Week.ending.date>as.Date('2018-09-01')) %>%
mutate(cumulative_rate=cumsum(Rate))
# fit seasonal ends from pre-covid
m = lm(cumulative_rate ~ Week.ending.date,
data=pd %>%
# filter(MMWR.Week==18) %>%
filter(Week.ending.date<as.Date('2020-06-01')))
pd2=data.frame(Week.ending.date=seq.Date(as.Date('2018-05-01'),as.Date('2023-05-07'),by='week'))
pd2$pre_covid_trend=predict(m,newdata=pd2)
pd2 = pd2 %>% filter(pre_covid_trend>0)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate,color=Season))+
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
xlab('') + ylab('cumulative attack rate per 100k')+
ggtitle('all ages')
ggsave('rsv-net_cumulative_hospitalization_all_ages.png',units='in',width=6,height=4)
# adults
pd = d %>%
filter(State == 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(Age.Category=='18+ (Adults)') %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
# filter(Week.ending.date>as.Date('2018-09-01')) %>%
mutate(cumulative_rate=cumsum(Rate))
# fit seasonal ends from pre-covid
m = lm(cumulative_rate ~ Week.ending.date,
data=pd %>%
# filter(MMWR.Week==17) %>%
filter(Week.ending.date<as.Date('2020-06-01'))%>%
filter(Week.ending.date>as.Date('2017-06-01')))
pd2=data.frame(Week.ending.date=seq.Date(as.Date('2013-05-01'),as.Date('2023-05-07'),by='week'))
pd2$pre_covid_trend=predict(m,newdata=pd2)
pd2 = pd2 %>% filter(pre_covid_trend>0)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate,color=Season))+
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
xlab('') + ylab('cumulative attack rate per 100k')+
ggtitle('18+ (adults)')
ggsave('rsv-net_cumulative_hospitalization_adults.png',units='in',width=6,height=4)
# looks like sampling changed from 2014-2017
# age
pd = d %>%
filter(State == 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(grepl('----',Age.Category) | (Age.Category == '50-64 years')) %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
filter(Week.ending.date>as.Date('2018-09-01')) %>%
group_by(State,Age.Category) %>%
mutate(cumulative_rate=cumsum(Rate)) %>%
mutate(Age.Category = factor(sub('----','',Age.Category), levels = c('0-<6 months',
'6-<12 months',
'1-<2 years',
'2-4 years',
'5-11 years',
'12-17 years',
'18-29 years',
'30-39 years',
'40-49 years',
'50-64 years',
'65-74 years',
'75-84 years',
'85+ years')))
# fit seasonal ends from pre-covid
m = lm(cumulative_rate ~ Week.ending.date:Age.Category + Age.Category,
data=pd %>%
# filter(MMWR.Week==17) %>%
filter(Week.ending.date<as.Date('2020-06-01')))
summary(m)
pd2=expand.grid(Week.ending.date=seq.Date(as.Date('2013-05-01'),as.Date('2023-05-07'),by='week'),
Age.Category = levels(pd$Age.Category))
pd2$pre_covid_trend=predict(m,newdata=pd2)
pd2$pre_covid_trend_lower=predict(m,newdata=pd2)-2*predict(m,newdata=pd2,se=TRUE)$se
pd2$pre_covid_trend_upper=predict(m,newdata=pd2)+2*predict(m,newdata=pd2,se=TRUE)$se
pd2 = pd2 %>% filter(pre_covid_trend>0) %>%
mutate(pre_covid_trend_lower = pmax(0,pre_covid_trend_lower)) %>%
mutate(pre_covid_trend_upper = pmax(0,pre_covid_trend_upper))
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate)) +
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
facet_wrap('Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k') +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_color_viridis_d()
ggsave('rsv-net_cumulative_hospitalization_age.png',units='in',width=6,height=4)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=Rate)) +
# geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
facet_wrap('Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k') +
scale_color_viridis_d()
ggsave('rsv-net_weekly_hospitalization_age.png',units='in',width=6,height=4)
ggplot() +
geom_ribbon(data=pd2,aes(x=Week.ending.date,ymin=pre_covid_trend_lower,ymax=pre_covid_trend_upper),alpha=0.3) +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate)) +
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend),linetype='dashed') +
facet_wrap('Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k') +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_color_viridis_d()
ggsave('rsv-net_cumulative_hospitalization_age_trend_uncertainty.png',units='in',width=6,height=4)
# age_state
pd = d %>%
filter(State != 'Entire Network (RSV-NET)') %>%
filter(MMWR.Week!='Overall') %>%
filter(grepl('----',Age.Category) | (Age.Category == '50-64 years')) %>%
filter(Sex == 'Overall') %>%
filter(Race == 'Overall') %>%
filter(Week.ending.date>as.Date('2018-09-01')) %>%
group_by(State,Age.Category) %>%
mutate(cumulative_rate=cumsum(Rate)) %>%
mutate(Age.Category = factor(sub('----','',Age.Category), levels = c('0-<6 months',
'6-<12 months',
'1-<2 years',
'2-4 years',
'5-11 years',
'12-17 years',
'18-29 years',
'30-39 years',
'40-49 years',
'50-64 years',
'65-74 years',
'75-84 years',
'85+ years')))
# fit seasonal ends from pre-covid
m = lm(cumulative_rate ~ Week.ending.date:Age.Category:State + Age.Category:State + State,
data=pd %>%
# filter(MMWR.Week==17) %>%
filter(Week.ending.date<as.Date('2020-06-01')))
summary(m)
pd2=expand.grid(Week.ending.date=seq.Date(as.Date('2013-05-01'),as.Date('2023-05-07'),by='week'),
Age.Category = factor(levels(pd$Age.Category),levels=levels(pd$Age.Category)),
State=unique(pd$State))
pd2$pre_covid_trend=predict(m,newdata=pd2)
pd2$pre_covid_trend_lower=predict(m,newdata=pd2)-2*predict(m,newdata=pd2,se=TRUE)$se
pd2$pre_covid_trend_upper=predict(m,newdata=pd2)+2*predict(m,newdata=pd2,se=TRUE)$se
pd2 = pd2 %>% filter(pre_covid_trend>0)
pd2 = pd2 %>% filter(pre_covid_trend>0) %>%
mutate(pre_covid_trend_lower = pmax(0,pre_covid_trend_lower)) %>%
mutate(pre_covid_trend_upper = pmax(0,pre_covid_trend_upper))
# pd = pd %>% left_join(pd2,by = c("State", "Week.ending.date", "Age.Category"))
# no idea why this join is nan-ing out pre_covid_trend...
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=1+cumulative_rate,group=State,color=State))+
facet_wrap('Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k')
ggsave('rsv-net_cumulative_hospitalization_age_state.png',units='in',width=8,height=6)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate,group=Age.Category, color=Age.Category)) +
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend,group=Age.Category, color=Age.Category),linetype='dashed') +
facet_wrap('State~Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k')
ggsave('rsv-net_cumulative_hospitalization_age_state_trends.png',units='in',width=20,height=15)
ggplot() +
geom_ribbon(data=pd2,aes(x=Week.ending.date,ymin=pre_covid_trend_lower,ymax=pre_covid_trend_upper),alpha=0.3) +
geom_line(data=pd,aes(x=Week.ending.date,y=cumulative_rate,group=Age.Category, color=Age.Category)) +
geom_line(data=pd2,aes(x=Week.ending.date,y=pre_covid_trend,group=Age.Category, color=Age.Category),linetype='dashed') +
facet_wrap('State~Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k')
ggsave('rsv-net_cumulative_hospitalization_age_state_trends_uncertainty.png',units='in',width=20,height=15)
ggplot() +
geom_line(data=pd,aes(x=Week.ending.date,y=Rate,group=Age.Category, color=Age.Category)) +
facet_wrap('State~Age.Category',scale='free_y')+
xlab('') + ylab('cumulative attack rate per 100k')
ggsave('rsv-net_weekly_hospitalization_age_state.png',units='in',width=20,height=15)