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us_pi_excess apr.out.Rmd
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---
title: "Estimating the early death toll of COVID-19 in the United States"
author: ""
date: "6/19/2020"
output:
html_document:
df_print: paged
html_document: null
toc: yes
toc_depth: 2
toc_float: yes
word_document:
toc: yes
toc_depth: '2'
pdf_document:
keep_tex: true
params:
agg.level: 'state'
n.days.filter: 20
web.version: FALSE
extrap.date: '2020-01-26'
count.start.date: '2020-03-01'
end.data.date: '2020-04-30'
---
Dan Weinberger,^1^ Jenny Chen,^7^ Ted Cohen,^1^ Forrest W. Crawford,^2^ Farzad Mostashari,^3^ Don Olson,^4^ Virginia E Pitzer,^1^ Nicholas G Reich,^5^ Marcus Russi,^1^ Lone Simonsen,^6^ Annie Watkins,^1^ Cecile Viboud^7^
^1^Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT
^2^Department of Biostatistics and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT; Yale Departments of Ecology and Evolutionary Biology, Statistics & Data Science, Yale School of Management
^3^Aledade, Inc
^4^Department of Health and Mental Hygiene, New York City, NY
^5^Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA
^6^Department of Science and Environment, Roskilde University, Denmark
^7^Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD
```{r, include = F}
knitr::opts_chunk$set(
collapse = TRUE,
echo=F,
warning=FALSE,
message=FALSE,
comment = "#>",
dev=c('png','pdf'),
fig.path='./figures/',
gganimate = list(
nframes = 50)
)
extrap.date <- as.Date(params$extrap.date)
count.start.date <- as.Date(params$count.start.date)
end.data.date <- as.Date(params$end.data.date)
state.name2 <- c(state.name, 'District of Columbia','Puerto Rico', 'United States', 'New York City')
state.abb2 <- c(state.abb, 'DC','PR','US','NYC')
last.date.format <-
format(end.data.date, '%b %d, %Y')
```
```{r, eval=F}
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("weinbergerlab/ExcessILI")
```
```{r setup}
library(ExcessILI)
library(cdcfluview)
library(reshape2)
library(ggplot2)
library(lubridate)
library(RColorBrewer)
library(plotly)
library(MMWRweek)
library(readr)
library(rjson)
library(htmlTable)
library(RSocrata)
library(pdftools)
library(readr)
library(abind)
library(gsubfn)
library(dplyr)
library(RCurl)
library(gifski)
library(gganimate)
#library(jsonlite)
set.seed(123)
source('./functions/ts_plot_func.R')
source('./functions/format_table.R')
source('./functions/stack_plot.R')
```
```{r archivfunc}
# Using ExcessILI's data archiving functions, returns the most recent copy of
# output obtained by running a function or formula \code{f}, unless this
# copy doesn't exist or is older (by modification time) than \code{maxage}.
# In that case, \code{f} is run and the output is archived into the folder
# Data/'storeName' as an RDS file, using the function ExcessILI::storeRDS.
#
# @param storeName A string. The name of the folder to store output in
# @param f A function or formula taking no arguments. Formulas are coerced to
# functions.
# @param maxage How old can any existing archived file be before \code{f} is
# called again?
runIfExpired <- function(storeName, f, maxage=hours(9999999)) {
basepath <- "Data/"
mostRecent <- mostRecentTimestamp(storeName, basepath=basepath)
f <- rlang::as_function(f)
runAndArchive <- function() {
data <- f()
storeRDS(data, storeName, basepath)
data
}
if (is.na(mostRecent))
return(runAndArchive())
if (mostRecent %--% now() < maxage)
return(retrieveRDS(storeName, basepath))
runAndArchive()
}
```
```{r states_to_plot}
plot.states <- c('CA','TX','FL','NJ', 'NY','WA','IL','GA','MI','LA', 'MA' )
exclude.states <- c('CT', 'NC', 'PR') #no up-to-date number
jama.states <- c('NJ','FL','MA','MI')
#exclude.states <- c('')
```
```{r state_region_cw}
#cross walk file to map states to hhs regions
#
data(cdcfluview::hhs_regions)
hhs_states <- cdcfluview::hhs_regions
hhs_states$state <-
state.abb2[match(hhs_states$state_or_territory, state.name2)]
hhs_states$state_region <- hhs_states$region
hhs_states$state_region[hhs_states$state %in% plot.states] <-
hhs_states$state[hhs_states$state %in% plot.states]
hhs_states <-
hhs_states[, c('state', 'state_region')]
hhs_states$state_region <-
gsub(' ', '', hhs_states$state_region)
hhs_states.cw <-
hhs_states[!is.na(hhs_states$state), ]
#Exclude CT
hhs_states.cw <-
hhs_states.cw[!(hhs_states.cw$state %in% exclude.states), ]
hhs_states.cw.spl <- split(hhs_states.cw,
hhs_states.cw$state_region)
hhs_states.cw.spl <- lapply(hhs_states.cw.spl, function(x) {
x$state_region.lab <- paste(x$state, collapse = ',')
return(x)
})
hhs_states.cw <- do.call('rbind.data.frame', hhs_states.cw.spl)
hhs_states.cw$state_region[hhs_states.cw$state_region != hhs_states.cw$state] <-
hhs_states.cw$state_region.lab[hhs_states.cw$state_region != hhs_states.cw$state]
nyc.state <- as.data.frame(matrix(c('NYC','NYC','NYC'), nrow=1 ))
names(nyc.state) <- names(hhs_states.cw)
hhs_states.cw <- rbind.data.frame(hhs_states.cw,nyc.state)
```
```{r nrevs_format}
#download the NREVSS data
#ARCHIVE
nrevvs.state <- runIfExpired('nrevss_state', ~cdcfluview::who_nrevss(region = c("state")))
clin <- nrevvs.state[["clinical_labs"]]
clin$state <- state.abb2[match(clin$region, state.name2)]
data(hhs_regions)
cw.file <- cdcfluview::hhs_regions
clin2 <- merge(clin, hhs_states.cw,
by = "state")
clin2 <- merge(clin2, cw.file,
by.x = "region",
by.y = "state_or_territory")
clin2.subsetvars <-
c('region', 'region_number',
'year', 'week', 'wk_date',
'total_a','total_b',
'total_specimens','state_region')
clin2 <- clin2[, clin2.subsetvars]
names(clin2)[1:2] <- c("state", "hhs_region")
clin2$total_specimens <- as.numeric(clin2$total_specimens)
clin2$total_a <- as.numeric(clin2$total_a)
clin2$total_b <- as.numeric(clin2$total_b)
#clin2 <- aggregate(clin2[, c('total_specimens','total_a','total_b')], by=list('state'=clin2$state, 'wk_date'=clin2$wk_date,'hhs_region'=clin2$hhs_region), FUN=sum, na.rm=T)
##Florida doesn't have ILI data, so use regions ILI dat
#
#ARCHIVE
nrevvs_hhs <- runIfExpired('nrevss_hhs', ~cdcfluview::who_nrevss(region = c("hhs")))
clin.hhs <- nrevvs_hhs[["clinical_labs"]]
clin.hhs.subsetvars <-
c('region',
'wk_date',
"total_a",'total_b',
'total_specimens')
clin.hhs <- clin.hhs[, clin.hhs.subsetvars]
clin.hhs$region <- as.numeric(gsub("Region ", "", clin.hhs$region))
names(clin.hhs) <-
c("hhs_region",
"wk_date",
"hhs_total_a",'hhs_total_b',
'hhs_total_specimens')
clin3 <- merge(clin2, clin.hhs,
by = c("hhs_region", "wk_date"))
#If no specimens for state, use HHS region estimates
clin3$total_specimens[is.na(clin3$total_specimens)] <-
clin3$hhs_total_specimens[is.na(clin3$total_specimens==0)]
clin3$total_a[is.na(clin3$total_a)] <-
clin3$hhs_total_a[is.na(clin3$total_a)]
clin3$total_b[is.na(clin3$total_b)] <-
clin3$hhs_total_b[is.na(clin3$total_b)]
clin3$total_specimens <- as.numeric(clin3$total_specimens)
clin3$total_a <- as.numeric(clin3$total_a)
clin3$total_b <- as.numeric(clin3$total_b)
clin3$flu_pct_adj <- (clin3$total_a + clin3$total_b)/clin3$total_specimens
clin4<-clin3[,c('state','flu_pct_adj', 'wk_date')]
clin4.lag1<-clin4
clin4.lag1$wk_date <- clin4$wk_date + days(7)
names(clin4.lag1) <-c('state','flu_pct_adj_lag1','wk_date')
clin4.lag2<-clin4
clin4.lag2$wk_date <- clin4$wk_date + days(14)
names(clin4.lag2) <-c('state','flu_pct_adj_lag2','wk_date')
clin4.lags <- merge(clin4, clin4.lag1, by=c('state','wk_date'))
clin4.lags <- merge(clin4.lags, clin4.lag2, by=c('state','wk_date'))
##national flu data
nrevvs.natl <- runIfExpired('nrevss_national', ~cdcfluview::who_nrevss(region = c("national")))
nrevvs.natl <- nrevvs.natl$clinical_labs
nrevvs.natl <-
nrevvs.natl[,c('wk_date','percent_positive') ]
names(nrevvs.natl) <- c('wk_date','flu_pct_adj')
nrevvs.natl$flu_pct_adj <- nrevvs.natl$flu_pct_adj/100
natl.lag1 <- nrevvs.natl
natl.lag1$wk_date <- natl.lag1$wk_date +days(7)
names(natl.lag1) <- c('wk_date','flu_pct_adj_lag1')
natl.lag2 <- nrevvs.natl
natl.lag2$wk_date <- natl.lag2$wk_date +days(14)
names(natl.lag2) <- c('wk_date','flu_pct_adj_lag2')
nrevvs.natl <- merge(nrevvs.natl, natl.lag1, by='wk_date')
nrevvs.natl <- merge(nrevvs.natl, natl.lag2, by='wk_date')
nrevvs.natl$state <- 'United States'
nrevvs.combo <-
rbind.data.frame(nrevvs.natl,clin4.lags)
nrevvs.combo$state <- state.abb2[match(nrevvs.combo$state , state.name2)]
#NYC duplicate
ny.nrevvs <- nrevvs.combo[nrevvs.combo$state=='NY',]
ny.nrevvs$state <-'NYC'
nrevvs.combo <- rbind.data.frame(nrevvs.combo,ny.nrevvs)
nrevvs.combo.spl <-
split(nrevvs.combo, nrevvs.combo$state)
nrevvs.combo.spl <- lapply(nrevvs.combo.spl, function(x){
x<- x[order(x$wk_date),]
month1 <- month(x$wk_date)
cont.correct <- min(x$flu_pct_adj_lag1[x$flu_pct_adj_lag1>0], na.rm=T)/2
x$log.flu.lag1 <- as.numeric(scale(log(x$flu_pct_adj_lag1+cont.correct )))
log.flu.lag1.serf <- x$log.flu.lag1
log.flu.lag1.serf[month1 %in% c(12,1,2) ] <- NA
log.flu.lag1.serf[x$wk_date >=as.Date('2020-03-01') ] <- NA
time <- 1:nrow(x)
sin52 <- sin(2*pi*time/52.1775)
sin26 <- sin(2*pi*time*2/52.1775)
cos52 <- cos(2*pi*time/52.1775)
cos26 <- cos(2*pi*time*2/52.1775)
mod.flu <- lm(log.flu.lag1.serf ~ sin52 +cos52 +sin26 +cos26 )
pred.flu <- predict(mod.flu, newdata=x)
x$log.flu.deseasonalize <- x$log.flu.lag1-pred.flu
x$log.flu.deseasonalize.0 <- x$log.flu.deseasonalize
x$log.flu.deseasonalize.0[x$log.flu.deseasonalize.0<0] <-0
return(x)
})
nrevvs.combo <- do.call('rbind.data.frame', nrevvs.combo.spl)
```
```{r, eval=F}
nrevvs.combo.m <- melt(nrevvs.combo[,c('wk_date','state','log.flu.deseasonalize.0' )], id.vars=c('state','wk_date'))
nrevvs.combo.c <- dcast(nrevvs.combo.m, wk_date~state)
matplot(nrevvs.combo.c[,1:10], type='l')
matplot(nrevvs.combo.c[,11:20], type='l')
matplot(nrevvs.combo.c[,c('TX','FL','NY')], type='l')
```
```{r}
#Reporting delays from NobBS
delays <- readRDS('./outputs/NobBs.complete.iters.rds')
dimnames(delays)[[3]][dimnames(delays)[[3]]=='US'] <- 'United States'
dimnames(delays)[[3]] <-
state.abb2[match(dimnames(delays)[[3]] , state.name2)]
#states1.match <- unique()
states2.match <- dimnames(delays)[[3]]
#commn.states <- Reduce(intersect, list(states1.match,states2.match))
#delays <- delays[,,commn.states]
delays <- delays[,1:10000,]
delays.m <- melt(delays)
names(delays.m) <- c('time.since.death','iter','state','prop')
delays.med <- apply(delays,c (1,3),median)
delays.med.m <- melt(delays.med)
delays.med.m$Var1 <- as.numeric(as.character(delays.med.m$Var1))
names(delays.med.m) <- c('weeks.since.death','state','prop.complete')
```
```{r import_state_pi_data}
#Import the P&I data from fluview--this is used to set the historical baseline; updated weekly
#ARCHIVE
#Use static version of 2014-2018 data
#url.nchs.base1 <- "https://data.cdc.gov/resource/pp7x-dyj2.json"
nchs.base1.data <-
read.csv('./Data/nchs_base1_data/Weekly_Counts_of_Deaths_by_State_and_Select_Causes__2014-2018.csv')
nchs.base1.data <-
nchs.base1.data[,c("Jurisdiction.of.Occurrence","MMWR.Year" ,"MMWR.Week" , "Week.Ending.Date","All..Cause","flag_allcause")]
names(nchs.base1.data) <-c('state', 'year','week','week_end','all_cause', 'flag_ac')
#API is truncating data--download static dataset
#url.nchs.base2 <- "https://data.cdc.gov/resource/muzy-jte6.json"
#ARCHIVE
#nchs.base2.data <- runIfExpired('nchs_base2_data',
# ~jsonlite::read_json(url.nchs.base2, simplifyVector = F))
#nchs.base2.data <-
# read.csv('./Data/nchs_base2_data/Weekly_Counts_of_Deaths_b#y_State_and_Select_Causes__2019-2020.csv')
csv.url <- "https://data.cdc.gov/api/views/r8kw-7aab/rows.csv?accessType=DOWNLOAD"
ds.func <- function(url){
ds1<- read.csv("https://data.cdc.gov/api/views/r8kw-7aab/rows.csv?accessType=DOWNLOAD")
return(ds1)
}
nchs.base2.data <- runIfExpired('nchs_base2_data',
~read.csv("https://data.cdc.gov/api/views/muzy-jte6/rows.csv?accessType=DOWNLOAD")
)
nchs.base2.data <- nchs.base2.data[,c("Jurisdiction.of.Occurrence" ,"Week.Ending.Date" ,"All.Cause")]
names(nchs.base2.data) <- c("State","End.Week", 'Total.Deaths' )
nchs.base2.data$All.Cause <-
as.numeric(nchs.base2.data$Total.Deaths)
names(nchs.base2.data) <-c('state', 'week_end','all_cause')
nchs.base2.data$week_end <- as.Date(nchs.base2.data$week_end, '%m/%d/%Y')
nchs.base2.data$all_cause <-
as.numeric(nchs.base2.data$all_cause)
nchs.base1.data$week_end <-
as.Date(nchs.base1.data$week_end, '%m/%d/%Y')
nchs.base.comb <-
bind_rows(nchs.base1.data,nchs.base2.data)
#Import the cdcfluview data
pi.data <- runIfExpired('pi_mortality_state', ~pi_mortality(coverage_area='state'))
pi.data <- pi.data[pi.data$coverage_area=='state',c('week_end','percent_pni' ,'region_name')]
names(pi.data) <-c('week_end',"percent_pni",'region_name')
pi.data <- merge(nchs.base.comb[,c('state','week_end','all_cause')], pi.data, by.x=c('state','week_end'), by.y=c('region_name','week_end'), all=T)
#estimated P&I deaths from %P&I and N all deaths
pi.data$pi.fv <-
round(pi.data$all_cause*pi.data$percent_pni)
pi.data <- pi.data[pi.data$week_end <= end.data.date,]
pi.data.last <- pi.data[pi.data$week_end==max(pi.data$week_end) ,]
last.date.avail.pi <-
max(pi.data$week_end)
pi.data <- pi.data[!(is.na(pi.data$week_end)),]
pi.data.not.miss.states <- unique(pi.data.last$state)
pi.data.not.miss.states <- state.abb2[match(pi.data.not.miss.states, state.name2)]
pi.data.not.miss.states <-
pi.data.not.miss.states[!is.na(pi.data.not.miss.states)]
```
```{r import_cdc_covid_view}
### Explanation of NCHS data sources
#NCHS mostly provides data based on location of death rather than location of residence. For states like Florida, where there is a large population of seasonal residents, this can make a big difference. NCHS also info on underlying cause for specific causes like pneumonia&influenza. The exception to this is the new P&I&Coronavirus data, which is based on multiple-cause of death statistics CDC fluview provides data on all-cause and P&I based on location of residence, and provides information on multiple-cause (e.g., P&I listed anywhere on death certificate.) To get a baseline for all-cause deaths, we use the NCHS all-cause data, and combine this with the covidView all-cause data. For excess P&I&C, we use combine the all-cause data from NCHS from the baseline period with the *proportion* of deaths that are due to P&I. The assumption is that this proportion is the same for deaths based on residence and location of death. We then back-calculate the number of deaths that were P&I from this number and combine with the updated covidView data.
#official CDC data, updated daily on P&I, covid, all deaths
#url.cdc.covid<-"https://data.cdc.gov/resource/hc4f-j6nb.json" #no longer being updated as of Apr 30
#Updated week days
#url.cdc.covid <- "https://data.cdc.gov/resource/r8kw-7aab.json"
#ARCHIVE
#cdc.data <- runIfExpired('cdc_covid_data',
# ~jsonlite::read_json(url.cdc.covid, simplifyVector = T))
cdc.data <- runIfExpired('cdc_covid_data',
~read.csv("https://data.cdc.gov/api/views/r8kw-7aab/rows.csv?accessType=DOWNLOAD")
)
names(cdc.data) <-
c('data_as_of','start_week', 'end_week', 'group', 'state','indicator','nchs.covid.deaths','nchs.total.deaths','percent_complete', 'nchs.pneu.death','nchs.pneum.w.covid','nchs.flu.death','nchs.pic')
cdc.data$start_week <- NULL
data.vintage <- as.Date(unique(cdc.data$data_as_of),'%m/%d/%Y')
cdc.data$end_week <-
as.Date(cdc.data$end_week,'%m/%d/%Y')
cdc.data$data_as_of <-
as.Date(cdc.data$data_as_of,'%m/%d/%Y')
most.recent <- max(cdc.data$data_as_of)
cdc.data <- cdc.data[cdc.data$data_as_of==most.recent,]
#Convert character to numeric
cdc.data[,c('nchs.covid.deaths','nchs.total.deaths','percent_complete', 'nchs.pneu.death','nchs.pneum.w.covid','nchs.flu.death','nchs.pic')] <- apply(cdc.data[,c('nchs.covid.deaths','nchs.total.deaths','percent_complete', 'nchs.pneu.death','nchs.pneum.w.covid','nchs.flu.death','nchs.pic')],2, as.numeric)
#NCHS suppresses cells with counts of 1-9, in these instances, take diff of PIC and P (assumes flu=0)
cdc.data$nchs.covid.deaths[is.na(cdc.data$nchs.covid.deaths)] <-
cdc.data$nchs.pic[is.na(cdc.data$nchs.covid.deaths)] - cdc.data$nchs.pneu.death[is.na(cdc.data$nchs.covid.deaths)]
#cdc.summary.nat <-
# cdc.data[cdc.data$state=='United States',]
cdc.summary.wk <- cdc.data
cdc.summary.wk <- cdc.summary.wk[, c('state', 'end_week' ,'nchs.covid.deaths','nchs.total.deaths','percent_complete', 'nchs.pneu.death','nchs.pneum.w.covid','nchs.flu.death','nchs.pic')]
```
```{r aggregate_fluview_deaths_natl}
##aggregate cdc fluview data to national-level (excluding exclusion states, to be used for national baseline)
pi.data.exclude <- pi.data
pi.data.exclude$state <-
state.abb2[match(pi.data.exclude$state, state.name2)]
pi.data.exclude <-
pi.data.exclude[!(pi.data.exclude$state %in% exclude.states),]
natl.pi.data <-
aggregate(pi.data.exclude[,c("pi.fv", 'all_cause')],
by=list('week_end'=pi.data.exclude$week_end,
'year_week_num'=pi.data.exclude$week ), FUN=sum)
natl.pi.data$state <- 'United States'
names(natl.pi.data) <- c('week_end','year_week_num','total_pni','all_deaths','state')
state.pi.data <-
pi.data[,c('week_end','pi.fv','all_cause','state')]
names(state.pi.data) <- c('week_end','total_pni','all_deaths','state')
pi.data.combined <-
bind_rows(state.pi.data, natl.pi.data)
pi.data.combined <-
pi.data.combined[pi.data.combined$week_end >=as.Date('2015-01-05'),]
```
```{r combine.cdc.data}
#Combine the fluview P&I data with the CDC's covid data
cdc.comb1 <-
merge(pi.data.combined,cdc.summary.wk, by.y=c('state','end_week'), by.x=c('state','week_end'), all=T)
#Filer dates after the specified end date
cdc.comb1 <- cdc.comb1[cdc.comb1$week_end <= end.data.date,]
cdc.comb1$year <-
year(cdc.comb1$week_end)
max.wk <-
as.numeric(unique(cdc.comb1$year_week_num[cdc.comb1$week_end==max(cdc.comb1$week_end, na.rm=T)])[1])
wk.range <- c(10,max.wk)
# NCHS data from api only available since late-Jan; fill in with historical data from fluview P&I data
cdc.comb1$all_deaths[!is.na(cdc.comb1$nchs.total.deaths)] <-
cdc.comb1$nchs.total.deaths[!is.na(cdc.comb1$nchs.total.deaths)]
cdc.comb1$nchs.pic[is.na(cdc.comb1$nchs.pic)] <-
cdc.comb1$total_pni[is.na(cdc.comb1$nchs.pic)]
cdc.comb1$nchs.total.deaths[is.na(cdc.comb1$nchs.total.deaths)] <-
cdc.comb1$all_deaths[is.na(cdc.comb1$nchs.total.deaths)]
cdc.comb1 <-
cdc.comb1[order(cdc.comb1$state,cdc.comb1$week_end),]
```
```{r clean_pi_data1, include=F}
cdc.comb2 <- cdc.comb1
cdc.comb2$state <-
state.abb2[match(cdc.comb2$state, state.name2)]
cdc.comb2 <- cdc.comb2[!(cdc.comb2$state %in% exclude.states),]
cdc.data.ny.separate <- cdc.comb2
#cdc.comb2$state[cdc.comb2$state=='NYC'] <-'NY'
cdc.comb2 <- merge(cdc.comb2,hhs_states.cw, by='state', all=T)
cdc.comb2$state_region[cdc.comb2$state=='US'] <- 'US'
pi.data.agg <- aggregate(cdc.comb2[,c('total_pni','all_deaths','nchs.covid.deaths','nchs.total.deaths','nchs.pneu.death','nchs.pneum.w.covid','nchs.flu.death','nchs.pic')], by=list('state'=cdc.comb2$state, 'week_end'=cdc.comb2$week_end), FUN=sum, na.rm=T)
spl1<-split(pi.data.agg, pi.data.agg$state)
min.state <- lapply(spl1, function(x){ x$miss.x<-min(x$total_pni, na.rm=T)
return(x)
})
pi.data.clean <- do.call('rbind.data.frame',min.state)
pi.data.clean <- pi.data.clean[!is.na(pi.data.clean$miss.x),]
pi.data.clean$nchs.total.deaths[pi.data.clean$week_end < as.Date('2020-02-01')] <-
pi.data.clean$all_deaths[pi.data.clean$week_end < as.Date('2020-02-01')]
pi.data.clean$nchs.pic[pi.data.clean$week_end < as.Date('2020-02-01')] <-
pi.data.clean$total_pni[pi.data.clean$week_end < as.Date('2020-02-01')]
nrevvs.combo$week_end <- nrevvs.combo$wk_date +days(6)
analysis.data <- merge(pi.data.clean, nrevvs.combo, by=c('state', 'week_end'), all=T)
analysis.data <-
analysis.data[analysis.data$week_end <= end.data.date,]
analysis.data <- analysis.data[!is.na(analysis.data$flu_pct_adj_lag1),]
states.cdc <- unique(analysis.data$state)
states.cdc <- states.cdc[states.cdc!='US']
date.print <- max(analysis.data$week_end, na.rm=T)
#Merge in reporting delay info
analysis.data$vintage <- data.vintage
analysis.data$n.weeks.ago <- round(as.numeric(difftime(analysis.data$vintage , analysis.data$week_end, units='weeks')))
#Merge in reporting delay
analysis.data <- merge(analysis.data,delays.med.m, by.x=c('state','n.weeks.ago'), by.y=c('state',"weeks.since.death"), all=T)
analysis.data$prop.complete[is.na(analysis.data$prop.complete)] <- 1
analysis.data <- analysis.data[!is.na(analysis.data$all_deaths),]
analysis.data$prop.complete.offset <-
analysis.data$prop.complete - 0.5
analysis.data <- analysis.data[analysis.data$state!='US',]
```
```{r import_covid_tracking}
#TESTING DATA FORMATTING
url.test<-"https://covidtracking.com/api/v1/states/daily.json"
#ARCHIVE
json_data <- runIfExpired('covidtracking_states_daily', ~fromJSON(file=url.test))
test.dates <- as.character(sapply(json_data,'[[','date'))
test.state <- sapply(json_data,'[[','state')
testN <- sapply(json_data, '[[', 'totalTestResultsIncrease')
testN <-sapply(testN, function(x){
if(is.null(x)){
x <-0
}
return(x)
}
)
deathN <- sapply(json_data, '[[', 'deathIncrease')
deathN <-sapply(deathN, function(x){
if(is.null(x)){
x <-0
}
return(x)
}
)
test.ds <- cbind.data.frame('state'=test.state,test.dates, 'testN.day'=testN, 'deathN.day'=deathN )
test.ds$test.date.wk <-floor_date(as.Date(test.ds$test.dates, '%Y%m%d'),'week')
test.ds <- merge(test.ds, hhs_states.cw, by='state')
test.ds.agg <- aggregate( test.ds[,c('testN.day','deathN.day')] , by=list('state'=test.ds$state,'date'=test.ds$test.date.wk), FUN=sum, na.rm=T)
names(test.ds.agg) <- c('state','date','testN','deathN')
pop1<-read.csv('./Data/nst-est2019-01.csv')
pop1$state_name <- substring(pop1$state_name,2)
pop1$state <- state.abb2[match(pop1$state_name, state.name2)]
pop1$census_bureau_pop_2019 <- gsub( ',', '',pop1$census_bureau_pop_2019)
pop1$census_bureau_pop_2019 <-as.numeric(pop1$census_bureau_pop_2019)
pop1.reg <- merge(pop1, hhs_states.cw, by='state')
pop2 <- aggregate(pop1.reg[,"census_bureau_pop_2019"], by=list('state'=pop1.reg$state), FUN=sum)
names(pop2) <- c('state','census_bureau_pop_2019')
test.ds2 <- merge(test.ds.agg, pop2, by='state')
test.ds2$test.week.per.capita <- test.ds2$testN/test.ds2$census_bureau_pop_2019*1000
names(test.ds2) <- c('state','date','testN','covid.track.death', 'pop2019',"test.week.per.capita")
test.ds3.spl <- test.ds2
test.ds3.spl$week_end <- test.ds3.spl$date +days(6)
```
```{r}
#Covid data
cd1 <- merge(test.ds3.spl, analysis.data, by=c('state', 'week_end'), all=T)
cd1 <- cd1[, c('state','week_end','nchs.covid.deaths',"covid.track.death")]
cd1$covid.track.death[is.na(cd1$covid.track.death)] <-0
cd1$covid.death.hybrid <- cd1$nchs.covid.deaths
cd1$covid.death.hybrid[cd1$covid.death.hybrid==0 & cd1$covid.track.death<10 & !is.na(cd1$covid.death.hybrid)] <-
cd1$covid.track.death[cd1$covid.death.hybrid==0 & cd1$covid.track.death<10& !is.na(cd1$covid.death.hybrid)]
```
### Mortality data on deaths due to pneumonia & influenza through the week ending `r date.print`
## Overview
*Background*
Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza or due to all causes, can provide a more complete and consistent picture of the burden caused by COVID-19.
*Methods*
We evaluated increases in the occurrence of deaths due to any cause above a seasonal baseline (adjusted for influenza activity) or deaths due to coronaivirus/P&I (P&I&C) for March-early-May across the United States. These estimates are compared with reported deaths due to COVID-19 and with testing data. Detailed methods can be found [here](https://github.com/weinbergerlab/excess_pi_covid/blob/master/post%20methods.pdf)
*Conclusions*
Excess deaths provide an estimate of full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus.
```{r natl_pi_analysis, include=F}
## National analysis of P&I
#Run analysis
analysis.data$one <- 1
analysis.data2 <-
analysis.data[!is.na(analysis.data$nchs.pic),]
set.seed(123)
excess_pi_natl <-
excessCases(ds = analysis.data2,
datevar = "week_end",
statevar = "state",
adj.flu = "none",
denom.var = "nchs.total.deaths",
use.syndromes = c("nchs.pic"),
covs= c('log.flu.deseasonalize.0'),
extrapolation.date = extrap.date ,
sum.dates = count.start.date,
model.type='poisson',
time.res='week')
ds <- excess_pi_natl
dates1 <-
ds[[1]][[1]][[1]]$date
sum.pred.iter <-
excessExtract(ds = ds,
syndrome = 'nchs.pic',
extract.quantity = "sum.pred.iter")
sum.obs <-
excessExtract(ds = ds,
syndrome = 'nchs.pic',
extract.quantity = "sum.obs")
sum.obs.state <- apply(sum.obs,2,sum)
sum.cases.excess <- sapply(1:length(sum.obs.state),
function(x){
sum.obs.state[x] - sum.pred.iter[,x,1]
})
sum.excess.deaths.range.pi <-t(
apply(sum.cases.excess,2,quantile, probs=c(0.025,0.5,0.975)))
sum.excess.deaths.range.pi <- cbind.data.frame('state'=unique(analysis.data2$state), sum.excess.deaths.range.pi)
res.pi <- format_excess_func(excess_pi_natl,'nchs.pic' )
#write.csv(res.pi, './outputs/p_i_natl_obs_expected.csv')
#res.pi.no.us <- res.pi[res.pi$state!='US',]
#res.pi.nat <- aggregate(res.pi.no.us[,c()] )
##National data is created by summing state data
sum.pred.iter.pic <-
excessExtract(ds = excess_pi_natl,
syndrome = 'nchs.pic',
extract.quantity = "sum.pred.iter")
pic.nat.obs.state <-
excessExtract(ds = excess_pi_natl,
syndrome = 'nchs.pic',
extract.quantity = "y")
pic.nat.obs <- pic.nat.obs.state[,dimnames(pic.nat.obs.state)[[2]]!='US',1]
pic.nat.obs <- apply(pic.nat.obs,1,sum)
pred.iter.pic <-
excessExtract(ds = excess_pi_natl,
syndrome = 'nchs.pic',
extract.quantity = "pred.iter")
pred.iter.pic2 <- array(pred.iter.pic,dim=c(length(dates1),10000,dim(pred.iter.pic)[2],1 ))
dimnames(pred.iter.pic2)[[3]] <- dimnames(pred.iter.pic)[[2]]
#Sum the state-level estimates to get national estimate
pred.iter.pic2.nat <-
pred.iter.pic2[,,dimnames(pred.iter.pic2)[[3]]!='US',1]
pred.iter.pic2.nat.sum <- apply(pred.iter.pic2.nat,c(1,2),sum)
pred.iter.pic2.nat.q <- t(apply(pred.iter.pic2.nat.sum,1, quantile, probs=c(0.025,0.5,0.975)))
res.pi.nat <- cbind.data.frame('week_start'=dates1,'all_cause'=pic.nat.obs,pred.iter.pic2.nat.q)
names(res.pi.nat) <- c('week_start','obs','lpi','pred','upi')
res.pi.nat$week_end <- res.pi.nat$week_start+days(6)
res.pi.nat$unexplained.cases <-
res.pi.nat$obs- res.pi.nat$pred
res.pi.nat$state <- 'US.agg'
mmwr.ag1 <- mmwr_week(res.pi.nat$week_end)
res.pi.nat <- cbind.data.frame(mmwr.ag1,res.pi.nat)
sum(res.pi.nat$unexplained.cases[res.pi.nat$week_start>=params$count.start.date] )
res.pi.nat <- bind_rows(res.pi.nat, res.pi)
#sum.excess.deaths.range.pi
#national: sum state estimates
state.sum.pred.iter.pi <- sum.pred.iter.pic[,dimnames(sum.pred.iter.pic)[[2]]!='US',]
nat.sum.pred.iter.pi <- apply(state.sum.pred.iter.pi,1,sum)
obs.state2 <- cbind.data.frame(dates1,pic.nat.obs.state[,,1])
obs.state2 <- obs.state2[obs.state2$dates1 >=params$count.start.date,]
sum.obs.pi.nat <-
sum(obs.state2[,-1] )
excess.nat.iter <- sum.obs.pi.nat - nat.sum.pred.iter.pi
sum.excess.deaths.range.pi.nat <-
quantile(excess.nat.iter, probs=c(0.025,0.1,0.3,0.5,0.975))
sum.excess.deaths.range.pi <- bind_rows(sum.excess.deaths.range.pi.nat,sum.excess.deaths.range.pi)
sum.excess.deaths.range.pi$state <- as.character(sum.excess.deaths.range.pi$state)
sum.excess.deaths.range.pi$state[is.na(sum.excess.deaths.range.pi$state)] <- 'US.agg'
```
```{r, eval= F}
#Excess deaths due to pneumonia/influenza/covid nationally in the specified time period
sum.excess.deaths.range.pi <- sum.excess.deaths.range.pi[order(-sum.excess.deaths.range.pi$`50%`),]
print(sum.excess.deaths.range.pi)
```
```{r natl_pi_plot, eval=F}
natplot <- res.pi.nat[res.pi.nat$state=='US.agg',]
yrange.pneu <- range(c(res.pi.nat$pred, res.pi.nat$obs, res.pi.nat$upi,0))
plot(natplot$week_end, natplot$pred, type='l', ylim=yrange.pneu, col='gray',bty='l', ylab='Deaths due to P&I&C', xlab='' )
polygon(c(natplot$week_end, rev(natplot$week_end)), c(natplot$lpi, rev(natplot$upi)),col = rgb(0, 0, 0, alpha = 0.1), border = NA )
points(natplot$week_end, natplot$obs, type='l', col='red', bty='l')
plot( natplot$obs/ natplot$pred, type='l')
abline(h=1)
```
```{r main analysis, include=F}
## National analysis of all-cause
#Run analysis
analysis.data$one <- 1
analysis.data2 <-
analysis.data[!is.na(analysis.data$total_pni),]
analysis.data$one <- 365000000/100000
set.seed(123)
excess_ac_natl <-
excessCases(ds = analysis.data2,
datevar = "week_end",
statevar = "state",
adj.flu = "none" ,
denom.var = "prop.complete.offset",
use.syndromes = c("all_deaths"),
covs= c('log.flu.deseasonalize.0'),
extrapolation.date = extrap.date,
sum.dates=count.start.date,
model.type='poisson',
time.res='week')
ds <- excess_ac_natl
dates1 <-
ds[[1]][[1]][[1]]$date
sum.obs.ac <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "sum.obs")
denom.check <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "denom")
dispersion <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "disp")
##National data is created by summing state data
sum.pred.iter.ac <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "sum.pred.iter")
ac.nat.obs <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "y")
ac.nat.obs <- ac.nat.obs[,dimnames(ac.nat.obs)[[2]]!='US',1]
ac.nat.obs <- apply(ac.nat.obs,1,sum)
pred.iter.ac <-
excessExtract(ds = ds,
syndrome = 'all_deaths',
extract.quantity = "pred.iter")
pred.iter.ac2 <- array(pred.iter.ac,dim=c(length(dates1),10000,dim(pred.iter.ac)[2],1 ))
dimnames(pred.iter.ac2)[[3]] <- dimnames(pred.iter.ac)[[2]]
#Sum the state-level estimates to get national estimate
pred.iter.ac2.nat <-
pred.iter.ac2[,,dimnames(pred.iter.ac2)[[3]]!='US',1]
pred.iter.ac2.nat.sum <- apply(pred.iter.ac2.nat,c(1,2),sum)
pred.iter.ac2.nat.q <- t(apply(pred.iter.ac2.nat.sum,1, quantile, probs=c(0.025,0.5,0.975)))
res.ac.nat <- cbind.data.frame('week_start'=dates1,'all_cause'=ac.nat.obs,pred.iter.ac2.nat.q)
names(res.ac.nat) <- c('week_start','obs','lpi','pred','upi')
res.ac.nat$week_end <- res.ac.nat$week_start+days(6)
covid.deaths.nat <- aggregate(cd1[cd1$state!='US',"covid.death.hybrid",drop=F], by=list('week_end'=cd1$week_end[cd1$state!='US']), FUN=sum)
covid.deaths.nat$week_start <- covid.deaths.nat$week_end -days(6)
res.ac.nat <- merge(res.ac.nat, covid.deaths.nat, by=c('week_start', 'week_end') , all=T)
res.ac.nat$unexplained.cases <- res.ac.nat$obs - res.ac.nat$pred
res.ac.nat$state <- 'US.agg'
mmwr.ag1 <- mmwr_week(res.ac.nat$week_end)
res.ac.nat <- cbind.data.frame(mmwr.ag1,res.ac.nat)
res.ac.nat <- res.ac.nat[!is.na(res.ac.nat$pred),]
sum.obs.ac2 <- apply(sum.obs.ac,2,sum)
sum.ac.excess <- sapply(1:length(sum.obs.ac2),
function(x){
sum.obs.ac2[x] - sum.pred.iter.ac[,x,1]
})
sum.excess.deaths.range.ac <-t(
apply(sum.ac.excess,2,quantile, probs=c(0.025,0.1,0.3,0.5,0.975)))
sum.excess.deaths.range.ac <- cbind.data.frame('state'=unique(analysis.data2$state), sum.excess.deaths.range.ac)
res.ac <- format_excess_func(excess_ac_natl,'all_deaths')
res.ac <- merge(res.ac,cd1[,c("covid.death.hybrid", 'state', 'week_end')] , by=c('state', 'week_end'))
res.ac <- bind_rows(res.ac.nat,res.ac)
res.ac <- res.ac[!is.na(res.ac$pred),]
#national: sum state estimates
state.sum.pred.iter.ac <- sum.pred.iter.ac[,dimnames(sum.pred.iter.ac)[[2]]!='US',]
nat.sum.pred.iter.ac <- apply(state.sum.pred.iter.ac,1,sum)
sum.obs.ac.nat <- sum(sum.obs.ac2[names(sum.obs.ac2)!='US'])
excess.nat.iter <- sum.obs.ac.nat - nat.sum.pred.iter.ac
sum.excess.deaths.range.ac.nat <-
quantile(excess.nat.iter, probs=c(0.025,0.1,0.3,0.5,0.975))
sum.excess.deaths.range.ac <- bind_rows(sum.excess.deaths.range.ac.nat,sum.excess.deaths.range.ac)
sum.excess.deaths.range.ac$state <- as.character(sum.excess.deaths.range.ac$state)
sum.excess.deaths.range.ac$state[is.na(sum.excess.deaths.range.ac$state)] <- 'US.agg'
```
```{r, include=F }
#print(sum.excess.deaths.range.ac)
nat.range.ac <-sum.excess.deaths.range.ac.nat
formatted.ac <-
paste0(round(nat.range.ac['50%'],-2),
'(' ,
round(nat.range.ac['2.5%'],-2), ',' ,
round(nat.range.ac['97.5%'],-2), ')'
)
formatted.ac
nat.range.ac
```