R impute/'approximate' delayed covid time series points using non-delayed total - r

Question

I'm using a grouped time series data set where there's often NAs for more recent dates (length of NAs varies fairly randomly). A total of all the series is provided in the data, where for more recent dates, this total is actually greater than the sum of the individual series, I guess because of imputation/forecasting.
So, my question is, how can the missing values be estimated, assuming that the series total is correct?
My general approach is to calculate what proportion of the total each series is, and somehow extrapolate to the future missing dates. As you can see by the graphs, I'm not so successful. There's complications caused by differing last dates of reported data. I'm not sure if cumulative makes a difference.
R code for simulated data and failed solution below:
Code:
## simulate simple grouped time series
library(tidyverse)
set.seed(555)
## time series length, e.g. 10
len=10
## group names
grps=letters[1:5]
df=bind_rows(lapply(grps,function(z){
tibble(rn=seq(1:len)) %>%
mutate(real_val=runif(len,min=0,max=1)) %>%
mutate(grp=z) %>%
select(grp,rn,real_val) %>%
## replace final data points with NA, length varying by group
## this simulates delays in data reporting across groups
mutate(reported_val=ifelse(rn>len-match(z,letters)+1,NA,real_val)) %>%
# mutate(reported_val=ifelse(rn>len-runif(1,0,round(max_trim)),NA,real_val)) %>%
## make cumulative to assist viz a bit. may affect imputation method.
group_by(grp) %>%
arrange(rn) %>%
mutate(real_val=cumsum(real_val),reported_val=cumsum(reported_val)) %>%
ungroup()
}))
df
## attempt to impute/estimate missing real values, given total for each rn (date)
## general solution is to use (adjusted?) proportions of the total.
df2=df %>%
group_by(rn) %>%
mutate(sum_real_val=sum(real_val),sum_reported_val=sum(reported_val,na.rm=T)) %>%
ungroup() %>%
## total value missing for each date
mutate(val_missing=sum_real_val-sum_reported_val) %>%
## what proportion of the continent that country takes up
mutate(prop=reported_val/sum_real_val) %>%
# mutate(prop=reported_val/sum_reported_val) %>%
## fill missing proportions to end terminus from most recent value
group_by(grp) %>%
arrange(rn) %>%
fill(prop,.direction='down') %>%
ungroup() %>%
## get estimated proportion of those missing
mutate(temp1=ifelse(is.na(reported_val),prop,NA)) %>%
## re-calculate proportion as only of those missing.
group_by(grp) %>%
mutate(prop_temp1=temp1/sum(temp1,na.rm=T)) %>%
ungroup() %>%
## if value missing, then multiply total missing by expected proportion of missing
mutate(result_val=ifelse(is.na(reported_val),val_missing*prop_temp1,reported_val)) %>%
ungroup()
## time series plot
## stacked by group, black line shows real_val total.
ggplot(df2 %>%
select(grp,rn,real_val,reported_val,sum_real_val,result_val) %>%
gather(val_grp,val,-c(grp,rn,sum_real_val)) %>%
ungroup(),aes(rn,val))+
# geom_line(aes(colour=loc))+
geom_area(aes(fill=grp))+
geom_line(aes(y=sum_real_val))+
facet_wrap("val_grp")
## but alas the result total doesn't agree with the reported total
## nb the imputed values for each group don't necessarily have to agree with the real values.

Answer 1

It's hard to know what conclusions to draw from the incomplete dates. One simple assumption could be to take the last share and extrapolate that into the future:
default_share <- df %>%
count(rn, grp, wt = !is.na(reported_val)) %>%
count(rn, n) %>%
filter(nn == max(nn)) %>%
slice_max(rn) %>%
left_join(df) %>%
mutate(share = real_val / sum(real_val)) %>%
select(rn, grp, share)
df %>%
group_by(rn) %>%
mutate(result_val = if_else(rn > default_share$rn[[1]],
sum(real_val) * default_share$share[rn == rn],
real_val)
) %>% ungroup() %>%
select(-reported_val) %>%
pivot_longer(-c(grp:rn)) %>%
ggplot(aes(rn,value))+
geom_area(aes(fill=grp))+
facet_wrap(~name)