CovidGer

The goal of CovidGer is to provide relatively easy access to the data used in “Inference under superspreading” by Patrick Schmidt.

The repository contains additional code on the generation of the data files in the data-raw folder.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Schmidtpk/CovidGer")

Case data by the rki

This is a basic example which shows you how to use the case data by the rki. See ?rki_new for the data source.

The following example aggregates German wide cases and deaths by symptom onset.

library(CovidGer)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.1.3
library(tidyr)
#> Warning: package 'tidyr' was built under R version 4.1.3
library(ggplot2)

df<-rki_new %>%
  dplyr::filter(!is.na(Refdatum))%>%
  group_by(Refdatum,age)%>%
  summarise(
    positive= sum(AnzahlFall[Neuer.Fall%in%c(0,1)]),
    deaths = sum(AnzahlTodesfall[Neuer.Todesfall%in%c(0,1)])
  ) %>%
  rename(
    date=Refdatum,
  )%>%pivot_longer(c(positive,deaths))

ggplot(df,aes(x=date,y=value))+
  geom_point()+
  geom_line()+
  facet_grid(name~age,scale="free_y")+
  xlab("date of symptom onset")

Delay from Symptom onset to reporting to health departement

The following code computes the delay from symptom onset to reporting. Symptom onset is given in Refdatum and reporting date in Meldedatum.

rki_new %>%
  dplyr::filter(Refdatum>=as.Date("2020-03-01"),
                Refdatum<as.Date("2020-09-01"))%>%
  mutate(
    delay = as.numeric(Meldedatum-Refdatum),
    delay = if_else(delay>14,14,delay),
    delay = if_else(delay<(-7),-7,delay)) %>%
  group_by(Refdatum)%>%
  summarise(
    delaym = mean(delay,na.rm=TRUE),
    delay1 = quantile(delay,na.rm=TRUE,probs = .1),
    delay9 = quantile(delay,na.rm=TRUE,probs = .9))%>%
  rename(date = Refdatum)%>%
  ggplot(aes(x=date,y=delaym))+
  geom_ribbon(aes(ymin=delay1,ymax=delay9),alpha=.2)+
  geom_line()

Other data

The Package also contains data on population statistics (Regionaldatenbank) in regionaldatenbank, on location specific weather from the German Weather Services (DWD) in weather_dwd, and on policy interventions in interventions.list and interventions.

The intervention data was generated in a spreadsheet, which is directly accessible here.