How to identify contacts that converted into new cases?

[MallerBeach]

Dear all,

a further question for clarification regarding SORMAS ÖGD. How to identify contacts that converted into new cases?

The table contacts contains the variable contactstatus, but in our cases it does not seem to be a valid representation of the current dynamic of the COVID19 pandemic bearing in mind the Variants of Concern.

Any clearification would be appreciated!

[VitaliHZI]

Hey @MallerBeach, I think this GitHub Repo is not the right space for this question. Maybe you could write an Email to sormas-covid@helmholtz-hzi.de and the support there can help you with this question.

BR Vitali

[MallerBeach]

@VitaliHZI Thanks for that hint.

I am not expecting a code review....below I am just sharing some thoughts...

#### Using the example-data from the statistics-export

# sormas_path <-  # ...path to data

library(data.table)
library(dplyr)

##### Cases -----
cases <- fread(paste0(sormas_path, "cases.csv"), skip = 1, encoding = "UTF-8") %>%
  tibble() %>%
  filter(deleted == "f" & !caseclassification == "NO_CASE")

##### Contacts -----
contacts <- fread(paste0(sormas_path, "contacts.csv"), skip = 1, encoding = "UTF-8") %>%
  tibble() %>%
  filter(deleted == "f" & !is.na(caze_id))

##### Convertd contacts -----
converted_contacts <- filter(contacts, contactstatus == "CONVERTED") %>%
  group_by(person_id) %>%
  mutate(first_conversion = case_when(creationdate == min(creationdate) ~ 1)) %>%
  select(first_conversion, person_id, creationdate, contactstatus) %>%
  arrange(person_id, creationdate) %>%
  filter(first_conversion == 1) 

##### Converted cases that were contacts -----
cases <- mutate(cases, converted_contact = case_when(person_id %in% converted_contacts$person_id ~ "converted_contact",
                                                              TRUE ~ "case"))

table(cases$converted_contact)