https://github.com/epiforecasts/covid19.slovakia.mass.testing
Data and code accompanying the preprint "The effectiveness of population-wide screening in reducing SARS-CoV-2 infection prevalence in Slovakia"
https://github.com/epiforecasts/covid19.slovakia.mass.testing
Last synced: 11 months ago
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Data and code accompanying the preprint "The effectiveness of population-wide screening in reducing SARS-CoV-2 infection prevalence in Slovakia"
- Host: GitHub
- URL: https://github.com/epiforecasts/covid19.slovakia.mass.testing
- Owner: epiforecasts
- License: other
- Created: 2020-11-30T00:01:32.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-03-01T16:53:47.000Z (over 5 years ago)
- Last Synced: 2025-07-18T03:18:42.736Z (11 months ago)
- Language: R
- Size: 32 MB
- Stars: 2
- Watchers: 3
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
Title <- "The effectiveness of population-wide screening in reducing SARS-CoV-2 infection prevalence in Slovakia"
```
# The effectiveness of population-wide screening in reducing SARS-CoV-2 infection prevalence in Slovakia
This repository contains the data and code for our manuscript:
Pavelka S, Van-Zandvoort K, Abbott S, Sherratt K, Majdan M, CMMID COVID-19 working group, Jarčuška P, Krajčí M, Flasche S*, Funk S* (*: equal contribution), _`r Title`_. Available at .
### How to download or install
You can download the compendium as a zip from from this URL: .
Or you can install this compendium as an R package, `covid19.slovakia.mass.testing`, from GitHub with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
remotes::install_github("sbfnk/covid19.slovakia.mass.testing")
```
### Included data sets
The repository contains three data sets:
The testing data set `ms.tst` can be loaded with
```{r mstst, eval = FALSE}
data(ms.tst)
```
Incidence of cases confirmed by PCR per county `PCR.inc` can be accessed with
```{r pcrinc, eval = FALSE}
data(PCR.inc)
```
The `Rt.county` data set contains the estimated median reproduction number in each county on 22 October 2020.
```{r Rt.county, eval = FALSE}
data(Rt.county)
```
This data set can be re-created using the The [EpiNow2](https://epiforecasts.io/EpiNow2/) R package by running (noting that it can take a long time to run depending on the hardware available).
```{r reproduce_r, eval = FALSE}
source(here::here("data-raw", "scripts", "rt.r"))
source(here::here("data-raw", "scripts", "convert_data.r"))
```
The [EpiNow2](https://epiforecasts.io/EpiNow2/) R package that is used to estimate the reproduction numbers uses generation times and delay distributions saved in `data-raw/data`. They can be re-generated by running.
```{r reproduce_r_distributions, eval = FALSE}
source(here::here("data-raw", "scripts", "rt-distributions.r"))
```
The Google mobility data set for Slovakia `mob.slo` visualised in Supplementary Figure S4 can be accessed with
```{r mob.slo, eval = FALSE}
data(mob.slo)
```
### Figures and tables
To regenerate Table 1, run
```{r county_table, eval = FALSE}
county_table("table1.pdf")
```
To regenerate Fig. 1, run
```{r pcr_incidence, eval = FALSE}
pcr_incidence()
```
To regenerate Fig. 2, run
```{r risk_ratios, eval = FALSE}
rr <- risk_ratios()
rr$figures$a
rr$figures$b
rr$figures$c
rr$tables
```
To generate Table S1 and estimate the adjusted prevalence ratio, run
```{r regression, eval = FALSE}
r <- regression()
```
To regenerate Fig. S4, run
```{r mobility, eval = FALSE}
mobility()
```
To regenerate Fig. S6, run
```{r bed_occupancy, eval = FALSE}
bed_occupancy()
```
To regenerate Fig. S7, run
```{r prevalence, eval = FALSE}
prevalence()
```
### Minimum specificity
To estimate minimum specificity, run
```{r min_spec}
estimate_min_specificity()
```