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https://github.com/dirkschumacher/covid-19-indicators
Epidemiological Indicators (CFR, incubation period, serial interval) for COVID-19
https://github.com/dirkschumacher/covid-19-indicators
coronavirus covid-19
Last synced: 19 days ago
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Epidemiological Indicators (CFR, incubation period, serial interval) for COVID-19
- Host: GitHub
- URL: https://github.com/dirkschumacher/covid-19-indicators
- Owner: dirkschumacher
- License: cc0-1.0
- Created: 2020-03-17T21:03:12.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T21:35:36.000Z (almost 5 years ago)
- Last Synced: 2025-01-15T19:11:39.065Z (24 days ago)
- Topics: coronavirus, covid-19
- Homepage:
- Size: 94.7 KB
- Stars: 6
- Watchers: 3
- Forks: 2
- Open Issues: 5
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE, message=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
echo = FALSE,
comment = "#>"
)
suppressPackageStartupMessages(library(meta))
```
# covid-19-indicators
Epidemiological Indicators of COVID-19. If you would like to add information, have ideas or found a mistake, send a PR or open an issue.
## [Case Fatality Rate](https://en.wikipedia.org/wiki/Case_fatality_rate)
```{r,message=FALSE}
suppressPackageStartupMessages(library(dplyr))
cfr_data <- readr::read_csv("data/COVID-19-Indicators - CFR.csv")
from_percent <- function(x) {
as.numeric(gsub("%", "", x, fixed = TRUE)) / 100
}
cfr_data <- mutate(cfr_data,
Estimate = from_percent(Estimate),
UI_Lower = from_percent(UI_Lower),
UI_Upper = from_percent(UI_Upper)
)
```
```{r}
library(scales)
suppressPackageStartupMessages(library(glue))
cfr_data %>%
arrange(desc(Published)) %>%
transmute(
`Point Estimate` = percent(Estimate, accuracy = 0.01),
`Uncertainty Interval` = glue("({percent(1 - UI_alpha)} {UI_Type}: {percent(UI_Lower)}-{percent(UI_Upper)})"),
N = N,
Place = Place,
Time = Time,
`Age Group` = Age_Group,
Published = Published,
Reference = glue("[{Reference}]({Reference_Url})")
) %>%
knitr::kable()
```
## [Incubation Period](https://en.wikipedia.org/wiki/Incubation_period)
| Estimate in Days | Type | Uncertainty Interval | N | Place/Time | Published | Reference |
| -------------: |-------------|-------------| -----:|-----|-----|-----|
| 5.1 | Median | (95% CI: 4.5-5.8) | 181 | Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. | 2020-03-10 | [(Lauer et al., 2020), Ann Intern Med.](https://www.ncbi.nlm.nih.gov/pubmed/32150748) |
| 11.5 | 97.5% quantile | (95% CI: 8.2-15.6) | 181 | Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. | 2020-03-10 | [(Lauer et al., 2020), Ann Intern Med.](https://www.ncbi.nlm.nih.gov/pubmed/32150748) |
| 5.0 | Mean | (95% CrI: 4.2-6.0) | 52 | China?, excluding Wuhan residents | 2020-02-17 | [(Linton et al., 2020), J. Clin. Med..](https://www.mdpi.com/2077-0383/9/2/538) |
| 5.6 | Mean | (95% CrI: 5.0-6.3) | 158 | China?, including Wuhan residents | 2020-02-17 | [(Linton et al., 2020), J. Clin. Med..](https://www.mdpi.com/2077-0383/9/2/538) |
| 5.2 | Mean | (95% CI: 4.1-7.0) | 425 | Wuhan, Hubei Province, China, in December 2019 and January 2020 | 2020-01-29 | [(Qun et al., 2020), NEJM](https://www.nejm.org/doi/full/10.1056/NEJMoa2001316) |
| 12.5 | 95% quantile | (95% CI: 4.1-7.0) | 425 | Wuhan, Hubei Province, China, in December 2019 and January 2020 | 2020-01-29 | [(Qun et al., 2020), NEJM](https://www.nejm.org/doi/full/10.1056/NEJMoa2001316) |
## [Serial Interval](https://en.wikipedia.org/wiki/Serial_interval)
* "[...] 12.6% of the serial intervals in our sample were negative." [Ref](https://doi.org/10.3201/eid2606.200357). Models need to account for negative serial intervals.
| Estimate in Days | Type | Uncertainty Interval | N | Place/Time | Published | Reference |
| -------------: |-------------|-------------| -----:|-----|-----|-----|
| 3.96 | Mean | (95% CI: 3.53-4.39) | 468 | Mainland China outside of Hubei Province during January 21–February 8, 2020 | 2020-03-19 | [(Du et al., 2020), Emerging Infectious Diseases](https://doi.org/10.3201/eid2606.200357) |
| 4.75 | SD | (95% CI: 4.46-5.07) | 468 | Mainland China outside of Hubei Province during January 21–February 8, 2020 | 2020-03-19 | [(Du et al., 2020), Emerging Infectious Diseases](https://doi.org/10.3201/eid2606.200357) |
| 4.6 | Median | (95% CrI: 3.5-5.9) | 18 | ? | 2020-03-02 | [(Nishiura et al., 2020), Int J Infect Dis.](https://www.ncbi.nlm.nih.gov/pubmed/32145466) |
| 4.4 | Mean | (95% CI: 2.9−6.7) | 21 | Hong Kong from January 16 to February 15, 2020 | 2020-02-25 | [(Zhao et al., 2020), medrxiv](https://www.medrxiv.org/content/10.1101/2020.02.21.20026559v1.article-info) |
| 7.5 | Mean | (95% CI: 5.3-19) | 425 | Wuhan, Hubei Province, China, in December 2019 and January 2020 | 2020-01-29 | [(Qun et al., 2020), NEJM](https://www.nejm.org/doi/full/10.1056/NEJMoa2001316) |
### Pooled Mean
I used the `meta` package to pool the various reported means of the serial interval. Please be careful as this might not be the right way to pool means of serial intervals. But it is something. If you have any suggestions leave an issue.
```{r,message=FALSE,fig.width=10}
# pooling effect sizes
studies <- tibble::tribble(
~label, ~n, ~mean, ~ci_lower, ~ci_upper,
"Qun et al. 2020", 425, 7.5, 5.3, 19,
"Nishiura et al., 2020", 28, 4.7, 3.7, 6.0,
"Zhao et al. 2020", 21, 4.4, 2.9, 6.7,
"Du et al., 2020", 468, 3.96, 3.53, 4.39
)
m <- metagen(
studies$mean,
n.e = studies$n,
lower = studies$ci_lower,
upper = studies$ci_upper,
studlab = studies$label,
level.ci = 0.95,
sm = "MD",
method.tau = "REML"
)
forest(m)
```
## Proportion Pre-Symptomatic Transmission
| Estimate | Uncertainty Interval | N | Place/Time | Published | Reference |
| -------------: |-------------| -----:|-----|-----|-----|
| 46% | - | 391 | Shenzen, China | 2020-04-01 | [(Liu et al., 2020), Wellcome Open Res](https://doi.org/10.12688/wellcomeopenres.15788.1) |
| 44% | (95% CI: 25–69%) | 77 | Guangzhou, China | 2020-03-15 | [(Xi et al., 2020), Nat Med](https://doi.org/10.1038/s41591-020-0869-5) |
| 48% | (95% CI: 32-67%) | - | Singapore | 2020-03-05 | [(Ganyani et al., 2020), medrxiv](https://doi.org/10.1101/2020.03.05.20031815 ) |
| 62% | (95% CI: 50-76%) | - | Tianjin, China | 2020-03-05 | [(Ganyani et al., 2020), medrxiv](https://doi.org/10.1101/2020.03.05.20031815 ) |
## WHO Publication Database
The WHO maintains a database of all COVID-19 related studies. This is a good starting point if you want to contribute instead of text searches on pubmed (what I did in the beginning).
* https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov