midas-network / covid-19 Goto Github PK

https://www.cdc.go.kr/board/board.es?mid=a20501000000&bid=0015&list_no=365794&act=view#

https://www.cdc.go.kr/board/board.es?mid=a20501000000&bid=0015&list_no=365805&act=view#

On https://github.com/midas-network/COVID-19/blob/master/software_tools/software_catalog.csv,
maybe column "URL_original" should be moved to be the 3rd column. Just my 2-cent. Feel free to close it without any change.

Why don't you put Hong Kong in the tie-in section in China?

General question:

Are these parameters being sourced on a pull request basis or is someone actively reading through the literature to get the estimates?

Because over the past few days, I've seen a few China CDC reports which are very comprehensive. It would be useful to include them if they're not already there. For some of these estimates, having an 'N' (number of patients) will be useful and somewhat essential when we try to use the most reliable one.

Finally, I am more comfortable with seeing 'XYZ et al.' as a way of citing them rather than the University (since a lot of the research is collaborative).

The dispersion in the incubation period is listed under the "Dispersion" category, which I believe is meant to show dispersion in transmission.

The first numbers in the estimates readme.md is 'Cumulative case counts'. My understanding is these are total infections (including undetected ones).

Firstly, these have an 'expiration date', and date when the estimate was done. Also what is being estimated varies widely across the papers. So would be better to have something like 'reporting rate' or 'detection rate', which has better utility for model-builders.

Source : https://en.wikipedia.org/wiki/Template:Notable_flu_pandemics

For R0 : https://en.wikipedia.org/wiki/Template:Notable_flu_pandemics#cite_note-4

• Biggerstaff, Matthew; Cauchemez, Simon; Reed, Carrie; Gambhir, Manoj; Finelli, Lyn (2014-09-04). "Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature". BMC Infectious Diseases. 14 (1): 480. doi:10.1186/1471-2334-14-480. ISSN 1471-2334. PMC 4169819. PMID 25186370.

See also your section.

covid141,NA,medRxiv,ascertainment rate,NA,proportion,Japan,Unspecified,country,Unspecified,2020-28-02,Unspecified,0.44,confidence level

Date is formatted as YDM rather than YMD as all other dates. (2020-28-02 instead of 2020-02-28)

The following papers contain parameter estimates for days from symptom onset to hospitalization that could be added to the dataset here:

• https://www.medrxiv.org/content/10.1101/2020.05.18.20103390v1
• https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e3.htm

I'm new to GitHub, so apologies in advance if this is not the right channel or format to raise this.

Hey folks,
I'm collecting the official news provided by Singapore Gov. I did some text analysis and generate the cluster view of Singapore cases. I'm willing to contribute and get to know more and help to generate graph-structured data during COVID19. Please find the repo here.

https://github.com/lushl9301/Statistics-of-Singapore-COVID-19-Cases

Willing to adjust my data format so that to contribute to the data pool.

Thanks and Regards

I'm running an hourly-updated system producing future-prediction coefficients. The output is JSON format.

An example showing it in action is here: https://cryptinc.com/covid19/covid19_predictor.html

It would benefit from someone with JavaScript and Charting/Mapping skills turning that data into an interactive tool to help people see what is in their immediate future. So far, it's proving to be accurate to within 3% when looking forward a few days, with good accuracy on mid term predictions as well.

A dozen curated global sources feed the back ends.

I noticed some weird values of the "death" variable in the line listing data. For example, the 27th case in Japan died at 02/13 but the death variable is 1 in the file 2020_02_18_1800EST_linelist_NIHFogarty.csv and 2020_02_19_1800EST_linelist_NIHFogarty.csv but becomes 1581552000 in all the following files starting from 2/20 to 3/16. There are 50-ish cases with similar issues though with different strange numbers. But interestingly for each case the weird number is consistent based on my partial observation.

Is this weird number indicating a death? My guess is that this input is recorded as the death date but then transformed into an integer accidentally.

Amazing work aggregating the parameter estimates, but for the casual viewer it would be nice to be able to /see/ these values. Perhaps something like this figure could be added to the README.

The code for this figure should be easy to adapt to the other parameters as well if you are interested.

library(dplyr)
library(reshape2)
library(ggplot2)


country_names <- c("China", "Iran", "Singapore")

x <- read.csv("estimates.csv",
              stringsAsFactors = FALSE,
              header = TRUE) %>%
    select(id,
           peer_review,
           name,
           abbreviation,
           units,
           country,
           value,
           lower_bound,
           upper_bound,
           title_publication) %>%
  filter(abbreviation == "R0") %>%
    filter(lower_bound != "Unspecified",
           value != "Unspecified",
           country != "Unspecified") %>%
    mutate(value = as.numeric(value),
           lower_bound = as.numeric(lower_bound),
           upper_bound = as.numeric(upper_bound))

id_order <- x$id[sort.int(x$value, index.return = TRUE)$ix]

nice_theme <- theme(
    panel.background = element_blank(),
    panel.grid.minor.y = element_blank(),
    axis.line = element_line(colour = "black"),
    axis.title = element_text(size = 22),
    axis.text = element_text(size = 16),
    plot.title = element_text(size = 32),
    plot.subtitle = element_text(size = 22),
    legend.background = element_rect(colour = "black"),
    legend.title = element_text(size = 22),
    legend.text = element_text(size = 16),
    legend.key = element_rect(fill = "white")
    )

plot_df <- x
plot_df$plot_id <- factor(plot_df$id, levels = id_order)
plot_df$plot_peer_review <- sapply(plot_df$peer_review, is.na)

ggplot(plot_df,
       aes(x = plot_id,
           y = value,
           ymin = lower_bound,
           ymax = upper_bound,
           colour = country,
           shape = plot_peer_review)) +
  geom_pointrange() +
    geom_hline(yintercept = 1,
               linetype = "dashed") +
    labs(x = "Estimate Identifier",
         y = "Estimate",
         title = "R-naught",
         subtitle = "Basic Reproduction Number",
         colour = "Country",
         shape = "Peer Review\nStatus") +
    coord_flip() +
    nice_theme


## scale_factor <- 2
## ggsave("demo.png",
##        height = scale_factor * 14.8,
##        width = scale_factor * 10.5,
##        units = "cm")

I've been doing my best tracking online modeling efforts here:

https://github.com/vejmelkam/covid19-models

Maybe it would be useful as a reference, or just copy it here if interested. So far I was unfortunately unsuccessful in getting any help mapping modeling efforts in different languages (other than English, Czech, Slovak that is).

Regular/Daily Briefings:

• China: http://www.nhc.gov.cn/yjb/s2907/new_list.shtml
• Hubei Province: http://wjw.hubei.gov.cn/fbjd/dtyw/
• Hong Kong: https://www.dh.gov.hk/english/press/press.html and https://www.dh.gov.hk/tc_chi/press/press.html
• Thai MOPH: https://ddc.moph.go.th/viralpneumonia/eng/news.php - also a thai version
• Macau: https://news.gov.mo/search?5
• WHO sitreps: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports

22 Jan 2020 - Wuhan local health commission announced that all briefings would go through Hubei Province rather than the local commission.

Periodic:

• WHO: https://www.who.int/csr/don/archive/disease/novel_coronavirus/en/
• WHO general DON: https://www.who.int/csr/don/en/
• CDC: https://www.cdc.gov/media/dpk/diseases-and-conditions/coronavirus/coronavirus-2020.html
• WHO guidance: https://www.who.int/health-topics/coronavirus
• promedmail.org

1. The Oxford set has the most line listings but is not current. Is there another source where a newer version can be found, or is it no longer being maintained?
2. The NIH Fogerty line listings contains 1400 rows. Is there an explanation somewhere of what this subset of cases consists of?

Thank you very much

Would be good!
Cheers!

Most of the content of parameter_estimates/2019_novel_coronavirus is from 2020-02-1X, mid february. Did you move the effort elsewhere ? @LucieContamin

There is a collection of sources for data on testing at the following page:

https://ourworldindata.org/coronavirus-testing-source-data

if this sort of data could be included that would be useful to understand the observation process.

On https://github.com/midas-network/COVID-19/blob/master/software_tools/software_catalog.csv
can you help change creator_contact for both of NSSAC dashboards to [email protected]?

If you don't mind, please update the version for our surveillance dashboard to 0.8.6.

https://www.medrxiv.org/content/10.1101/2020.02.12.20022467v1

Thank you!

• Dates on files are publication dates
• Date format: DD Mon YYYY - e.g., 22 Jan 2020
• Use Google translate for machine translation.
• Publish in English and original language.
• Include a text file of PDF case reports and briefings when possible.
• Files rather than links for briefings, publications etc. Include origin link to facilitate checking for updates.
• Try to change special characters in file names. IE write out "beta" etc

As of 2020/03/25 17:30 commas are being used in the status table on the Ontario Ministry of Health's page which is breaking the CSV format (e.g Cases_in_Ontario_2020-03-26.csv)

String quoting of CSV fields could be added to address this

nCoV-2019 Situation Reports from Johns Hopkins University Center for Health Security (metadata)

The metadata link is leading to 404 error

20200927 Data is the Same as 20200926 Data.
Both 9/26 and 9/27 have the exactly same data, ending at 9/25/20.
9/27 is missing new data for 9/26/20.
Please review.

As it is, it's not clear whether the value for any of the estimates (but I was looking in particular at the incubation period) are means or medians of distributions or exactly what the value represents.