Bilingual (EN-UK) corpus acquired from Wikipedia on health and COVID-19 domain (2nd May 2020)

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset deposited in PRIDE database with accession number: PXD107710

ISA-Tab annotation for the "SARS-CoV-2 infected host cell proteomics reveal potential therapy targets" publication.

Github repository: https://github.com/ISA-tools/PXD017710

This is part of an effort to (re-)annotate: https://dx.doi.org/10.21203/rs.3.rs-17218/v1

Additional work done as part of:

https://github.com/virtual-biohackathons/covid-19-bh20

https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData

Proteomics data

Available from PRIDE at https://www.ebi.ac.uk/pride/archive/projects/PXD017710 and [MassIVE/CCMS Maestro+MSstats reanalysis of MSV000085096 / PXD017710]

ISA-Tab representation:

Rationale: Demonstrate suitability of the ISA format for representing MS based protein profiling experiment with more granularity and details, thus providing a better representation of the experiment design. The formatting and re-annotation are based on information extracted from: - the original publication - the supplementary tables available from the publishers site - the 'filtered-results.csv' helper file as supplied to @sneumann during the HUPO-PSI meeting March 2020

Viewing the ISA-tab formatted and re-annotated PXD017710 with ISATab-Viewer

Viewing the ISA-tab formatted and re-annotated PXD017710 locally, do the following:

python -m http.server 8000

Then point your browser to http://0.0.0.0:8000/isaviewer-demo.html

Curation tasks performed:

• initial structure of the study design in ISA format:

• linkage of Proteome and Translatome data (supplementary material) to ISA assay tables (via Derived Data File)

• processing the Proteome and Translatome data (supplementary material) with python pandas library to generate the following csv files:

  • proteome_intensities_long_table_ggplot2.txt
  • proteome_diffanal_ratio_pvalue_long_table_ggplot2.txt
  • translatome_intensities_long_table_ggplot2.txt
  • translatome_diffanal_ratio_pvalue_long_table_ggplot2

  The files are long table corresponding to a melt on the Excel file originally generated by the users and can be readily loaded in R ggplot2 library for graphical representation. The statistical relevant elements have been annotated with the STATO ontology and the tables comply with a Frictionless.io Data Package. The jupyter notebook for the transformation is available.

• conversion of raw data to mzML format: detailed in https://github.com/ISA-tools/PXD017710

install docker: bash >brew update >brew install docker

sign in to docker bash >docker start >docker login

pull docker container for ProteoWizard: ```bash

docker pull chambm/pwiz-i-agree-to-the-vendor-licenses ```

:warning: be sure to sign-up and login to https://hub.docker.com/

in order to be able to reach

https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses

run the pwiz tool from the container over the raw data: bash docker run -it --rm -e WINEDEBUG=-all -v /Users/Downloads/PXD017710/raw/:/data chambm/pwiz-skyline-i-agree-to-the-vendor-licenses wine msconvert /data/*.raw --mzML

• ontology markup for:
  • declaration of independent variables as ISA Study Factors:{biological agent, dose, time point, replicate} ->OBI
  • Taxonomic information (host cells and virus) -> NCBITaxonomy
  • Cell line: CaCo-2 cells -> Cell Line Ontology
  • Disease: Colon Cancer -> Human Phenotype Ontology
  • MS specific aspect (TMT reagent, instrument ... ) -> PSI-MS
  • Statistical Tests -> STATO

Unresolved curatorial issues:

1. ambiguities related to Tandem Mass Tag labelling protocol

   • the publication mentions TMT11 (see Figure 2 in https://www.researchsquare.com/article/rs-17218/v1)
   • the information available from PRIDE mentions TMT6 (https://www.ebi.ac.uk/pride/archive/projects/PXD017710) This may require another round of annotation on the TMT agents and fractions in the ISA a_assay representation

2. SARS-Cov2 isolate: no clear NCBI Taxonomic anchoring and unclear origin: -> the markup is made to the parent class (as of 06.04.2020)

Release and packaging as a BDBAG:

The tgz file associated with this upload has been producing using https://github.com/fair-research/bdbag. It contains several manifest files detailing metadata and data files, providing md5 and sha256 checksums.

Github repository: https://github.com/ISA-tools/PXD017710

This project contains anonymised data, R scripts for analysis and supplementary material for this secondary analysis of the Bug Watch prospective community cohort study. In this analysis we estimated the incidence of cough or fever in England in 2018-2019. We then estimated the COVID-19 diagnostic testing rates required in the UK for baseline cough or fever cases for the period July 2020-June 2021. This was explored for different rates of the population requesting tests and four COVID-19 second wave scenarios. Estimates were then compared to current national capacity.