[Turing Data Story] Investigating non official indicators of the Covid19 epidemic in Venezuela

[crangelsmith]

Story description

Please provide a high level description of the Turing Data Story

The idea of this story is to investigate non-official sources of data (google searches, Twitter, GofoundMe campaigns) that can be used to gauge the magnitude of the epidemic in countries where official data is not available or reliable. In this particular case we'll focus on Venezuela, however this can be extended to other countries/regions.

Which datasets will you be using in this Turing Data Story?

• Google trends for keywords.
• Twitter data, following hashtags and key words.
• GoFoundMe campaigns to raise funds for Covid19 treatments.
• OurWorld in Data and IHME model.

Additional context

This story is being develop as a hands-on data project for a master course of LA-CoNGA physics. The data analysis is done by @alfonso-navas and @bonaldee with the supervision of @crangelsmith and support of @HarveyMaddocks.

Some background info:

• https://www.caracaschronicles.com/2021/04/15/gofundme-venezuelans-lifesaver-against-covid-19/
• https://twitter.com/CamilaRangelS/status/1376286680584482817?s=20
• https://twitter.com/LuisCarlos/status/1379203638082613253?s=20

Ethical guideline

Ideally a Turing Data Story has these properties and follows the 5 safes framework.

• [x] The analysis you produce is openly available and reproducible.
• [x] Any data used are open and have an explicit licence, provenance and attribution.
• [x] Any data used are not personal data (i.e. the data is anonymous or anonymised).
• [x] Any linkage of datasets in your data story does not lead to an increased risk of the personal identification of individuals.
• [x] The Story must be truthful and clear about any limitations of analysis (and potential biases in data).
• [x] The Story will not lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice.

Note from the authors:

• The story will be using data scraped from GoFoundME with code from a private repo. GoFoundMe campaigns can have sensitive personal information, such as names, health information, etc. We will make sure that the data used in the story won't contain any if this information, even if this means reducing transparency of the data creation step, like not making public the code of the web scraper and aggregating data.

Current status

• [x] Scoping of the story
• [x] Write story outline
• [ ] Add material to the story notebook
• [ ] Combine materials into a readable story
• [ ] Proofread
• [ ] Request reviews
• [ ] Address reviews
• [ ] Merge to master branch.

Updates

[crangelsmith]

Update of this story: most of the work has been done by @alfonso-navas and @bonaldee, and lived in here. @crangelsmith and @HarveyMaddocks are supposed to translate the work to English and run the analysis with the latest data, this should happen in the next couple of months.

@helendduncan has kindly agreed to be one of the reviewers of this story.