COVID-19

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COVID-19 Pandemic in South America

View the Project on GitHub bgonzalezbustamante/COVID-19-South-America

COVID-19 in South America Tracker

Project Status: Inactive – The project has reached a stable, usable state but is no longer being actively developed; support/maintenance will be provided as time allows. Update DOI R Jekyll

This online tracker was deprecated in mid-May 2020 to focus on a paper which evaluates early projections and governmental responses to COVID-19 epidemic in South America. This paper was published in World Development journal (DOI: 10.1016/j.worlddev.2020.105180). See its demonstration for R.

This was an online tracker of COVID-19 coronavirus outbreak -a pandemic- in South America deployed on GitHub between March 2020 and mid-May 2020, which presented the epidemic curves based on the incidence in the region (see epicurve stratified by country). Furthermore, estimated R, explored serial interval (SI) distribution, and future incidence simulations are fitted by case.1 The countries reviewed are as follows:

Moreover, Spain (ESP), the United Kingdom (GBR), and Italy (ITA) are computed for comparative purposes (see epicurve stratified by country).2

The charts and estimations are updated after the close of the day in Greenwich Mean Time based on the Johns Hopkins University CSSE COVID-19 Dataset (see repository).

The materials can be downloaded from the OSF-Project (DOI: 10.17605/OSF.IO/6FM7X). This site will be updated on a regular basis, see the changelog.

About Me

I am completing my DPhil (PhD) dissertation in the Department of Politics and International Relations and St Hilda’s College at the University of Oxford, United Kingdom.

Further details on my website bgonzalezbustamante.com.

Notes

  1. R is specifically the effective reproduction number (Re), which is calculated on a daily incidence basis. Therefore, it allows us to measure the public health systems response (see Cori et al., 2013; see also Thomson et al., 2019). On the other hand, SI is the time interval between the symptoms in each case and the onset of them in secondary cases because of transmission. Finally, the projections are 1,000 future incidence simulations by case using maximum-likelihood estimations or log-incidence models, depending on the stage of the growth phase. For further details, see the technical note. ↩
  2. At the beginning of the project, I was scraping data from the health services and ministries of each country. Since March 17, 2020 (see the changelog), to standardise the measurement and improve -and automatise- the data collection process, I have connected these analyses to the Johns Hopkins University CSSE COVID-19 Dataset. ↩