Here we explain our motivation for putting together the Open Science COVID Analysis (OSCOVIDA) framework.

1. Understanding the pandemic

The reporting in the media on the COVID19 situation is not always providing sufficient context to interpret the numbers: we hear many news reports about how many people have been confirmed to be infected by the virus, or have died on a particular day.

The relevant questions are though: how do these numbers compare to yesterday, and the week before? Can we see and understand how quickly infections are spreading? Can we see if the containment measures of people staying at home, schools and universities closing etc are showing any effect and if so, how strong is it? What can we learn from countries that have managed to reduce the number of new infections?

With the plots and data available here, we hope to contribute to this.

Discussion and contributions are welcome.

2. Preparing for living with COVID19 -- the later stages

Once the epidemic growth of infections is brought under control, we will need to find a fine balance between containment measures (such as social distancing, closure of schools, restaurants, shops, etc) and permitting work and live as was possible before the pandemic to avoid repeated exponential growth of infections.

In the absence of a COVID19 vaccine, it is possible that this needs to carry on for many years to come. OSCOVIDA provides a tool that can help to assess the effectiveness of containment measures, and can help to understand the size and dynamics of new, hopefully fewer and more localised, COVID19 outbreaks. We want to enable the interested citizen to inspect and understand the data to a level that as barely possible with the reports in the media.

3. Enable citizen science and reproducible analysis

Headlines that attract attention may not deliver useful information. We would like to offer complementary information and tools with OSCOVIDA:

4. Simplify use of the existing data sets for experts

Our project can be used to get access to the COVID19 data from the convenience of a Python function; this may help other scientists to build better models or data analysis.

(Contributions to our plots and software are welcome.)

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