This was my personal project for visualizing and analyzing COVID-19 data for Slovenia during the first wave of the pandemic in spring 2020. I started it on 13. march (beginning of the first lockdown), and kept updating and improving it until april, when Sledilnik became good enough to replace most of it.
Raw data (number of cases and tests) was gathered from various sources, then combined, cleaned and analysed in Python. The visualizations were done in Python inside Jupyter Notebook using pandas, matplotlib and seaborn. Predictions were calculated using SIR-X model. At first I was exporting the results as a pdf of the entire notebook, but later switched to exporting just the images.
Below you can see example visualizations for 1. april. Final analysis for the last day changes were made (4. april 2020) is in the Korona.ipynb notebook, and the corresponding images are in the images folder. Check out the archive folder for earlier data. Note that everything is in Slovenian.
- NIJZ: Spremljanje koronavirusa SARS-CoV-2 (COVID-19)
- NIJZ: Dnevno spremljanje okužb s SARS-CoV-2 (COVID-19) [graph]
- Data published by the government on gov.si and twitter.
- Luka Renko and others: COVID-19 Slovenija
Cumulative & daily new cases (linear and log scale), growth analysis (new cases as percentage of all, doubling time)
Cumulative & daily new (linear and log scale), daily and overall positive percentage
Hospitilized patients, ICU patiens and cumulative deaths, both absolute and as a percentage of cumulative infections
Cumulative and daily new cases by age group (total and per 100k population), both absolute and relative
Cumulative and daily new cases by region (total and per 100k population), both absolute and relative
Predictions for the next week using SIR-X model
