Rapport sur une analyse de série temporelle d'un étudiant de CY Tech

1. COVID Analysis
Maria Roulet Sterkel, Elie Brosset, Sirine Haddouche
Cases and deaths April and May 2020

2. Introduction
Plot representing new cases of COVID in 6 different countries between April
and May 2020

3. Plot representing new deaths of COVID in 6 different
countries between April and May 2020

4. Coronavirus and Media
https://www.lefigaro.fr/sciences/covid-19-le-taux-de-positivite-reste-en-hausse-malgre-le-traditionnel-repli-du-week-end-20200927

5. The cyclicality of the new cases of the virus
First interpretation leads to a
question: why do we not study
the same thing for specific
countries ?
7 days

6. The key of the analysis: the ratios
Summing all rows per week number
Summing all rows per country
Time analysis (Plotly)
Location analysis (Choropleth map)

7. Problematic of the analysis
● To what extent ratios impact the location of the virus in the world ?
● To what extent ratios evolve through the time ?

8. Methods
● R studio
● Time Series packages (zoo, xts)
● Data visualization packages (plotly, leaflet)
● First the whole world, then specific cases (countries)
● First the whole period, then 2 months

9. Results
France

10. USA
UK

12. Choropleth map demo

13. Discussion
Difference between countries and over time

14. Difference between countries
● Such differences are due to certain unique parameters such as
- Total population
- Measures taken
- Amount of tests done
- Culture
- HDI

15. Difference over time
● Geographical impact:
- USA 8x more cases than UK and France (late March)
- USA more marked peak (48925 cases on April 26th)
● Impact on cyclicality:
- The difference between weekdays and weekend depends on the period
we looked at. For example, in April and May 2020 in France, the lockdown
made the data less seasonal than the one for UK for example.

16. Conclusion
● Numerous differences:
- between countries
- over time
● Hypothesis