COVID-19 Risk Map

COVID-19 risk map based on mobility and socio-demographic data.

Workflow

Use the mitma-covid repository to generate the province_flux.csv file. Copy it to the data/raw folder in this package. You can also use the dacot repo if you want to use INE mobility data (with are sparser).
Run make data to generate the additional data needed to plot everything (that is the covid cases that are updated weekly by the Health Ministry).

After running step 2, data/processed will have the following files:

cantabria-incidence.csv: covid cases in Cantabria, by municipalities, for the most recent date
provinces-incidence.csv: covid cases for all provinces, for all dates. Cases are divided in:
- cases new: newly diagnosed cases
- cases acc: cumsum of cases since the start of the pandemic
- cases inc: increment of changes, porcentual changes in accumulated cases
- incidence X: new cases per 100K persons, summed over last X days
provinces-mobility-incidence.csv: We add mobility info to the previous file. Columns indicate the provinces where the trip starts (origin), the rows the province where the trips end (destination). Each province column (origins) is divided in three, as a Pandas multiindex dataframe. For example Zamora has:
- Zamora.0: flux coming from Zamora (in persons)
- Zamora.1: incidence at 14 days in Zamora
- Zamora.2: incidence at 7 days in Zamora
The origin's (flux, inc 14, inc 7) values where origin=destination have been set to NaN as this information is already present in the destination's (flux intra, incidence 7, incidence 14).

Run make visualize. This will directly open the maps in your browser. Scroll down the webpage to the different plots.

Geographical data:

Statistical data: