The CovidCases analysis class version 5.2

Introduction#

The basic concept of the class is to provide a pandas data frame with a time series of attributes such as the daily infections for a given list of countries. The GeoInfomationWorld class lists ISO-3166-alpha_2 and ISO-3166-alpha_3 codes for 217 countries of the world including their population based on the year 2020. You may take a look at the list here.
Once having the data frame we provide you with functions to plot the data using the PlotterBuilder class or draw a world map as a heatmap using the CovidMap class. Both classes have not been documented yet, but the source code of course includes comments and sample applications are also available on GitHub.

CovidCases class documentation version 5.2#

This abstract class acts as a parent class for different sub-classes which we call the CovidCases World sub-classes. While the sub-classes are responsible to get the data from different data sources and to provide them as a Pandas DataFrame this base-class provide functions to process the data.
This are the methods provided by the class:

def __init__(self, DataFrame):

The constructor of the class just takes the Pandas DataFrame created by a sub-class. The rows of the DataFrame contain the data for a specific date, they build a time series of data with the latest date in the top (row 0). The columns have to include the following mandatory attributes and may have additional private columns if required:

Based on the given columns the class will generate the following columns:

If you use the data from Our World in Data you have additional access to the following attributes:

After calling add_r0 or add_incidence_7day_per_100Kpopulation or add_lowpass_filter_for_attribute (refer to the function definitions below) you will notice additional attributes such as:

def get_country_data_by_geoid_list(self, geoIDs, lastNdays=0, sinceNcases=0):

Return a Pandas DataFrame by a list of strings containing the geoIDs of countries such as [[DE] [UK]]. Here you will find a list of GeoIDs and countries. Optional parameters are:

• lastNdays: returns just the data of the last n days.
• sinceNcases: returns just the data since the nth case has been exceeded per country.

def get__data_by_geoid_string_list(self, geoIDstringList, lastNdays=0, sinceNcases=0):

Exactly the same as the function above, but this time the list of GeoIDs is given as a comma separated list such as "DE, UK".

def get_all_data(self, lastNdays=0, sinceNcases=0):

The function works as the two functions above, but this time it returns a DataFrame for all countries in the csv. Notice that it might take some time before the function returns.

def save_df_to_csv(self, df, filename):

Saves the given DataFrame df to a csv file. The file will contain all columns of the DataFrame, also those who have been added by the functions below.

def add_r0(self, df):

Adds an attribute to the given DataFrame df of each country that is an estimation of the reproduction number R0. Here the number is called ‘R’. The returned DataFrame will contain low-passed filtered data with a kernel size of 7. If the attribute already exists in the df the function will return the given df.

def add_incidence_7day_per_100Kpopulation(self, df):

Adds an attribute to the df of each country that is representing the accumulated 7-day incidence. That is the sum of the daily cases of the last 7 days divided by the population in 100000 people. If the attribute already exists the function will return the given df.

def add_lowpass_filter_for_attribute(self, df, attribute, n):

Adds an attribute to the given DataFrame df of each country that is the low-pass filtered data of the given attribute (attribute name as a string). The width of the low-pass is given by the number n. The name of the newly created attribute is the given name with a tailing number n. E.g. Cases with n = 7 will add to a newly added attribute named Cases7. If the attribute already exists the function will return the given DataFrame df.

@staticmethod
def create_combined_dataframe_by_geoid_string_list(dfList, geoIDs, lastNdays=0, sinceNcases=0): 

Creates a combined data frame from a list of individual data frames. To avoid duplicate country names the method will add a ‘-DATASOURCE’ string behind the country name (e.g. ‘Germany-OWID’). The method takes a tuple of DataFrame objects as a first parameter. geoIDs is a list of GeoIDs given as a comma separated list such as "DE, UK". The optional parameter lastNdaysallows you to select only the data for the last N days. Alternatively you can align the data based on the day when the first N cases have been reported using the optional parameter sinceNcases. The function finally returns a combined data frame containing the data from all given data frames.

@abstractmethod
def get_available_GeoID_list(self):

Returns a DataFrame having just two columns GeoID and GeoName. You may want to store the returned DataFrame as a csv file. The function has to be implemented by all sub-classes.

@abstractmethod
def get_data_source_info(self):

Returns a DataFrame containing information about the data source. The DataFrame holds 3 columns:
InfoFullName: The full name of the data source
InfoShortName: A shortname for the data source
InfoLink: The link to get the data
The function has to be implemented by all sub-classes.

@abstractmethod
def review_geoid_list(self, geoIDs):

Returns a corrected version of the given geoID list to ensure that mismatches like UK versus GB are corrected by the sub-class. For instance: If the given list contains [‘DE’, ‘UK’] the function will return [‘DE’, ‘GB’] to correct the wrong UK with the ISO-3166-alpha_2 conformal GB.

CovidCases sub classes documentation#

Refer to the CovidCases sub-classes version 5.2 documentation for details about the different sub-classes as these might contain additional features or attributes.