Data

Original Research

To make the database compatible with other database, we encode country names with the World Bank's country name coder and manually fix mistakes.

target_countries = wb.economy.coder(orig_df['Country']) orig_df['Country Code'] = target_countries orig_df.at[11, "Country Code"] = "COG" orig_df.at[46, "Country Code"] = "SDS" target_countries.head()

World Bank

wb_final_df = wb_average_df.copy() wb_final_df[wb_to_scale] = wb_final_df[wb_to_scale].div(wb_final_df.Population, axis = 0) wb_final_df

UNICEF

Merge

Mapping

""" Get latitudes of longitudes. """ geolocator = GeoNames(username = "sydneydegen1") def geolocate(country): # Geolocate the center of the country loc = geolocator.geocode(query=country) # And return latitude and longitude return (loc.latitude, loc.longitude) if loc else (0,0) final_df[["Latitude", "Longitude"]] = final_df["Country"].apply(geolocate).apply(pd.Series) options = list(final_df.columns)

map_feature

/shared-libs/python3.7/py/lib/python3.7/site-packages/folium/folium.py:413: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  FutureWarning

Analysis

Correlation Matrix

Governance and Prevalence

Observations 27
GDP                      -1.155604e-04
NMRA Rating              -1.324730e-02
Healthcare Expenditure    8.917094e-04
Government Expenditure    5.277955e-12
CPIA Budgeting           -1.038497e-01
CPIA Administration       1.533537e-01
CPIA Corruption           3.856467e-02
Urban Population          1.653616e-03
dtype: float64
GDP                      -1.197479
NMRA Rating              -1.691267
Healthcare Expenditure    1.488216
Government Expenditure    0.708842
CPIA Budgeting           -1.095660
CPIA Administration       1.506160
CPIA Corruption           0.494997
Urban Population          0.711684
dtype: float64

Mortality and Morbidity

Child Mortality

Observations 35
Weighted Prevalence    55.767585
GDP                     0.000219
dtype: float64
Weighted Prevalence    6.802031
GDP                    0.323940
dtype: float64

Malaria Incidence

Observations 35
Weighted Prevalence    716.920386
GDP                      0.002070
dtype: float64
Weighted Prevalence    4.424998
GDP                    0.154744
dtype: float64

HIV Incidence

Observations 32
Weighted Prevalence    10814.277686
GDP                        0.466579
dtype: float64
Weighted Prevalence    1.948766
GDP                    0.937380
dtype: float64

Infectious Disease

Observations 35
Weighted Prevalence    164.994302
GDP                      0.002764
dtype: float64
Weighted Prevalence    7.608140
GDP                    1.544055
dtype: float64