Releases: yuryatin/covid19_age_adjusted_mortality
COVID-19 gender and age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted fatality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it won't make sense.
The (hard coded) arbitrary functions (if necessary) can be updated both inside the shared C library (for calculation) and in the wrapper Python script (for plotting and reporting).
Published at GitHub
https://github.com/yuryatin/covid19_age_adjusted_mortality
To find detailed description, please, visit GitHub at
https://github.com/yuryatin/covid19_age_adjusted_mortality
The newest plot can be separately downloaded at https://doi.org/10.5281/zenodo.3787930
COVID-19 age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted fatality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it won't make sense.
The (hard coded) arbitrary functions (if necessary) can be updated both inside the shared C library (for calculation) and in the wrapper Python script (for plotting and reporting).
Published at GitHub
https://github.com/yuryatin/covid19_age_adjusted_mortality
To find detailed description, please, visit GitHub at
https://github.com/yuryatin/covid19_age_adjusted_mortality
COVID-19 age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted fatality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it will make no sense.
The (hard coded) arbitrary functions should be updated both inside the shared C library (for calculation) and in the wrapper Python script (for plotting and reporting).
Published at GitHub
https://github.com/yuryatin/covid19_age_adjusted_mortality
COVID-19 age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted mortality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it will make no sense.
The (hard coded) arbitrary functions should be updated both inside the shared C library (for calculation) and in the wrapper Python script (for plotting and reporting).
COVID-19 age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted mortality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it will make no sense.
The arbitrary functions are updated both inside the shared C library (for calculation) and in the wrapper Python script (for plotting and reporting).
COVID-19 age-adjusted mortality
This is a mixed Python & C package for data scientists.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted mortality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it will make no sense.
The arbitrary functions are updated inside the shared C library and in the wrapper Python script.
COVID-19 age-adjusted mortality
The code to fit analytically expressed functions to model dependency of COVID-19 mortality on age.
The code of the Python scripts and of the affiliated shared C library may help you fit analytically expressed functions to the COVID-19 mortality data to model age-adjusted mortality risk using maximum likelihood point estimates.
This software was desinged in two parts: a shared C library to dramatically speed up the calculation and a Python wrapper script to feed the input data into the affiliated shared C library and finally plot the fitted curves. A sample Python script.py file, which helps more easily import and transform the input tabular data and imports and communicates with the Python wrapper module, is also attached.
The shared C library provides the opportunity to test arbitrary functions on condition that, in the domain between 0 and 120+, they return values between 0.0 and 1.0 - otherwise, in this scenario, it will make no sense.