Analytical reference framework to analyze non‑COVID‑19 events
Impact of Covid-19 in Colombia
Development and evaluation of mathematical and epidemiological models that support decision-making in response to the Covid-19 emergency in Colombia. The project was approached from the perspective of data science, using data analytics and machine learning techniques.
The framework proposed and implemented in this work, named “Analytics for Non-COVID-19 Events” (ANE, from now on), adapts the ASUM-DM (Analytics Solutions Unified Method for Data Mining) methodology to include and handle specific characteristics of health events and their underlying data. The proposed framework can be seen in the following image.
The proposed analytical framework was mainly implemented in 2 software components:
Events / Diseases
The selected events to which we applied the proposed framework are:
- Tuberculosis (TB)
- Suicide Attempt (SA)
- Infant Mortality (IM)
- Diabetes Mellitus (DM)
- Acute Diarrheal Disease (EDA)
- Excess Mortality (EM)
Click here to view the dataset files for the descriptive solution.
Click here to view the dataset files for the predictive solution.
The data (both source and predictions) are up to date:
- Tuberculosis: 12/27/2020
- Suicide Attempt: 12/27/2020
- Infant Mortality: 4/12/2020
- Diabetes Mellitus: 4/12/2020
- Acute Diarrheal Disease: 4/12/2020
- Excess Mortality: 4/12/2020
The project was carried out with the latest version of Anaconda on Windows.
The program can also be run on Linux with Python 3.7.x (or higher) by previously installing the following libraries:
sudo apt install sudo add-apt-repository universe sudo apt-get update sudo apt install python3-pandas sudo apt install python3-sklearn sudo apt install python3-statsmodels
- Created by Andrés Segura Tinoco
- Created on Jul 15, 2020
- Last update on Sep 06, 2021
We would like to make a special acknowledgement to the National Health Institute of Colombia.