Climate Change Calculated

In this special issue, I show the link between climate change and new viruses, such as the COVID-19 virus. I show a statistical model based on the logistic function to model and predict the spread of the virus in Germany.

In this episode, I show the changes in climate that we had last winter and from the Corona virus.

Then I will compare my model for the spread of the Corona virus with the data from the Johns Hopkins Universityupdate my model with this data and show comparisons of the daily increase factor for different countries.

Comparing the data to my model, the effects of the actions from March 20, 2020 are clearly visible.

I will use the updated model for April 2020 forecasts and for a 150-day long-term forecast.

Video update Corona special on YouTube :YouTube video link.

Slides on the Corona virus update

Python programs for the forecast model (can be used for other countries as well):

coronacasesreview.py

coronacasesApril.py

coronacasesAprillongterm.py

The following program can be used to track the daily increase factor of Corona cases, starting with April 1, 2020. It loads the latest data every time it is launched from Github. With a factor of 1.02 (doubling every 35 days), the slow convergence becomes visible. You can also see that South Korea has already achieved this.

coronacasesfactorofincrease.py

In this episode, I analyze the current status of SARS-CoV-2 spread, calculate the density of actively ill patients and their growth factors, and compare developments in Germany and the United States.

Video Update 2 Corona Special on YouTube:https://youtu.be/3g8QmB8fqEI

Slides on the Corona virus update 2

Python program to calculate the plots

Programs and Jupyter Notebooks for Corona computations

In this episode, I calculate the underdetection rate of Corona cases, also called the "dark rate," using my online Corona program and give a 30- and 60-day forecast for the number of actively ill.

YouTube video:https://youtu.be/uMbrgJW34Fw

Slides.pdf

Jupyter notebook in github and Google Colab.

In this episode, I show my Python Jupyter Notebook for modeling and predicting the spread of the new Corona virus. Different countries are compared, temporal trends, predictions and analysis are shown. Used is the Johns-Hopkins University online GitHub Repository for the most recent data. The Jupyter Notebook and presentation run in Google Colab, so it can be customized in the browser, e.g. by country selection.

YouTubeVideo (English):https://youtu.be/4kbCc4roZIg

Jupyter notebook in github and Google Colab:https://github.com/TUIlmenauAMS/CoronaComputationPrograms

In this episode: Delta and Omikron

YouTube video

In this episode I conclude my Corona coverage with a review of my model and the data for the concurrently sick and the Corona related death cases, with international comparisons between Germany, the US and Singapore.

Link to my Colab notebook and slides:

https://github.com/TUIlmenauAMS/CoronaComputationPrograms

Media Technology channel: https://youtu.be/Yrx16lD74LI