Mathematical Epidemiology
Spread of Covid-19

COVID-19 Epidemiological Modelling Project Repositories
The COVID-19 Epidemiological Modelling Project uses repositories for exchange of data, models, data analysis and literature surveys.
You will need a GitHub screen name in order to use the repositories. You can register for free at
GitHub
If you like to access the repositories, write an e-mail to
markus@mathematical-modelling.science
with subject repositories. You need to enter your GitHub screen name in the body of the e-mail.
You will need a GitHub screen name in order to use the repositories. You can register for free at
GitHub
If you like to access the repositories, write an e-mail to
markus@mathematical-modelling.science
with subject repositories. You need to enter your GitHub screen name in the body of the e-mail.
Data Repository
The data repository can be accessed here:
DATA REPOSITORY
There is also a corresponding wiki which data collectors can use for internal communication:
DATA WIKI
The data repository contains at its core world-wide collected data on a national level since the outbreak of the pandemic. It should contain for each country at least the following categories:
Very useful would also be an age-distribution of newly daily deceased individuals per country.
We also need to add regional and metropolitan data in order to understand the contact process better. More detailed studies and tracing methods should become more and more avilable.
DATA REPOSITORY
There is also a corresponding wiki which data collectors can use for internal communication:
DATA WIKI
The data repository contains at its core world-wide collected data on a national level since the outbreak of the pandemic. It should contain for each country at least the following categories:
- Daily number of newly confirmed (tested) infected individuals.
- Daily number of deaths due to Covid-19 (including all cases, not just people dying in hospitals).
- Daily number of estimated newly infected individuals, (tested plus non-tested individuals).
Very useful would also be an age-distribution of newly daily deceased individuals per country.
We also need to add regional and metropolitan data in order to understand the contact process better. More detailed studies and tracing methods should become more and more avilable.
Reference Repository
The references repository can be accessed here:
REFERENCE REPOSITORY
REFERENCE REPOSITORY
There is also a corresponding wiki which reference collectors can use for internal communication:
The reference repository contains references to scientific articles and news coverage of the pandemic. We have the following categories:
- Epidemiology
- Medicine
- Ecology
- Political Sciences
- Mathematical Models
- Economy and Finance
Simulation Repository
The data repository can be accessed here:
SIMULATION REPOSITORY
There is also a corresponding wiki which simulation activities can use for internal communication:
SIMULATION WIKI
The simulation repository contains simulation codes for the pandemic. It currently covers the following categories categories:
SIMULATION REPOSITORY
There is also a corresponding wiki which simulation activities can use for internal communication:
SIMULATION WIKI
The simulation repository contains simulation codes for the pandemic. It currently covers the following categories categories:
- Ordinary Differential Equations (ODE)
- Partial Differential Equations (PDE)
- Stochastic Simulation Algorithms (SSA)
- Physiologically Structured Models (PSM)
- Agent-Based Models (ABM)
Data Analysis Repository
This repository contains algorithms and methods of data analysis, like statistics and machine learning applied to Covid-19 data. The data analysis repository can be accessed here:
There is also a corresponding wiki which reference collectors can use for internal communication:
The data analysis repository contains algorithms and codes applied to the data from the data analysis repository. It coveres the following areas:
- Statistics
- Parameter estimation (as a particular important part of statistics)
- Machine Learning
- Topological Data Analysis