SEIRS-based COVID-19 Simulation Package

Category: Codes/Software

Abstract/Vision:

The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.

The simulation package is available free and as an open-source, and distributed under the GNU license.

Any feedback and comments are welcome.

More details on the simulation package can be found at https://www.markusng.com/COVIDSIM/

If you use this simulation package in your research, please cite the following publication:

Contact: Mark Ng



Isolat: a data-driven approach to addressing the COVID-19 pandemic

Category: Codes/Software

Abstract/Vision:

IDSS COVID-19 Collaboration (Isolat) is a volunteer collaboration organized by IDSS to provide systematic and rigorous analyses of data associated with the Covid-19 pandemic in order to inform policy makers. Email idss-isolat@mit.edu to collaborate with the group, or with any questions you may have.

Contact: Ahmad Taha



Repository for SEIR models and beyond

Category: Codes/Software

Abstract/Vision:

By coincidence I have become a member of a collection of (German) epidemiologists. They are actively fine-tuning models that describe the outbreak and—more importantly—the evolution of the spread of Covid-19. If you have yourself a model (ODE, agent-based, etc.) consider “donating” the model to the community by making your code part of the repository. Check the GitHub site for more details.

Stay safe!

Contact: tillmann mühlpfordt



Simple Matlab Example of how to download data from the Johns-Hopkins University

Category: Codes/Software

Abstract/Vision:

This Matlab file is a simple example of how to download Covid19 Data
from the Web. In particular, the deaths cases (global) corresponding
to the Covid19 Data Repository by Johns Hopkins CSSE at GitHub
See https://arxiv.org/abs/2004.06111 for other data sets and Open Data Resources to Fight Covid19 (arxiv review paper).

Authors: Teodoro Alamo and Jose Luis Guzmán (members of CONCO-Team).

Code available at https://github.com/CONCO-Team/CONtrol-COvid19-TEAM/blob/master/Death_Cases_JHU_CSSE_Covid19.m

 

 

Contact: Teodoro Alamo