Vahid S. Bokharaie, PhD

Senior Research Scientist

Max Planck Institute for Biological Cybernetics

Tübingen, Germany

vahid DOT bokharaie AT tuebingen DOT mpg DOT de

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In my current job, I work as a neuroscientist (more info on that line of work here). But I worked on mathematical epidemiology as part of my PhD thesis which also led to this and this publication.

These days, I am trying to use my knowledge of mathematical epidemiology to address some of the issues facing us all in the COVID-19 pandemic. The result so far has been a method to adapt a known mathematical model to the data obtained from the spread of COVID-19. The method can be used in any population with a known age distribution. It can also be used to predict the effects of various containment strategies on the spread of the virus which can provide helpful hints for policy-makers to make more informed decisions.
Below, you can find detailed descriptions of the model, and how it can be used to answer different questions. If you want to jump to the main results, read this manuscript (pdf).


Contents

1 Modelling the Spread of COVID-19
 1.1 Using Available Data to Model the Spread of COVID-19
 1.2 The Code
2 Case studies
 2.1 Germany
 2.2 Iran and COVID-19: A Humanitarian Disaster

 1
Modelling the Spread of COVID-19

1.1 Using Available Data to Model the Spread of COVID-19

I have used the available data on the spread of COVID-19 in China to estimate the parameters of an epidemiological model in a population stratified based on the age distribution. The model can be adapted and used for every country or population with a known age distribution. It can then be used to analyse the spread of COVID-19 in a population in each age group. More importantly, it can be used to evaluate various containment policies and their effects on the spread of COVID-19. To read the mathematical details of the model, you can look at this manuscript (pdf).

1.2 The Code

If you want to run your own simulations, you can find the code in this GitHub Repository. The code includes the results of the optimisation scheme. If you want to run the optimisation scheme yourself, please let me know and I can send you the code. But please note that the code needs Global Optimisation Toolbox in Matlab and if run on a typical PC, might take hours. I ran it on 40 hyper-threaded E5 cores and it can take around 15 minutes on such a cluster to run.

 2
Case studies

2.1 Germany

The above-mentioned manuscript uses Germany as a case study. You can look into that for all the details of the simulations and results, including the results on how to optimise the limited number of vaccines in the population of Germany to obtain the best outcome.

2.2 Iran and COVID-19: A Humanitarian Disaster

I have used the mathematical model to simulate the spread of COVID-19 in Iran too. I wrote a report in March which predicted a grim picture of the situation in Iran in the months to come. You can read the report here. In early November, I updated that report and looking at how the situation has unfolded, or better to say an estimate of that, given the complete lack of transparency in Iran. You can read the updated report here.

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Last updated on November 8, 2020.