`MiTepid_opt`: An optimisation toolbox to estimate the parameters of compartmental epidemiological models.
The toolbox is written in Matlab uses Global Optimization Toolbox. It calculates the contact rates of a compartmental epidemiological model based on real-world data. I used it to estimate the contact rates of a model that simulates the spread of SARS-CoV-2. The estimated parameters accompany the repository (. The results of MiTepid_opt can and are used in MiTepid_sim library. The results of many runs can be already found in folder x0R_opt_all_vars and the ones exported to MiTepid_sim can be found in folder x0R_opt_MiTepid_sim.
The toolbox provides a method to calculate the contact rates of an epidemiological model based on actual collected data on the spread of SARS-CoV-2. And it provides a method to optimise the distribution of a limited number of vaccine units in a population with a given age distribution to bring the spread of the virus as low as possible. The optimisation scheme has two constraints. One part is aimed to estimate the parameters of a set of Ordinary Differential Equations based on values of states at a given time instance, while the other part is aimed to set the spectral radius of the matrix of the contact rates to a certain value. The reasoning behind it comes from results in the theory of monotone systems and also the Perron-Frobenius Theorem. The details of the optimisation scheme as explained in the following preprint a few months after the outbreak of SARS-CoV-2. I shared it with relevant authorities in the German government and later on I published it as an open-access paper in PLOS ONE.
Running each run of the optimise_CR function takes 15-20 minutes on a cluster which have 20 cores of type Intel(R) Xeon(R) CPU E5-2687W v4 @ 3.00GHz. And for optimise_HI function, it is around 6-7 minutes.
Just download the mitepid_opt folder into your local hard disk. Add it to Matlab Paths and run script_Calculate_Contact_Rates.m or script_Optimise_Vaccine_Distribution.m.
And please remember that you will need the Global Optimization Toolbox too.
This code is tested under Matlab 2019b but is expected to run without errors on newer versions.
GNU General Public License (Version 3).
MiTepid_opt is maintained by Vahid Samadi Bokharaie.