Huseyin Atakan Varol, MOSES: A Matlab-Based Open-Source Stochastic Epidemic Simulator, EMBC, 2016.
You can download simulator code from the ARMS Repository:
If you use the simulator in your work, please cite us.
This work presents an open-source stochastic epidemic simulator. Discrete Time Markov Chain based simulator is implemented in Matlab. The simulator capable of simulating SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model can be reduced to simpler models by setting some of the parameters (transition probabilities) to zero. Similarly, it can be extended to more complicated models by editing the source code. It is designed to be used for testing different control algorithms to contain epidemics. The simulator is also designed to be compatible with a network based epidemic simulator and can be used in the network based scheme for the simulation of a node. Simulations show the capability of reproducing different epidemic model behaviors successfully in a computationally efficient manner.
- The simulator is based on SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model
- Discrete Time Markov Chain based stochastic simulation is employed
- Transition probabilities are summarized in a statechart
- The code is available under BSD-3-Clause Licence at arms.nu.edu.kz/episim
- Vital dynamics such as yearly birth and death rate included
- Different pathways for immunization is provided allowing simulation of diverse scenarios.
- The length of the exposure, infection and vaccination periods can be adjusted allowing the simulation of delay effects
- The model can be reduced to simulate simple models by setting relevant parameters to zero
In this work, a Matlab-based open-source stochastic epidemic simulator was presented. The original model can be reduced to simpler models by setting irrelevant parameters to zero. It can also be extended to more complex models by modifying the source code. Simulation results show that the simulator is capable of highlighting various effects such as vaccination and the delay in the dynamics due to exposed period. Future work includes use of control theory based algorithms for the control of epidemics using MOSES and integrating it to a network based simulator.