AN EDUCATIONAL APPROACH FOR MATHEMATICAL MODELS OF COVID-19 PROPAGATION: ODE AND NEURAL NETWOKS
AN EDUCATIONAL APPROACH FOR MATHEMATICAL MODELS OF COVID-19 PROPAGATION: ODE AND NEURAL NETWOKS

Summary/Abstract: A special type of coronavirus responsible for respiratory illness was first discovered in Wuhan, China in December 2019 and shortly is spreading in the world. Even few vaccines have been developed, actually their effects are not completely studies during few years over a large population, and it is possible that this pandemic to be seasonal in the future, in a similar manner like the flu. An educational tool will be useful for students and research to learn the model of spreading the disease, especially for biomedical engineering students that have knowledge both about the infectious diseases and mathematical modelling. An educational tool for recurrent waves of COVID-19 disease is proposed. The tool uses two general types of models: a compartment one that can be translated in a system of ordinary differential diseases (ODE) and neural network ones. The asymptomatic compartment and quarantine along with possibility of re-infection is taken into account in both models. The lockdown is simulated using few types of continuous function instead on constant transmission rate of disease. A graphic user interface (GUI) is constructed in order to offer to user an intuitive manner to user learn the COVID-19 models. The tool offers the possibility to choose the parameters of models and suggestion for equilibrium stability to the disease-free and endemic equilibrium and calculation of basic reproduction number. A predefined set with collected data for few countries was downloaded from site that maintains the evidence of cases in the world, but also new collected data by user can be loaded into tool to test the models.

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