Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning




SEIR, Machine Learning, Epidemic Model, Vaccination, Covid-19, El Salvador


The purpose of this study is to build an epidemic model with vaccination control for Covid-19 in El Salvador. A combination of epidemiological SEIR (Susceptible, Exposed, Infectious or Recovered) models and the estimation of parameters using machine learning and contact networks is proposed. The project consisted of three phases: a) Analysis: the critical or key factors or variables of the phenomenon under study were identified, the model to be used, as well as its parameters and components, were defined, designed, and constructed b) Simulation: simulation made it possible to modify variables, implement alternatives, and modify the model itself without affecting the real system, which is highly useful for decision-making and preparing results and recommendations. The simulations were carried out using population data from El Salvador. c) Optimization: different scenarios were evaluated in which vaccination control measures and social distancing measures were applied, in order to identify the optimal strategy. As a result of this study, the best strategy for controlling the disease was identified: a combination of vaccinating the vulnerable population and maintaining social distancing measures provided the best results in terms of reducing the impact of infection and minimizing treatment costs. Finally, recommendations are made to government health authorities for distribution and application of the treatment.


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Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning. (2022). Uniciencia, 36(1), 1-15.



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How to Cite

Covid-19 Vaccine Distribution: Combining SEIR and Machine Learning. (2022). Uniciencia, 36(1), 1-15.

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