A Fuzzy Logic-Based Extension of SEIR Models for COVID-19 Epidemic Simulation

The COVID-19 pandemic has necessitated the development of sophisticated epidemiological models to guide public health interventions. This paper introduces an extended SEIR model incorporating fuzzy logic to better capture the complexities of disease transmission and progression. The model includes distinct compartments: Exposed E for individuals exposed to the virus but not yet infectious, Infected I for those capable of spreading the virus, Hospitalized H for those requiring hospitalization, Quarantined Q for infected but non-hospitalized individuals, Recovered R for those who have recovered and are assumed immune, and Deceased D for individuals who have died from the virus. Key parameters such as the transmission rate β , influenced by factors like population density and social distancing; progression rate from exposed to infected σ , affected by the incubation period; hospitalization rate η , determined by disease severity and healthcare access; quarantine rate δ , dependent on testing and isolation effectiveness; recovery rate γ , based on healthcare quality; and mortality rate ν , influenced by healthcare capacity and demographics, are all modeled using fuzzy logic to account for their inherent uncertainties. The incorporation of fuzzy logic allows the model to dynamically adjust these parameters, providing more accurate and adaptable predictions. Applied to COVID-19 case data, the Fuzzy SEIR model demonstrates improved accuracy in forecasting infection trends and calculating the Basic Reproduction Number R0 compared to traditional models, thereby offering a robust tool for optimizing public health responses and resource allocation during infectious disease outbreaks