SimCOVID

SimCOVID simulates and analyzes the spread of COVID-19 to estimate transmission parameters and predict outbreak dynamics.

Key Features:

• Simulation Frameworks: Implements Simulink block-diagram programs and MATLAB scripts for simulation.
• Mathematical Models: Implements SIR, SEIR, and SEIRD epidemiological models represented by differential-algebraic equations.
• Model Adaptability: Allows modification and customization of model structure and parameters.
• Dynamic Tracking Methodology: Represents outbreak dynamics using a sum of sigmoid functions to capture temporal changes in infection rates.
• Parameter Estimation: Uses MATLAB parameter estimation to compute infection and recovery rate functions and applies statistical analyses including linear analysis, mean, root-mean-square, and standard deviation.
• Predictive Modeling with ANFIS: Integrates an adaptive neuro-fuzzy inference system (ANFIS) for training models and predicting outcomes.

Scientific Applications:

• Research Studies: Enables analysis of COVID-19 spread patterns, evaluation of intervention scenarios, and exploration of model and parameter configurations.
• Educational Use: Provides hands-on computational examples for teaching infectious disease modeling and parameter estimation methods.

Methodology:

Implements SIR/SEIR/SEIRD differential-algebraic equation models via Simulink and MATLAB scripts, applies a sum of sigmoid functions for dynamic tracking, performs MATLAB parameter estimation on infection and recovery rate functions with linear analysis, mean, root-mean-square and standard deviation metrics, and employs ANFIS for training and prediction.