Calibrating a COVID-19 system dynamics model with multi-country cases to enhance model validity - Aisyah, Ibrahim; Hamdan, Daniyal; Tuty Asmawaty, Abdul Kadir; MacDonald, Roderick H. (IEEE Xplore, 2025)
Validating System Dynamics (SD) models has been a serious concern for many years. To address this issue, this study presents the validation of an SD model of COVID-19 using empirical data from multiple countries to assess both its structural and behavioural validity. The model was developed in Vensim DSS and manually calibrated to evaluate its performance against realworld data cases across five European countries: Spain, Portugal, Italy, France, and Hungary, which represent diverse outbreak contexts. The results indicate that the model successfully replicates the trends of the COVID-19 outbreak in each country, providing strong evidence of the model’s structural soundness, robustness and its capacity to adapt across diverse yet comparable contexts. Thus, uncertainty surrounding both the model’s structure and behaviour is reduced. The future implications of this study suggest that the model can support broader scenario testing and provide a foundation for future applications in other regions where validation across multiple cases is required.