Simulating the Mpox Early Outbreak: Dynamic-Spread Assessment via vSEIR Modelling and Kink Detection in Disease Transmission

The mpox epidemic rapidly sparks new panic in the context that the aftermath triggered by the COVID-19 pandemic has not fully dissipated, amplifying the necessity for delving deeper into its propagation features and dynamic simulation of its spread. Therefore, this paper proposes a varying coefficient Susceptible-Exposed-Infected-Removed (vSEIR) model to simulate the early mpox epidemic, considering the time-varying infection rate and the group protected by the smallpox vaccination. We apply the recursive least squares algorithm with a forgetting factor for real-time identification of time-varying infection rates and the efficacy of non-pharmacological interventions. The sparse Hodrick-Prescott (HP) filter, tuned with leave-one-out cross-validation, captures mpox epidemic kinks via the effective reproduction number Rt obtained from the vSEIR model. We experiment with this approach in Brazil, Spain, UK and US, identifying epidemic propagation periods based on the HP filter-generated kinks. Finally, comparisons between COVID-19 and mpox epidemic are conducted based on those kinks and propagation cycles, identifying that the Rt of mpox is lower than COVID-19 and except for Spain, mpox epidemic reached its decline period earlier than COVID-19 without strong interventions. Additionally, the result regarding sensitivity analysis on vaccine protection shows that the total number of infections would have increased by 12% in the early transmission stage without smallpox vaccination, and prevention of its future resurgence is recommended.