Implementation of the SEQHRV3 epidemiological model for mitigating COVID-19 and other vaccine-preventable disease outbreaks in Indonesia

Abstract

The global landscape faces continuous challenges from infectious diseases, often presenting unique and complex epidemic patterns due to emerging pathogens (e.g., COVID-19). This complexity underscores the necessity for effective control strategies. This study analyzes disease spread dynamics and develops an epidemiological model for assessing outbreak prevention strategies, focusing on vaccination as an intervention variable to model the dynamic progression of the disease more accurately, thereby simulating real-world epidemiological scenarios. The analysis employs the SEQHRV3 compartmental model using the most recent pandemic data (COVID-19), with parameters estimated from national health insurance, vaccination, and mortality databases. The system of differential equations was solved numerically over 5,000 days, utilizing Indonesian population data for initial values. The basic reproduction number was estimated at \(\mathcal{R}_0 \approx 2.0\), consistent with early COVID-19 transmission estimates. The simulation generated a clear epidemic wave that peaked sharply around day 100 before rapid subsidence, validating the 80% mild case assumption. Long-term analysis showed that the sequential three-dose strategy causes the \(V_3\) compartment to become the dominant immune group, while the susceptible population (\(S\)) stabilizes at a minimal level. The model predicts a successful transition toward an endemic state. These findings highlight the critical role of disease modelling in assessing the effectiveness of interventions made to prevent further outbreaks of a disease.