The emergence and re-emergence of infectious diseases pose ongoing threats to public health. This study aims to develop an agent-based simulation model (ABM) to predict the spread of novel infectious diseases during early outbreak phases and evaluate the effectiveness of control measures, specifically focusing on the impact of interventions such as maskwearing, vaccination, and social distancing on outbreak dynamics and the reduction of symptomatic cases. Using demographic and COVID-19 outbreak data from South Korea, we constructed a detailed contact network model encompassing workplaces, schools, households, and communities. Using demographic and COVID-19 outbreak data from Seoul, South Korea, we constructed a detailed contact network model encompassing workplaces, schools, households, and communities. Key transmission parameters were inferred using Approximate Bayesian Computation. The resulting ABM platform, implemented in a C-based R package, allows for flexible scenario simulation involving 56 adjustable parameters, including mask-wearing, vaccination coverage, and social distancing. Simulation outputs demonstrated the model’s capacity to reproduce observed transmission patterns in workplace and school outbreaks, enabling public health authorities to anticipate outbreak dynamics and assess interventions. This framework provides a valuable decision-support tool for controlling future infectious disease incursions.

Abstract
INTRODUCTION
MATERIALS AND METHODS
    Base model design
    Parameter estimation
    Expanded Seoul simulation
    Scenario and policy simulation
RESULTS
    Workplace transmission
    School transmission
    City-wide dynamics
    Policy effectiveness
DISCUSSION
ACKNOWLEDGEMENTS
AUTHOR CONTRIBUTIONS
REFERENCES

• Jun-Hee Han(EpiCentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand)
• Inho Hong(Graduate School of Data Science, Chonnam National University, Gwang Ju, Korea)
• Jaehoon Kim(Division of Disease Control Research Planning, Bureau of Department of Data Science, Korea Disease Control and Prevention Agency, Cheongju, Korea)
• Min-Gyu Yoo(Division of Disease Control Research Planning, Bureau of Department of Data Science, Korea Disease Control and Prevention Agency, Cheongju, Korea)
• Myeongsu Yoo(Division of Disease Control Research Planning, Bureau of Department of Data Science, Korea Disease Control and Prevention Agency, Cheongju, Korea)
• Dae Sung Yoo(Department of Veterinary Epidemiology, College of Veterinary Medicine, Chonnam National University, Gwang Ju, Korea) Corresponding author