An Approach to, and Tool for, Predicting the Time Course of an Infectious Disease Outbreak as a Function of Behavioral Interventions.

INTRODUCTION

In the early twenty-first century, humanity faced life-threatening infectious diseases, primarily caused by zoonotic pathogens, such as Ebola, Influenza, Middle East Respiratory Syndrome Coronavirus and Severe Acute Respiratory Syndrome Coronavirus. Early warnings based on epidemic models are crucial for preventing and controlling outbreaks. However, most decisions rely on expert opinions rather than robust epidemic model outputs. Implementing epidemic models can be challenging without a strong background in statistical modeling.

METHODS

This paper presents a simple, user-friendly tool in MATLAB to predict the time course of an infectious disease outbreak using various modified epidemic models. These models incorporate key non-pharmaceutical interventions to limit transmission within communities. Additionally, we introduce improved epidemiological model structures for outbreak control and prevention. To demonstrate the application of our interactive program and discuss user decision-making, we provide an example using key parameters from recent studies. The simulation model was run with varying scenarios, adjusting the effectiveness and coverage of social distancing, hand washing, and face mask usage.

RESULTS

Without any intervention, 99.6% of the population could be infected within 100 days. Combining social distancing with 80% coverage of face masks and hand washing reduced transmission and death by 99.96% and 99.98%, respectively, compared to no preventive measures.

CONCLUSION

This interactive computer program aids epidemiologists, public health experts, and decision-makers in understanding and predicting infectious transmission. It is also valuable for generating rapid reports on infectious diseases and outbreak responses where time is a critical. Furthermore, it enhances collaboration between public health stakeholders and modeling professionals, aiming to optimize disease prevention and control strategies during outbreaks or epidemics.