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

Haftom Temesgen Abebe,1,2 Amir Siraj,3 Kiros Berhane,4 Dawd Siraj,5 Gerard JP Van Breukelen6 1Department of Biostatistics, Mekelle University College of Health Sciences, Mekelle, Tigray, Ethiopia; 2Laboratory Interdisciplinary Statistical Data Analysis, Mekelle University College of Health Sciences, Mekelle, Tigray, Ethiopia; 3Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; 4Department of Biostatistics, Columbia University, New York, NY, USA; 5Division of Infectious Diseases, University of Wisconsin-Madison, Madison, WI, USA; 6Department of Methodology and Statistics, School CAPHRI Care and Public Health Research Institute and Graduate School of Psychology and Neuroscience, University of Maastricht, Maastricht, The NetherlandsCorrespondence: Haftom Temesgen Abebe, Department of Biostatistics, Mekelle University College of Health Sciences, P.O. Box 1871, Mekelle, Tigray, Ethiopia, Email [email protected]: 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.Plain Language Summary: Early warning to prevent and control outbreaks or epidemics of infectious diseases often rely on expert opinion rather than robust epidemiological model outputs. Predicting the time course of an infectious disease outbreak is a challenging task. This interactive computer program predicts the time course of an infectious disease outbreak using various improved epidemiological model structures under different scenarios of non-pharmaceutical interventions that can limit transmission within a community. Combining and maintaining social distancing with 80% coverage of face masks and hand washing reduced transmission by 99.96% and the death rate by 99.98% compared to the scenario of no preventive measures. This tool helps epidemiologists, public health experts, and decision-makers better understand and predict infectious transmission. It will enhance the collaboration between public health stakeholders and modeling professionals with the objective of optimizing disease prevention and control strategies during infectious disease outbreaks or epidemics.Keywords: Infectious disease, COVID-19, epidemic models, non-pharmaceutical interventions