Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Y¨ ucesan, eds. INCORPORATING HEALTHCARE SYSTEMS IN PANDEMIC MODELS Natalia E. Lizon Dionne M. Aleman Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road, Toronto, ON M5S 3G8, CANADA Brian Schwartz Department of Family and Community Medicine University of Toronto 263 McCaul Street, Toronto, ON M5T 1W7, CANADA ABSTRACT There are several models used to predict the spread of disease in a pandemic, but few, if any, incorporate the effect of healthcare systems in preventing propagation of the disease. In areas where healthcare is easily available to the general public (specifically, countries with universal healthcare), the ability of infected individuals to receive rapid treatment should impact disease spread. Additionally, the presence of a pandemic will result in an increased load on the healthcare system as infected individuals seek medical attention at hospitals and from their family doctors. We modify an existing non-homogeneous, agent-based simulation pandemic disease spread model to incorporate a public healthcare system in a pandemic influenza simulation on the Greater Toronto Area, Ontario, Canada. Results show that healthcare availability significantly significantly increases disease spread due to increased contacts within the population. We also find that the creation of flu centers decreases flu-related deaths and decreases hospital admissions. 1 INTRODUCTION In 2002-2003, a global outbreak of Severe Acute Respiratory Syndrome (SARS) resulted in over 8,000 infections and almost 800 deaths. This outbreak illustrated the need for not only pandemic planning, but also for accurate pandemic modeling. Realistic models for predicting the spread of disease can provide evidence as to the effects of mitigation strategies in lessening the spread of disease. For example, the effect of vaccinating individuals in certain age groups can be estimated in advance and compared to other possible strategies. In this work, we are specifically interested in the effect of healthcare availability on disease spread, and the effect of disease spread on healthcare resources. We are additionally focused on the pilot region of the Greater Toronto Area (GTA), Ontario, Canada. The GTA consists of approximately 4.99 million people living in a largely urban environment. The presence of universal healthcare and wide usage of public transportation make this region difficult to model for most existing disease spread approaches. The majority of pandemic disease spread models in the literature rely on simplifying measures that make incorporating healthcare systems complicated or even impossible. Homogeneous mixing models, the most common approach to disease spread modeling, assume that all infected individuals are assumed to transmit the disease to the same number of people (that number is called the basic reproduction number, R 0 ). This simplification has been shown to lead to inconsistent and inaccurate predictions of disease spread (Meyers et al. 2005, Newman 2002). Additionally, the uniformity of each individual of the population make it difficult to account for individualistic attributes such as use of public transportation or inclination to visit a family doctor once infected. Although more realistic non-homogeneous (heterogeneous) mixing models exist (Meyers et al. 2005, Valle et al. 2007, Larson 2007), it is still difficult to address individualistic attributes. Instead, we employ a non-homogeneous agent-based simulation model (Aleman et al. 2009a, Aleman et al. 2009b) that allows for each individual in the population to be unique, including demographic information and behavioral patterns. The individual characteristics will allow us to simulate not only the number of infections and deaths caused by the disease, but also the impact on regional healthcare facilities. 2230 978-1-4244-9864-2/10/$26.00 ©2010 IEEE