Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher, eds SIMPHO: AN ONTOLOGY FOR SIMULATION MODELING OF POPULATION HEALTH Anya Okhmatovskaia Philippe Finès David L. Buckeridge Arash Shaban-Nejad Andrew Sutcliffe McGill University Statistics Canada 1140 Pine Ave. West 100 Promenade Tunney's Pasture H3A 1A3 Montreal, QC, CANADA K1A 0T6 Ottawa, ON, CANADA Jacek A. Kopec Michael C. Wolfson Arthritis Research Centre of Canada University of Ottawa 895 West 10th Avenue 451 Smyth Rd. V5Z 1L7 Vancouver, BC, CANADA K1H 8M5 Ottawa, ON, CANADA ABSTRACT Simulation modeling of population health is being used increasingly for epidemiology research and public health policy-making. However, the impact of population health simulation models is inhibited by their complexity and the lack of established standards to describe these models. To address this issue, we are developing the Ontology for Simulation Modeling of Population Health (SimPHO) – a formal, explicit, computer-readable approach to describing population health simulation models. SimPHO builds on pre- vious work to classify and formally represent knowledge about simulation models, and incorporates the semantics of the epidemiology and public health domains. SimPHO will allow model developers to make explicit their assumptions, to describe their models in a formal, consistent and interoperable manner, and to facilitate model reuse and integration. To illustrate the use of SimPHO, we describe one software ap- plication driven by this ontology, an automated visualization tool for generating interactive web-based di- agrams of population health simulation models. 1 INTRODUCTION Computer simulation of population health is a powerful technique that has been used for over two decades by epidemiologists and public health researchers to study population-level health phenomena emerging from complex interactions of mechanisms at the levels of individuals, populations, and healthcare sys- tems. With recent advances in computing technology and the increasing availability of electronic health data, simulation modeling continues to gain credibility and exert greater influence on public health policy. Modern applications of health simulation models include predicting transmission patterns of infectious diseases, estimating the economic burden of disease outbreaks, analyzing the performance of public health surveillance systems and evaluating the impacts of health interventions and policies. There remain, however, significant issues that inhibit wider acceptance of agent-based models of population health among researchers and practitioners. Particularly, the inherent complexity of these models and the lack of standards for describing them present formidable barriers to communicating the details of the models between model developers on one side and other researchers and policy-makers on the other. Scientific publications presenting health simulation models vary in the accuracy and consisten- 978-1-4673-4780-8/12/$31.00 ©2012 IEEE 883 978-1-4673-4782-2/12/$31.00 ©2012 IEEE