in silico Public Health: The Essential Role of Highly Detailed Simulations in Support of Public Health Decision-Making

Public Health requires a trans-disciplinary approach to tackle the breadth and depth of the issues it faces. Public health decisions are reached through the compilation of multiple data sources and their thoughtful synthesis. The complexity and importance of these decisions necessitates a variety of approaches, with simulations increasingly being relied upon. This dissertation describes several research efforts that demonstrate the utility of highly detailed simulations in public health decision-making.

Simulations are frequently used to represent dynamic processes and to synthesize data to predict future outcomes, which can be used in cost-benefit and course of action analyses. The threat of pandemic influenza and its subsequent arrival prompted many simulation-based studies. This dissertation details several such studies conducted at the federal policy level. Their use for planning and the rapid response to the unfolding crisis demonstrates the integration of highly detailed simulations into the public health decision-making process.

Most analytic methods developed by public health practitioners rely on historical data sources, but are intended to be broadly applicable. Oftentimes this data is limited or incomplete. This dissertation describes the use of highly detailed simulations to evaluate the performance of outbreak detection algorithms. By creating methods that generate realistic and configurable synthetic data, the reliance on these historical samples can be reduced, thus facilitating the development and improvement of methods for public health practice.

The process of decision-making itself can significantly influence the decisions reached. Many fields use simulations to train and evaluate, however, public health has yet to fully adopt these approaches. This dissertation details the construction of highly detailed synthetic data that was used to build an interactive environment designed to evaluate the decision-making processes for pertussis control. The realistic data sets provide sufficient face validity to experienced public health practitioners, creating a natural and effective medium for training and evaluation purposes.

Advances in high-performance computing, information sciences, computer science, and epidemiology are enabling increasing innovation in the application of simulations. This dissertation illustrates several applications of simulations to relevant public health practices and strongly argues that highly detailed simulations have an essential role to play in Public Health decision-making.