In spite of the remarkable development of modern medical treatment and technology, the threat of pandemic diseases such as anthrax, cholera, and SARS has not disappeared. As a part of emerging healthcare decision problems, many researchers have studied how to detect and contain disease outbreaks, and our research is aligned with this trend. This thesis mainly consists of two parts: epidemic simulation modeling for effective intervention strategies and spatiotemporal monitoring for outbreak detection. We developed a stochastic epidemic simulation model of a pandemic influenza virus (H1N1) to test possible interventions within a structured population. The possible interventions — such as vaccination, antiviral treatment, household prophylaxis, school closure and social distancing — are investigated in a large number of scenarios, including delays in vaccine delivery and low and moderate efficacy of the vaccine. Since timely and accurate detection of a disease outbreak is crucial in terms of preparation for emergencies in healthcare and biosurveillance, we suggest two spatiotemporal monitoring charts, namely, the SMCUSUM and RMCUSUM charts, to detect increases in the rate or count of disease incidents. Our research includes convenient methods to approximate the control limits of the charts. An analytical control limit approximation method for the SMCUSUM chart performs well under certain conditions on the data distribution and monitoring range. Another control limit approximation method for the RMCUSUM chart provides robust performance to various monitoring range, spatial correlation structures, and data distributions without intensive modeling of the underlying process.