Simulation-based optimization of mitigation strategies for pandemic influenza.

Millions of people have been infected and died as results of influenza pandemics in human history. In order to prepare for these disasters, it is important to know how the disease spreads. Further, intervention strategies should be implemented during the pandemics to mitigate their ill effects. Knowledge of how these interventions will affect the pandemic course is paramount for decision makers. This study develops a simulation-based optimization model which aims at finding a combination of strategies that result in the best value for an objective function of defined metrics under a set of constraints. Also, a procedure is presented to solve the optimization model. In particular, a simulation model for the spread of the influenza virus in case of a pandemic is presented that is based on the socio-demographic characteristics of the Jefferson County, KY. Then, School closure and home confinement are considered as the two intervention strategies that are investigated in this study and the simulation model is enhanced to incorporate the changes of the pandemic course (e.g. the number of ill individuals during the pandemic period) as results of the establishment of different scenarios for the intervention strategies. Finally, an optimization model is developed that its feasible region includes the feasible scenarios for establishment of intervention strategies (i.e. home confinement and school closure). The optimization model aims at finding an optimal combination of those two strategies to minimize the economic cost of the pandemic under a set of constraints on the control variables. Control variables include time, length of closure for schools, and the rate of home confinement of the individuals for home confinement strategy. This optimization model is connected to the pre-mentioned simulation model and is solved using a simulation-based optimization procedure called NSGS. Where the results of the analysis show both home confinement and school closure strategies are effective in terms of the outputs of the model (e.g. number of illness cases during the pandemic), they show home confinement is a more cost effective one

Modelling Hospital Acquired Clostridium difficile Infections And Its Transmission In Acute Hospital Settings

The thesis explored a number of fundamental issues regarding the development of predictive models for hospital acquired Clostridium difficile infection (HA CDI) and its outbreaks. As predictive modeling for hospital acquired infection is still an emerging field and the ability to analyse HA CDI and potential outbreaks are in a developmental stage, the research documented in this thesis is exploratory and preliminary. Predictive modeling for the outbreak of hospital acquired infections can be considered at two levels: population and individual. We provide a comprehensive review regarding modeling methodology in this field at both population level and individual level. The transmission of HA CDI is not well understood. An agent based simulation model was built to evaluate the relative importance of the potential sources of Clostridium difficile (C. difficile) infection in a non-outbreak ward setting in an acute care hospital. The model was calibrated through a two stage procedure which utilized Latin Hypercube Sampling methodology and Genetic Algorithm optimization to capture five different patterns reported in the literature. A number of aspects of the model including housekeeping, hand hygiene compliance, patient turnover, and antibiotic pressure were explored. Based on the modeling results, several prevention policies are recommended. One widely used tool to better understand the dynamics of infectious disease outbreaks is network epidemiology. We explored the potential of using network statistics for the prediction of the transmission of HA CDIs in the hospital. Two types of dynamic networks were studied: ward level contacts and hospital transfers. An innovative method that combines time series data mining and predictive classification models was introduced for the analysis of these dynamic networks and for the prediction of HA CDI transmission. The results suggest that the network statistics extracted from the dynamic networks are potential predictors for the transmission of HA CDIs. We explored the potential of using the “multiple modeling methods approach” to predict HA CDI patient at risk by using the data from the information systems in the hospital. A range of machine learning predictive models were utilized to analyse collected data from a hospital. Our results suggest that the multiple modeling methods approach is able to improve prediction performance and to reveal new insights in the data set. We recommend that this approach might be considered for future studies on the predictive model construction and risk factor analysis