Date of Award
Doctor of Philosophy
Industrial and Entrepreneurial Engineering and Engineering Management
Dr. Steven Butt
Dr. Diana Prieto
Dr. Azim Houshyar
Dr. Larry Mallak
Agent-based modeling, simulation, optimization, influenza surveillance, sampling methods, experimental design
It is unclear how data collection operations for surveillance alter the disease portrayal that influenza reported trends attempt to provide during an emergency. This study developed a model that simulates the collection and testing of influenza specimens after an outbreak is declared in Michigan. It performed simulation based optimization to understand which operational factors affect the biases between the growth rates of original and observed influenza incidence trends, and to quantify the predictive power of the influenza incidence trends at different points of data collection. The results show that emergency driven high risk perception increases the reporting, which leads to the reduction of biases in the growth rates. Therefore, a recently declared emergency is a potential opportunity to collect larger sample sizes.
This study also suggests that the growth rate that better predicts the original ILI growth rate, is the one estimated from the trend of specimens submitted to the Public Health Laboratories. State Health departments can benefit from the explanatory power of the submitted trend in their efforts to improve the epidemiological characterization of emergent influenza viral strains.
Several criteria under which Public Health Laboratories can order specimens for case testing and confirmation were tested. First come first serve outperforms other criteria as long as there is enough testing capacity. When the capacity was limited, collecting first come first serve for certain groups of interest (e.g., collecting until a sample per age group is completed) seems to be a better strategy.
Gu, Yuwen, "Evaluation of Data Collection Operations for Real-Time Influenza Surveillance during an Emergency" (2019). Dissertations. 3494.