| Description |
Acute gastroenteritis (AGE) outbreaks present a significant challenge to investigating public health officials, who need to know whether point source transmission - such as contaminated food, fomites, or highly infectious individuals - has occurred to respond effectively. However, information on the mode of transmission is frequently unavailable, especially during the early stages of an outbreak when control measures have the greatest impact. Clinical decision support systems (CDS) may be used to assist outbreak investigators when only limited data are available. This dissertation research investigated a) how the guidelines for norovirus outbreaks in healthcare settings vary between state public health agencies across the U.S., b) how mathematical modeling can be used to help outbreak investigators identify potential point source outbreaks, and c) how availability of outbreak information impacts public health decision-making. After introductory material in Chapters 1 and 2, Chapter 3 describes variation in norovirus outbreak guidelines and outcomes between states. Chapter 4 describes the development of a stochastic individual-level mathematical model for predicting whether an outbreak was likely caused by point source transmission. The model's internal and external validity were assessed, and the model was used to estimate potential misclassification in outbreaks reported to have been transmitted person-to-person. Chapter 5 describes semi-structured interviews with AGE epidemiologists about decision-making in hypothetical outbreak scenarios based on different levels of data availability, including results from a CDS based on the mathematical model. There was substantial variation in state healthcare-associated norovirus outbreak response guidelines, and there were differences between states with and without guidelines consistent with national guidance. The model performed well on measures of internal and external validity, and 73% of person-to-person norovirus outbreaks had at least some evidence of point source transmission. AGE epidemiologists drew different conclusions when presented with different levels of information, and there was evidence that CDS could help improve decision-making when only minimal data are available. These results demonstrate the need for CDS for transmission mode classification, the effectiveness of mathematical modeling for outbreak response decision support in some circumstances, and the potential for CDS to improve decision-making when data are sparse and public health action is required. |
