Simulation of Infectious Disease Transmission in a Hospital Emergency Department Open Access

Wang, Yuke (2014)

Permanent URL: https://etd.library.emory.edu/concern/etds/bg257f55p?locale=en
Published

Abstract

People with infectious diseases in crowded emergency department may bring about a disease outbreak and endanger public health. This paper focused on finding out whether the probabilities of disease transmissions are diverse for various types of contacts between infectious sources and exposed individuals. By applying an empirical emergency department network data in the simulation, we can predict what kind of people in the emergency department will be most dangerous if they become infectious. By comparing a number generated from a cumulative exponential distribution using the contact's duration with a random number from uniform distribution, we determined whether the contact could infect others. We initialized people in these networks as infectious one by one, and ran 10,000 simulations for all its contacts to get percentage of spreading the disease. In accordance with the results, there were 3637 nodes and 31350 contacts between them delineating one meter contacts in networks across 35 shifts. Among them, there were 6 types of nodes (222 MD, 526 RN, 515 staff, 438 admitted, 1779 not admitted, and 157 unknown) and 36 types of two-way contacts. The simulation results were analyzed at both individual level and shift level.

In both levels, the percentage of getting infected and spreading diseases through ED network was low (<1%) for all three types of patients (admitted, not admitted, and unknown). By contrast, the percentage of spreading the disease between healthcare workers is relatively high. There are some extreme outlier contacts having about 25% of getting infected for RN-RN, RN-Staff, Staff-RN, and Staff-Staff in individual level. Moreover, when we compared day and night shifts, weekday and weekend shifts, H1N1 season and not H1N1 season shifts, we found the percentage of getting infected were almost identical for these shifts, except for the standard deviation. For night shifts and not H1N1 season shifts, there were more contacts with large probability of spreading the disease. These results are helpful for understanding the patterns of infectious disease transmission through social networks. Most importantly, the results can have an important impact on helping design interventions to control the spread of infectious disease inside hospitals.

Table of Contents

Table of Contents

Chapter I Introduction & Background

Chapter II Literature Review

Chapter III Methods

Chapter IV Results

Chapter V Discussion

Reference

Appendix A

Appendix B

Appendix C

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