Utility of Potential Misdiagnoses in Predicting Foodborne Outbreaks

Authors

  • Lucia Lucia Singapore Management University, Singapore, Singapore.
  • Artur Dubrawski Carnegie Mellon University, Pittsburgh, PA, United States
  • Lujie Chen Carnegie Mellon University, Pittsburgh, PA, United States

DOI:

https://doi.org/10.5210/ojphi.v6i1.5199

Abstract

Reliable detection and accurate scoping of outbreaks of foodborne illness are important to effective mitigation of their consequences. However, relatively small number of persons affected and underreporting, challenge the reliability of surveillance models. In this work, we investigate utility of using inpatient and emergency room diagnoses to detect outbreaks of Salmonellosis in humans, and quantify the impact of including potential misdiagnoses of Salmonellosis. We found that the data support and reliability of detection could be improved by including misdiagnoses of Salmonellosis, therefore tracking these diseases could support accuracy of foodborne illness surveillance.

Author Biography

Lucia Lucia, Singapore Management University, Singapore, Singapore.

Lucia is a PhD student in School of Information System in Singapore Management University which is started since 2009. Previously, she received her master degree in information and communication technology from University of Wollongong, Australia in 2005. Her research is in software system and data mining, especially in software fault localization, software testing, and data analytic. She started working on data analysis in public health when visiting Carnegie Mellon University in 2012.

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Published

2014-03-09

How to Cite

Lucia, L., Dubrawski, A., & Chen, L. (2014). Utility of Potential Misdiagnoses in Predicting Foodborne Outbreaks. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5199

Issue

Section

Oral Presentations