Utility of Potential Misdiagnoses in Predicting Foodborne Outbreaks

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