Keywords

traceback model, trade network, foodborne outbreaks, outbreak source, global sensitivity and uncertainty analyses, surveillance, value of information

Location

Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale

Start Date

18-6-2014 10:40 AM

End Date

18-6-2014 12:00 PM

Description

Foodborne diseases cause an estimated 48 million illnesses each year in the United States, including 9.4 million caused by known pathogens. Real time detection of cases and outbreak sources are important epidemic intelligence services that can decrease morbidity and mortality of foodborne illnesses, and allow optimal response to identify the causal pathways leading to contamination. For most outbreaks associated with fresh produce items, outbreak source detection typically occurs after the contaminated produce items have been consumed and are no longer in the marketplace.

We developed a probabilistic model for real time outbreak source detection, prediction of outbreaks, and contamination-prone area mapping with the aim of developing a cyber-infrastructure to support this activity. The model's inputs include environmental, trade and epidemiological dynamics. Because effective distance reliably predicts disease arrival times we estimate the distance of outbreak sources from spatio-temporal patterns of foodborne outbreaks. As a case study we consider the 2013 Cyclospora outbreaks in the USA that were related to contaminated fresh produce (cilantro and fresh salad mix) from Mexico. We are able to match case distributions related to both food commodities and determine their outbreak sources with an average accuracy of 0.93. Assuming a similar pattern of contamination for 2014, outbreak patterns can be similar or worse with an unchanged food trade that is likely. We complement the model by introducing an optimal selection algorithm of surveillance nodes based on the value of infonnation (Vol) of the surveillance network. We show how such Vol network maximize the accuracy of outbreak source detection with respect other surveillance network configurations.

The study aims to provide a methodological framework to evaluate environmentally sensitive food contamination and assess interdependencies of socio-environmental factors causing contamination. We emphasize the linkage of patterns and processes, the positive role of uncertainty, and challenge the belief that information about the whole food supply chain is needed for traceback analysis to be useful for identifying likely sources. Our specific prediction strongly emphasizes the need for real-time surveillance to identify and respond to this pending outbreak.

 
Jun 18th, 10:40 AM Jun 18th, 12:00 PM

Optimal Surveillance System Design for Outbreak Source Detection Maximization: a Vol Model

Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale

Foodborne diseases cause an estimated 48 million illnesses each year in the United States, including 9.4 million caused by known pathogens. Real time detection of cases and outbreak sources are important epidemic intelligence services that can decrease morbidity and mortality of foodborne illnesses, and allow optimal response to identify the causal pathways leading to contamination. For most outbreaks associated with fresh produce items, outbreak source detection typically occurs after the contaminated produce items have been consumed and are no longer in the marketplace.

We developed a probabilistic model for real time outbreak source detection, prediction of outbreaks, and contamination-prone area mapping with the aim of developing a cyber-infrastructure to support this activity. The model's inputs include environmental, trade and epidemiological dynamics. Because effective distance reliably predicts disease arrival times we estimate the distance of outbreak sources from spatio-temporal patterns of foodborne outbreaks. As a case study we consider the 2013 Cyclospora outbreaks in the USA that were related to contaminated fresh produce (cilantro and fresh salad mix) from Mexico. We are able to match case distributions related to both food commodities and determine their outbreak sources with an average accuracy of 0.93. Assuming a similar pattern of contamination for 2014, outbreak patterns can be similar or worse with an unchanged food trade that is likely. We complement the model by introducing an optimal selection algorithm of surveillance nodes based on the value of infonnation (Vol) of the surveillance network. We show how such Vol network maximize the accuracy of outbreak source detection with respect other surveillance network configurations.

The study aims to provide a methodological framework to evaluate environmentally sensitive food contamination and assess interdependencies of socio-environmental factors causing contamination. We emphasize the linkage of patterns and processes, the positive role of uncertainty, and challenge the belief that information about the whole food supply chain is needed for traceback analysis to be useful for identifying likely sources. Our specific prediction strongly emphasizes the need for real-time surveillance to identify and respond to this pending outbreak.