Intelli-food: Cyberinfrastructure for real-time outbreak source detection and rapid response

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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 models 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. 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.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2014, Proceedings
PublisherSpringer- Verlag
Pages181-196
Number of pages16
ISBN (Print)9783319084152
DOIs
StatePublished - Jan 1 2014
Event2nd International Conference for Smart Health, CSH 2014 - Beijing, China
Duration: Jul 10 2014Jul 11 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8549 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference for Smart Health, CSH 2014
CountryChina
CityBeijing
Period7/10/147/11/14

Keywords

  • effective distance
  • epidemic intelligence
  • food trade
  • outbreak patterns
  • outbreak source

Fingerprint Dive into the research topics of 'Intelli-food: Cyberinfrastructure for real-time outbreak source detection and rapid response'. Together they form a unique fingerprint.

Cite this