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Locating the source of large-scale outbreaks of foodborne disease.

ABSTRACT: In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.

SUBMITTER: Horn AL

PROVIDER: S-EPMC6408345 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Project description:In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. Food distribution is a complex system that can be seen as a network of trade flows connecting supply chain actors. Identifying the source of an outbreak of foodborne disease distributed across this network can be solved by considering this network structure and the dimensions of information it contains. The literature on the network source identification problem has grown widely in recent years covering problems in many different contexts, from contagious disease infecting a human population, to computer viruses spreading through the Internet, to rumors or trends diffusing through a social network. Much of this work has focused on studying this problem in analytically tractable frameworks, designing approaches to work on trees and extending to general network structures in an ad hoc manner. These simplified frameworks lack many features of real-world networks and problem contexts that can dramatically impact transmission dynamics, and therefore, backwards inference of the transmission process. Moreover, the features that distinguish foodborne disease in the context of source identification have not previously been studied or identified. In this article we identify these features, then provide a review of existing work on the network source identification problem, categorizing approaches according to these features. We conclude that much of the existing work cannot be implemented in the foodborne disease problem because it makes assumptions about the transmission process that are unrealistic in the context of food supply networks—that is, identifying the source of an epidemic contagion whereas foodborne contamination spreads through a transport network-mediated diffusion process, or because it requires data that is not available—complete observations of the contamination status of all nodes in the network.

Project description:Clostridium perfringens is both an ubiquitous environmental bacterium and the fourth most common causative agent of foodborne outbreaks (FBOs) in France and Europe. These outbreaks are known to be caused by C. perfringens enterotoxin (CPE) encoded by the cpe gene. However, additional information on the toxin/virulence gene content of C. perfringens has become available in the last few years. Therefore, to understand the enteropathogenicity of this bacterium, we need to describe the toxin and virulence genes content of strains involved in FBOs. In this study, we used a new real-time PCR typing technique based on a comprehensive set of 17 genes encoding virulence factors. The analysis was performed on a collection of 141 strains involved in 42 FBOs in the Paris region. It was combined with whole genome sequence (WGS) phylogenomic reconstruction, based on the coregenome single nucleotide polymorphisms (SNPs) of 58 isolates, representatives of the identified virulence gene profiles. Two or three different virulence gene profiles were detected in 10 FBOs, demonstrating that C. perfringens FBOs may be associated with heterogeneous strains. cpe-positive strains were isolated in 23 outbreaks, confirming the prominent role of CPE in pathogenicity. However, while C. perfringens was the sole pathogen isolated from the incriminated food, the cpe gene was not detected in strains related to 13 outbreaks. This result indicates either that the standard method was not able to isolate cpe+ strains or that the cpe gene may not be the only determinant of the enterotoxigenic potential of C. perfringens strains. Using phylogenomic reconstruction, we identified two clades distinguishing chromosomal cpe-positive from cpe-negative and plasmid-borne cpe. Important epidemiological information was also garnered from this phylogenomic reconstruction that revealed unexpected links between different outbreaks associated with closely related strains (seven SNP differences) and having common virulence gene profiles. This study provides new insight into the characterization of foodborne C. perfringens and highlights the potential of WGS for the investigation of FBOs.

Project description:BackgroundFoodborne diseases are a major cause of morbidity and mortality and a major public health problem worldwide. We aim to explore characteristics of foodborne disease outbreaks (FBDOs) in Zhejiang Province and to provide data support for foodborne disease prevention and control. To our knowledge, few such analyses have been published at the provincial level.MethodsDescriptive statistical methods were used to analyze the data reported by centers for disease control (CDC) at all levels in Zhejiang Province through Foodborne Disease Outbreaks Surveillance System (FDOSS) during 2015-2020.ResultsA total of 962 FBDOs were reported during this period, resulting in 8324 illnesses, 1028 hospitalizations, and 20 deaths. The number of outbreaks (410 outbreaks, 42.62%) and cases (4991 cases, 59.96%) caused by bacteria were the largest, followed by poisonous mushrooms (157 outbreaks, 587 cases), which was the main cause of death (15 deaths, 75%). The highest number of FBDOs occurred in households (381 outbreaks, 39.60%), followed by restaurants (219 outbreaks, 22.77%) and canteens (174 outbreaks, 18.08%). Animal-based foods were the most common single food reported (232 outbreaks, 24.12%), followed by poisonous mushrooms (162 outbreaks, 16.84%), and plant-based foods (133 outbreaks, 13.83%). Poisonous mushrooms took the first place in outbreaks in households (38.32%, 146/381), while bacteria took the first place in outbreaks outside households. Vibrio parahaemolyticus was responsible for the largest number of outbreaks (232 outbreaks, 24.12%), which mainly occurred in catering service units (93.10%, 216/232). Different types of bacteria tended to be found in different food categories, such as Vibrio parahaemolyticus, which was mainly found in aquatic products.ConclusionsAnalysis of FBDOs can provide insight into the most important pathogens and sources of foodborne disease, helping authorities identify high-risk etiologies, high-risk foods, and high-risk settings to guide policies that would reduce FBDOs.

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Locating the source of large-scale outbreaks of foodborne disease.

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