Dataset Information

Reconstructing dynamics of foodborne disease outbreaks in the US cattle market from monitoring data.

ABSTRACT: Conventional empirical studies of foodborne-disease outbreaks (FDOs) in agricultural markets are linear-stochastic formulations hardwiring a world in which markets self-correct in response to external random shocks including FDOs. These formulations were unequipped to establish whether FDOs cause market reaction, or whether markets endogenously propagate outbreaks. We applied nonlinear time series analysis (NLTS) to reconstruct annual dynamics of FDOs in US cattle markets from CDC outbreak data, live cattle futures market prices, and USDA cattle inventories from 1967-2018, and used reconstructed dynamics to detect causality. Reconstructed deterministic nonlinear market dynamics are endogenously unstable-not self-correcting, and cattle inventories drive futures prices and FDOs attributed to beef in temporal patterns linked to a multi-decadal cattle cycle undetected in daily/weekly price movements investigated previously. Benchmarking real-world dynamics with NLTS offers more informative and credible empirical modeling at the convergence of natural and economic sciences.

SUBMITTER: Huffaker R

PROVIDER: S-EPMC7840002 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Reconstructing dynamics of foodborne disease outbreaks in the US cattle market from monitoring data.

Huffaker Ray R Hartmann Monika M

PloS one 20210127 1

Conventional empirical studies of foodborne-disease outbreaks (FDOs) in agricultural markets are linear-stochastic formulations hardwiring a world in which markets self-correct in response to external random shocks including FDOs. These formulations were unequipped to establish whether FDOs cause market reaction, or whether markets endogenously propagate outbreaks. We applied nonlinear time series analysis (NLTS) to reconstruct annual dynamics of FDOs in US cattle markets from CDC outbreak data, ...[more]

PMID: 33503063

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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.

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.

Dataset Information

Reconstructing dynamics of foodborne disease outbreaks in the US cattle market from monitoring data.

Publications

Reconstructing dynamics of foodborne disease outbreaks in the US cattle market from monitoring data.

Similar Datasets

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