Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany

Source identification via contact tracing in the presence of asymptomatic patients

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

The Diversity of Escherichia coli Pathotypes and Vaccination Strategies against This Versatile Bacterial Pathogen

Escherichia coli (E. coli) is a gram-negative bacillus and resident of the normal intestinal microbiota. However, some E. coli strains can cause diseases in humans, other mammals and birds ranging from intestinal infections, for example, diarrhea and dysentery, to extraintestinal infections, such as urinary tract infections, respiratory tract infections, meningitis, and sepsis. In terms of morbidity and mortality, pathogenic E. coli has a great impact on public health, with an economic cost of several billion dollars annually worldwide. Antibiotics are not usually used as first-line treatment for diarrheal illness caused by E. coli and in the case of bloody diarrhea, antibiotics are avoided due to the increased risk of hemolytic uremic syndrome. On the other hand, extraintestinal infections are treated with various antibiotics depending on the site of infection and susceptibility testing. Several alarming papers concerning the rising antibiotic resistance rates in E. coli strains have been published. The silent pandemic of multidrug-resistant bacteria including pathogenic E. coli that have become more difficult to treat favored prophylactic approaches such as E. coli vaccines. This review provides an overview of the pathogenesis of different pathotypes of E. coli, the virulence factors involved and updates on the major aspects of vaccine development against different E. coli pathotypes.

Modelling the propagation of infectious disease via transportation networks

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.

Microbial and Parasitic Contamination of Vegetables in Developing Countries and Their Food Safety Guidelines

The safety of humans is of paramount importance in the vegetable production chain. Evidence of microbial and parasitic contamination of these products poses a great threat to consumers. This is an emerging issue the world is battling, and it is still in the process of unravelling. However, one of the contributing factors responsible for the rapid spread of these pathogens to millions of people among other factors is the distribution of food in our food systems. The purpose of this study was to draw the attention of producers, retailers, consumers, and various stakeholders to the occurrence and potential hazard of these organisms, their contamination origin, and food safety protocols. Among the food system, vegetables play a major role, and their consumption has increased as they form a larger portion of daily diets. This urge for healthy diets coupled with changing dietary habits and human population explosion has therefore accelerated their production. This has resulted in parasitic and microbial contamination gaining grounds in salad vegetables, and as such, a wide range of microbes such as Escherichia coli O157: H7, Listeria monocytogenes, Salmonella spp., Shigella, and Staphylococcus, and parasites such as Giardia lamblia, Entamoeba coli, Entamoeba histolytica, Cystoisospora belli, Toxoplasma gondii, Trichuris trichiura, and Ascaris lumbricoides have been isolated from them. Therefore, major routes for salad vegetable contamination and prevention methods have been pointed out in this review article. The topic of protective countermeasures will also be covered here in this review. Notwithstanding, several control measures have been reported to be effective and efficient in removing or eliminating pathogens, including treatment of irrigation water and fertilizers, use of disinfectants like vinegar and saltwater, irradiation, ozone, and bacteriophages. Though consumption of vegetables and salads is encouraged due to their nutritional advantage, appropriate systems should be put in place to ensure their safety.

Sensor-based localization of epidemic sources on human mobility networks

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

Tracking the source of an epidemic outbreak is of crucial importance as it allows for identification of communities where control efforts should be focused for both short and long-term management and control of the disease. However, such identification is often problematic, time-consuming, and data-intensive. Recently network-based analysis approaches have been established for source detection to account for complex modern spreading, driven substantially by human mobility. Here we develop a probabilistic framework for waterborne disease, that allows investigators to infer the community or the region sparking an outbreak based on a sparse surveillance network. The framework can integrate prior information on the likelihood of a community being the source, for instance as a function of population size or hygiene conditions. Furthermore, we assign an accuracy measure to the resulting source estimate, which is crucial for its practical usability. We test the method in the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province with promising results. Moreover, we outline a series of guidelines in terms of data needs and preliminary operations to implement the proposed framework in practice.

Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy

Confirmed cases of the disease COVID-19 have spread to more than 200 countries and regions of the world within a few months. Although the authorities report the number of new cases on daily basis, there remains a gap between the number of reported cases and actual number of cases in a population. One way to bridge this gap is to gain more in-depth understanding of the disease. In this paper, we have used the recent findings about the clinical courses of inpatients with COVID-19 to reset the initial conditions of the epidemic process in order to estimate more realistic number of cases in the population. By translating the reported cases certain number of days earlier with regard to an average clinical course of the disease, we have obtained much higher number of cases, which suggests that the actual number of infected cases and death rate might have been higher than reported. Based on the outbreak of COVID-19 in Italy, this paper shows an estimate of the number of infected cases based on infection and removal rates from data during the pandemic.

Locating the source of large-scale outbreaks of foodborne disease

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.

Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

Influenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks

Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.

Methods for generating hypotheses in human enteric illness outbreak investigations: a scoping review of the evidence

Enteric illness outbreaks are complex events, therefore, outbreak investigators use many different hypothesis generation methods depending on the situation. This scoping review was conducted to describe methods used to generate a hypothesis during enteric illness outbreak investigations. The search included five databases and grey literature for articles published between 1 January 2000 and 2 May 2015. Relevance screening and article characterisation were conducted by two independent reviewers using pretested forms. There were 903 outbreaks that described hypothesis generation methods and 33 papers which focused on the evaluation of hypothesis generation methods. Common hypothesis generation methods described are analytic studies (64.8%), descriptive epidemiology (33.7%), food or environmental sampling (32.8%) and facility inspections (27.9%). The least common methods included the use of a single interviewer (0.4%) and investigation of outliers (0.4%). Most studies reported using two or more methods to generate hypotheses (81.2%), with 29.2% of studies reporting using four or more. The use of multiple different hypothesis generation methods both within and between outbreaks highlights the complexity of enteric illness outbreak investigations. Future research should examine the effectiveness of each method and the contexts for which each is most effective in efficiently leading to source identification.

The Network Source Location Problem in the Context of Foodborne Disease Outbreaks

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.

Spatial constraints on the diffusion of religious innovations: The case of early Christianity in the Roman Empire

Christianity emerged as a small and marginal movement in the first century Palestine and throughout the following three centuries it became highly visible in the whole Mediterranean. Little is known about the mechanisms of spreading innovative ideas in past societies. Here we investigate how well the spread of Christianity can be explained as a diffusive process constrained by physical travel in the Roman Empire. First, we combine a previously established model of the transportation network with city population estimates and evaluate to which extent the spatio-temporal pattern of the spread of Christianity can be explained by static factors. Second, we apply a network-theoretical approach to analyze the spreading process utilizing effective distance. We show that the spread of Christianity in the first two centuries closely follows a gravity-guided diffusion, and is substantially accelerated in the third century. Using the effective distance measure, we are able to suggest the probable path of the spread. Our work demonstrates how the spatio-temporal patterns we observe in the data can be explained using only spatial constraints and urbanization structure of the empire. Our findings also provide a methodological framework to be reused for studying other cultural spreading phenomena.

Range Expansion and the Origin of USA300 North American Epidemic Methicillin-Resistant Staphylococcus aureus

The USA300 North American epidemic (USA300-NAE) clone of methicillin-resistant Staphylococcus aureus has caused a wave of severe skin and soft tissue infections in the United States since it emerged in the early 2000s, but its geographic origin is obscure. Here we use the population genomic signatures expected from the serial founder effects of a geographic range expansion to infer the origin of USA300-NAE and identify polymorphisms associated with its spread. Genome sequences from 357 isolates from 22 U.S. states and territories and seven other countries are compared. We observe two significant signatures of range expansion, including decreases in genetic diversity and increases in derived allele frequency with geographic distance from the Pennsylvania region. These signatures account for approximately half of the core nucleotide variation of this clone, occur genome wide, and are robust to heterogeneity in temporal sampling of isolates, human population density, and recombination detection methods. The potential for positive selection of a gyrA fluoroquinolone resistance allele and several intergenic regions, along with a 2.4 times higher recombination rate in a resistant subclade, is noted. These results are the first to show a pattern of genetic variation that is consistent with a range expansion of an epidemic bacterial clone, and they highlight a rarely considered but potentially common mechanism by which genetic drift may profoundly influence bacterial genetic variation.

The process of geographic spread of an origin population by a series of smaller populations can result in distinctive patterns of genetic variation. We detect these patterns for the first time with an epidemic bacterial clone and use them to uncover the clone’s geographic origin and variants associated with its spread. We study the USA300 clone of methicillin-resistant Staphylococcus aureus, which was first noticed in the early 2000s and subsequently became the leading cause of skin and soft tissue infections in the United States. The eastern United States is the most likely origin of epidemic USA300. Relatively few variants, which include an antibiotic resistance mutation, have persisted during this clone’s spread. Our study suggests that an early chapter in the genetic history of this epidemic bacterial clone was greatly influenced by random subsampling of isolates during the clone’s geographic spread.

Public Health Research Resulting from One of the World's Largest Outbreaks Caused by Entero-Hemorrhagic Escherichia coli in Germany 2011: A Review

In 2011, Germany experienced one of the largest outbreaks of entero-hemorrhagic Escherichia coli (EHEC) ever reported. Four years thereafter, we systematically searched for scientific publications in PubMed and MEDPILOT relating to this outbreak in order to assess the pattern of respective research activities and to assess the main findings and recommendations in the field of public health. Following PRISMA guidelines, we selected 133 publications, half of which were published within 17 months after outbreak onset. Clinical medicine was covered by 71, microbiology by 60, epidemiology by 46, outbreak reporting by 11, and food safety by 9 papers. Those on the last three topics drew conclusions on methods in surveillance, diagnosis, and outbreak investigation, on resources in public health, as well as on inter-agency collaboration, and public communication. Although the outbreak primarily affected Germany, most publications were conducted by multinational cooperations. Our findings document how soon and in which fields research was conducted with respect to this outbreak.

Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011

In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence.

Optimal surveillance network design: a value of information model

Purpose

Infectious diseases are the second leading cause of deaths worldwide, accounting for 15 million deaths – that is more than 25% of all deaths – each year. Food plays a crucial role, contributing to 1.5 million deaths, most of which are children, through foodborne diarrheal disease alone. Thus, the ability to timely detect outbreak pathways via high-efficiency surveillance system is essential to the physical and social well being of populations. For this purpose, a traceability model inspired by wavepattern recognition models to detect “zero-patient” areas based on outbreak spread is proposed.

Methods

Model effectiveness is assessed for data from the 2010 Cholera epidemic in Cameroon, the 2012 foodborne Salmonella epidemic in USA, and the 2004-2007 H5N1 avian influenza pandemic. Previous models are complemented by the introduction of an optimal selection algorithm of surveillance networks based on the Value of Information (VoI) of reporting nodes that are subnetworks of mobility networks in which people, food, and species move. The surveillance network is considered the response variable to be determined in maximizing the accuracy of outbreak source detections while minimizing detection error. Surveillance network topologies are selected by considering their integrated network resilience expressing the rewiring probability that is related to the ability to report outbreak information even in case of network destruction or missing information.

Results

Independently of the outbreak epidemiology, the maximization of the VoI leads to a minimum increase in accuracy of 40% compared to the random surveillance model. Such accuracy is accompanied by an average reduction of 25% in required surveillance nodes with respect to random surveillance. Accuracy in systems diagnosis increases when system syndromic signs are the most informative in a way they reveal linkages between outbreak patterns and network transmission processes.

Conclusions

The model developed is extremely useful for the optimization of surveillance networks to drastically reduce the burden of food-borne and other infectious diseases. The model can be the framework of a cyber-technology that governments and industries can utilize in a real-time manner to avoid catastrophic and costly health and economic outcomes. Further applications are envisioned for chronic diseases, socially communicable diseases, biodefense and other detection related problems at different scales.

Understanding and predicting the global spread of emergent infectious diseases

The emergence and global spread of human infectious diseases has become one of the most serious public health threats of the 21st century. Sophisticated computer simulations have become a key tool for understanding and predicting disease spread on a global scale. Combining theoretical insights from nonlinear dynamics, stochastic processes and complex network theory these computational models are becoming increasingly important in the design of efficient mitigation and control strategies and for public health in general.