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Bumunang EW, Zaheer R, Niu D, Narvaez-Bravo C, Alexander T, McAllister TA, Stanford K. Bacteriophages for the Targeted Control of Foodborne Pathogens. Foods 2023; 12:2734. [PMID: 37509826 PMCID: PMC10379335 DOI: 10.3390/foods12142734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Foodborne illness is exacerbated by novel and emerging pathotypes, persistent contamination, antimicrobial resistance, an ever-changing environment, and the complexity of food production systems. Sporadic and outbreak events of common foodborne pathogens like Shiga toxigenic E. coli (STEC), Salmonella, Campylobacter, and Listeria monocytogenes are increasingly identified. Methods of controlling human infections linked with food products are essential to improve food safety and public health and to avoid economic losses associated with contaminated food product recalls and litigations. Bacteriophages (phages) are an attractive additional weapon in the ongoing search for preventative measures to improve food safety and public health. However, like all other antimicrobial interventions that are being employed in food production systems, phages are not a panacea to all food safety challenges. Therefore, while phage-based biocontrol can be promising in combating foodborne pathogens, their antibacterial spectrum is generally narrower than most antibiotics. The emergence of phage-insensitive single-cell variants and the formulation of effective cocktails are some of the challenges faced by phage-based biocontrol methods. This review examines phage-based applications at critical control points in food production systems with an emphasis on when and where they can be successfully applied at production and processing levels. Shortcomings associated with phage-based control measures are outlined together with strategies that can be applied to improve phage utility for current and future applications in food safety.
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Affiliation(s)
- Emmanuel W Bumunang
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 1M4, Canada
| | - Rahat Zaheer
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
| | - Dongyan Niu
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Claudia Narvaez-Bravo
- Food and Human Nutritional Sciences, Faculty of Agricultural & Food Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Trevor Alexander
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
| | - Tim A McAllister
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
| | - Kim Stanford
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 1M4, Canada
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2
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Childs LM, El Moustaid F, Gajewski Z, Kadelka S, Nikin-Beers R, Smith JW, Walker M, Johnson LR. Linked within-host and between-host models and data for infectious diseases: a systematic review. PeerJ 2019; 7:e7057. [PMID: 31249734 PMCID: PMC6589080 DOI: 10.7717/peerj.7057] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/28/2019] [Indexed: 12/17/2022] Open
Abstract
The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.
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Affiliation(s)
- Lauren M Childs
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Fadoua El Moustaid
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Zachary Gajewski
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Sarah Kadelka
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Ryan Nikin-Beers
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Mathematics, University of Florida, Gainesville, FL, USA
| | - John W Smith
- Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Melody Walker
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Leah R Johnson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Computational Modeling and Data Analytics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
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3
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Dawson DE, Farthing TS, Sanderson MW, Lanzas C. Transmission on empirical dynamic contact networks is influenced by data processing decisions. Epidemics 2019; 26:32-42. [PMID: 30528207 PMCID: PMC6613374 DOI: 10.1016/j.epidem.2018.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 08/01/2018] [Accepted: 08/27/2018] [Indexed: 11/02/2022] Open
Abstract
Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R0, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.
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Affiliation(s)
- Daniel E Dawson
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA.
| | - Trevor S Farthing
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA
| | - Michael W Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA
| | - Cristina Lanzas
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA
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McAloon CG, Roche S, Ritter C, Barkema HW, Whyte P, More SJ, O'Grady L, Green MJ, Doherty ML. A review of paratuberculosis in dairy herds - Part 1: Epidemiology. Vet J 2019; 246:59-65. [PMID: 30902190 DOI: 10.1016/j.tvjl.2019.01.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 01/24/2019] [Accepted: 01/25/2019] [Indexed: 11/24/2022]
Abstract
Bovine paratuberculosis is a chronic infectious disease of cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the first in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Paratuberculosis was originally described in 1895 and is now considered endemic among farmed cattle worldwide. MAP has been isolated from a wide range of non-ruminant wildlife as well as humans and non-human primates. In dairy herds, MAP is assumed to be introduced predominantly through the purchase of infected stock with additional factors modulating the risk of persistence or fade-out once an infected animal is introduced. Faecal shedding may vary widely between individuals and recent modelling work has shed some light on the role of super-shedding animals in the transmission of MAP within herds. Recent experimental work has revisited many of the assumptions around age susceptibility, faecal shedding in calves and calf-to-calf transmission. Further efforts to elucidate the relative contributions of different transmission routes to the dissemination of infection in endemic herds will aid in the prioritisation of efforts for control on farm.
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Affiliation(s)
- Conor G McAloon
- Section of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland.
| | - Steven Roche
- Department of Population Medicine, University of Guelph, 50 Stone Rd., Guelph, ON, N1G 2W1, Canada
| | - Caroline Ritter
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, 2500 University Drive, Calgary, AB, T2N 1N4, Canada
| | - Herman W Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, 2500 University Drive, Calgary, AB, T2N 1N4, Canada
| | - Paul Whyte
- Section of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland
| | - Simon J More
- Section of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland
| | - Luke O'Grady
- Section of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland
| | - Martin J Green
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, United Kingdom
| | - Michael L Doherty
- Section of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Ireland
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Dawson DE, Keung JH, Napoles MG, Vella MR, Chen S, Sanderson MW, Lanzas C. Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model. PLoS One 2018; 13:e0205418. [PMID: 30304002 PMCID: PMC6179278 DOI: 10.1371/journal.pone.0205418] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 09/25/2018] [Indexed: 11/18/2022] Open
Abstract
The causes of seasonal variability in pathogen transmission are not well understood, and have not been comprehensively investigated. In an example for enteric pathogens, incidence of Escherichia coli O157 (STEC) colonization in cattle is consistently higher during warmer months compared to cooler months in various cattle production systems. However, actual mechanisms for this seasonality remain elusive. In addition, the influence of host (cattle) behavior on this pattern has not been thoroughly considered. To that end, we constructed a spatially explicit agent-based model that accounted for the effect of temperature fluctuations on cattle behavior (direct contact among cattle and indirect between cattle and environment), as well as its effect on pathogen survival in the environment. We then simulated the model in a factorial approach to evaluate the hypothesis that temperature fluctuations can lead to seasonal STEC transmission dynamics by influencing cattle aggregation, grazing, and drinking behaviors. Simulation results showed that higher temperatures increased the frequency at which cattle aggregated under shade in pasture, resulting in increased direct contact and transmission of STEC between individual cattle, and hence higher incidence over model simulations in the warm season. In contrast, increased drinking behavior during warm season was not an important transmission pathway. Although sensitivity analyses suggested that the relative importance of direct vs. indirect (environmental) pathways depend to upon model parameterization, model simulations indicated that factors influencing cattle aggregation, such as temperature, were likely strong drivers of transmission dynamics of enteric pathogens.
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Affiliation(s)
- Daniel E. Dawson
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
| | - Jocelyn H. Keung
- National Institute for Mathematical and Biological Synthesis (NIMBioS), Knoxville, Tennessee, United States of America
| | - Monica G. Napoles
- National Institute for Mathematical and Biological Synthesis (NIMBioS), Knoxville, Tennessee, United States of America
| | - Michael R. Vella
- National Institute for Mathematical and Biological Synthesis (NIMBioS), Knoxville, Tennessee, United States of America
| | - Shi Chen
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, North Carolina, United States of America
| | - Michael W. Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, United States of America
| | - Cristina Lanzas
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
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Abstract
This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.
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Chen S, Sanderson MW, Lee C, Cernicchiaro N, Renter DG, Lanzas C. Basic Reproduction Number and Transmission Dynamics of Common Serogroups of Enterohemorrhagic Escherichia coli. Appl Environ Microbiol 2016; 82:5612-20. [PMID: 27401976 PMCID: PMC5007764 DOI: 10.1128/aem.00815-16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 07/01/2016] [Indexed: 11/20/2022] Open
Abstract
UNLABELLED Understanding the transmission dynamics of pathogens is essential to determine the epidemiology, ecology, and ways of controlling enterohemorrhagic Escherichia coli (EHEC) in animals and their environments. Our objective was to estimate the epidemiological fitness of common EHEC strains in cattle populations. For that purpose, we developed a Markov chain model to characterize the dynamics of 7 serogroups of enterohemorrhagic Escherichia coli (O26, O45, O103, O111, O121, O145, and O157) in cattle production environments based on a set of cross-sectional data on infection prevalence in 2 years in two U.S. states. The basic reproduction number (R0) was estimated using a Bayesian framework for each serogroup based on two criteria (using serogroup alone [the O-group data] and using O serogroup, Shiga toxin gene[s], and intimin [eae] gene together [the EHEC data]). In addition, correlations between external covariates (e.g., location, ambient temperature, dietary, and probiotic usage) and prevalence/R0 were quantified. R0 estimates varied substantially among different EHEC serogroups, with EHEC O157 having an R0 of >1 (∼1.5) and all six other EHEC serogroups having an R0 of less than 1. Using the O-group data substantially increased R0 estimates for the O26, O45, and O103 serogroups (R0 > 1) but not for the others. Different covariates had distinct influences on different serogroups: the coefficients for each covariate were different among serogroups. Our modeling and analysis of this system can be readily expanded to other pathogen systems in order to estimate the pathogen and external factors that influence spread of infectious agents. IMPORTANCE In this paper we describe a Bayesian modeling framework to estimate basic reproduction numbers of multiple serotypes of Shiga toxin-producing Escherichia coli according to a cross-sectional study. We then coupled a compartmental model to reconstruct the infection dynamics of these serotypes and quantify their risk in the population. We incorporated different sensitivity levels of detecting different serotypes and evaluated their potential influence on the estimation of basic reproduction numbers.
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Affiliation(s)
- Shi Chen
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, North Carolina, USA Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA
| | - Michael W Sanderson
- Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, Kansas, USA
| | - Chihoon Lee
- School of Business, Stevens Institute of Technology, Hoboken, New Jersey, USA Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Natalia Cernicchiaro
- Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, Kansas, USA
| | - David G Renter
- Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, Kansas, USA
| | - Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA
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Lanzas C, Chen S. Complex system modelling for veterinary epidemiology. Prev Vet Med 2014; 118:207-14. [PMID: 25449734 DOI: 10.1016/j.prevetmed.2014.09.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Revised: 07/29/2014] [Accepted: 09/09/2014] [Indexed: 11/16/2022]
Abstract
The use of mathematical models has a long tradition in infectious disease epidemiology. The nonlinear dynamics and complexity of pathogen transmission pose challenges in understanding its key determinants, in identifying critical points, and designing effective mitigation strategies. Mathematical modelling provides tools to explicitly represent the variability, interconnectedness, and complexity of systems, and has contributed to numerous insights and theoretical advances in disease transmission, as well as to changes in public policy, health practice, and management. In recent years, our modelling toolbox has considerably expanded due to the advancements in computing power and the need to model novel data generated by technologies such as proximity loggers and global positioning systems. In this review, we discuss the principles, advantages, and challenges associated with the most recent modelling approaches used in systems science, the interdisciplinary study of complex systems, including agent-based, network and compartmental modelling. Agent-based modelling is a powerful simulation technique that considers the individual behaviours of system components by defining a set of rules that govern how individuals ("agents") within given populations interact with one another and the environment. Agent-based models have become a recent popular choice in epidemiology to model hierarchical systems and address complex spatio-temporal dynamics because of their ability to integrate multiple scales and datasets.
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Affiliation(s)
- Cristina Lanzas
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, 2407 River Drive, Knoxville, TN 37996, USA; National Institute for Mathematical and Biological Synthesis, University of Tennessee, 1122 Volunteer Blvd, Knoxville, TN 37996, USA.
| | - Shi Chen
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, 2407 River Drive, Knoxville, TN 37996, USA
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Williams K, Ward M, Dhungyel O, Hall E, Van Breda L. A longitudinal study of the prevalence and super-shedding of Escherichia coli O157 in dairy heifers. Vet Microbiol 2014; 173:101-9. [DOI: 10.1016/j.vetmic.2014.07.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Revised: 06/28/2014] [Accepted: 07/04/2014] [Indexed: 12/13/2022]
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10
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Highly dynamic animal contact network and implications on disease transmission. Sci Rep 2014; 4:4472. [PMID: 24667241 PMCID: PMC3966050 DOI: 10.1038/srep04472] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 03/10/2014] [Indexed: 11/29/2022] Open
Abstract
Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmission. The animal contact network aggregated at hourly level was highly variable and dynamic within and between days, for both network structure (network degree distribution) and individual rank of degree distribution in the network (degree order). We integrated network degree distribution and degree order heterogeneities with a commonly used contact-based, directly transmitted disease model to quantify the effect of these two sources of heterogeneity on the infectious disease dynamics. Four conditions were simulated based on the combination of these two heterogeneities. Simulation results indicated that disease dynamics and individual contribution to new infections varied substantially among these four conditions under both parameter settings. Changes in the contact network had a greater effect on disease dynamics for pathogens with smaller basic reproduction number (i.e. R0 < 2).
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Temporal-spatial heterogeneity in animal-environment contact: implications for the exposure and transmission of pathogens. Sci Rep 2013; 3:3112. [PMID: 24177808 PMCID: PMC3814814 DOI: 10.1038/srep03112] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 10/11/2013] [Indexed: 11/22/2022] Open
Abstract
Contact structure, a critical driver of infectious disease transmission, is not completely understood and characterized for environmentally transmitted pathogens. In this study, we assessed the effects of temporal and spatial heterogeneity in animal contact structures on the dynamics of environmentally transmitted pathogens. We used real-time animal position data to describe contact between animals and specific environmental areas used for feeding and watering calves. The generated contact structure varied across days and among animals. We integrated animal and environmental heterogeneity into an agent-based simulation model for Escherichia coli O157 environmental transmission in cattle to simulate four different scenarios with different environmental bacteria concentrations at different areas. The simulation results suggest heterogeneity in environmental contact structure among cattle influences pathogen prevalence and exposure associated with each environment. Our findings suggest that interventions that target environmental areas, even relatively small areas, with high bacterial concentration can result in effective mitigation of environmentally transmitted pathogens.
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