1
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Seger HL, Sanderson MW, White BJ, Lanzas C. Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle. Prev Vet Med 2024; 227:106210. [PMID: 38688092 PMCID: PMC11247509 DOI: 10.1016/j.prevetmed.2024.106210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 03/26/2024] [Accepted: 04/14/2024] [Indexed: 05/02/2024]
Abstract
Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared "fenceline" area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. "Fenceline" contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.
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Affiliation(s)
- H L Seger
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States
| | - M W Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States.
| | - B J White
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States
| | - C Lanzas
- Department of Population Health and Pathobiology, North Carolina State University College of Veterinary Medicine, Raleigh, NC 27606, United States
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2
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Savagar B, Jones BA, Arnold M, Walker M, Fournié G. Modelling flock heterogeneity in the transmission of peste des petits ruminants virus and its impact on the effectiveness of vaccination for eradication. Epidemics 2023; 45:100725. [PMID: 37935076 DOI: 10.1016/j.epidem.2023.100725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/29/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
Peste des petits ruminants (PPR) is an acute infectious disease of small ruminants targeted for global eradication by 2030. The Global Strategy for Control and Eradication (GSCE) recommends mass vaccination targeting 70% coverage of small ruminant populations in PPR-endemic regions. These small ruminant populations are diverse with heterogeneous mixing patterns that may influence PPR virus (PPRV) transmission dynamics. This paper evaluates the impact of heterogeneous mixing on (i) PPRV transmission and (ii) the likelihood of different vaccination strategies achieving PPRV elimination, including the GSCE recommended strategy. We develop models simulating heterogeneous transmission between hosts, including a metapopulation model of PPRV transmission between villages in lowland Ethiopia fitted to serological data. Our results demonstrate that although heterogeneous mixing of small ruminant populations increases the instability of PPRV transmission-increasing the chance of fadeout in the absence of intervention-a vaccination coverage of 70% may be insufficient to achieve elimination if high-risk populations are not targeted. Transmission may persist despite very high vaccination coverage (>90% small ruminants) if vaccination is biased towards more accessible but lower-risk populations such as sedentary small ruminant flocks. These results highlight the importance of characterizing small ruminant mobility patterns and identifying high-risk populations for vaccination and support a move towards targeted, risk-based vaccination programmes in the next phase of the PPRV eradication programme. Our modelling approach also illustrates a general framework for incorporating heterogeneous mixing patterns into models of directly transmitted infectious diseases where detailed contact data are limited. This study improves understanding of PPRV transmission and elimination in heterogeneous small ruminant populations and should be used to inform and optimize the design of PPRV vaccination programmes.
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Affiliation(s)
- Bethan Savagar
- Veterinary Epidemiology, Economics and Public Health Group, WOAH Collaborating Centre for Risk Analysis and Modelling, Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK.
| | - Bryony A Jones
- Department of Epidemiological Sciences, WOAH Collaborating Centre in Risk Analysis and Modelling, Animal and Plant Health Agency (APHA), Addlestone, Surrey, UK
| | - Mark Arnold
- Department of Epidemiological Sciences, WOAH Collaborating Centre in Risk Analysis and Modelling, Animal and Plant Health Agency (APHA), Addlestone, Surrey, UK
| | - Martin Walker
- Veterinary Epidemiology, Economics and Public Health Group, WOAH Collaborating Centre for Risk Analysis and Modelling, Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK; London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - Guillaume Fournié
- Veterinary Epidemiology, Economics and Public Health Group, WOAH Collaborating Centre for Risk Analysis and Modelling, Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France; Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint Genes Champanelle, France
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3
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Southall E, Ogi-Gittins Z, Kaye AR, Hart WS, Lovell-Read FA, Thompson RN. A practical guide to mathematical methods for estimating infectious disease outbreak risks. J Theor Biol 2023; 562:111417. [PMID: 36682408 DOI: 10.1016/j.jtbi.2023.111417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023]
Abstract
Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- E Southall
- Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Z Ogi-Gittins
- Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - A R Kaye
- Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - W S Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | | | - R N Thompson
- Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.
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4
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Quantifying changes in the British cattle movement network. Prev Vet Med 2021; 198:105524. [PMID: 34775127 DOI: 10.1016/j.prevetmed.2021.105524] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 10/21/2021] [Accepted: 10/24/2021] [Indexed: 12/22/2022]
Abstract
The modelling of disease spread is crucial to the farming industry and policy makers. In some of these industries, excellent data exist on animal movements, along with the networks that these movements create, and allow researchers to model spread of disease (both epidemic and endemic). The Cattle Tracing System is an online recording system for cattle births, deaths and between-herd movements in the United Kingdom and is an excellent resource for any researchers interested in networks or modelling infectious disease spread through the UK cattle system. Data exist that cover many years, and it can be useful to know how much change is occurring in a network, to help judge the merit of using historical data within a modelling context. This article uses the data to construct weighted directed monthly movement networks for two distinct periods of time, 2004-2006 and 2015-2017, to quantify by how much the underlying structure of the network has changed. Substantial changes in network structure may influence policy-makers directly or may influence models built upon the network data, and these in turn could impact policy-makers and their assessment of risk. We examined 13 network metrics, ranging from general descriptive metrics such as total number of nodes with movements and total movements, through to metrics to describe the network (e.g., Giant weakly and strongly connected components) and metrics calculated per node (betweenness, degree and strength). Mixed effect models show that there is a statistically significant effect of the period (2004-2006 vs 2015-2017) in the values of nine of the 13 network metrics. For example median total degree decreased by 19%. In addition to examining networks for two time periods, two updates of the data were examined to determine by how much the movement data stored for 2004-2006 had been cleansed between updates. Examination of these updates shows that there are small decreases in problem movements (such as animals leaving slaughterhouses) and therefore evidence of historical data being improved between updates. In combination with the significant effect of period on many of the network metrics, the modification of data between updates provides further evidence that the most recent available data should be used for network modelling. This will ensure that the most representative descriptions of the network are available to provide accurate modelling results to best inform policy makers.
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5
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Okello WO, Amongi CA, Muhanguzi D, MacLeod ET, Waiswa C, Shaw AP, Welburn SC. Livestock Network Analysis for Rhodesiense Human African Trypanosomiasis Control in Uganda. Front Vet Sci 2021; 8:611132. [PMID: 34262958 PMCID: PMC8273440 DOI: 10.3389/fvets.2021.611132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness. Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (R o 1.287), hence regarded as "slow" pathogen, and evaluate the effects of disease interventions. Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network. Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public-private partnerships, would serve to limit its spread.
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Affiliation(s)
- Walter O. Okello
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Commonwealth and Scientific Research Organization, Land & Water Business Unit, Canberra, ACT, Australia
| | - Christine A. Amongi
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Dennis Muhanguzi
- Biotechnical and Laboratory Sciences, Department of Biomolecular and Biolaboratory Sciences, School of Biosecurity, College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Ewan T. MacLeod
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Charles Waiswa
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Biotechnical and Laboratory Sciences, Department of Biomolecular and Biolaboratory Sciences, School of Biosecurity, College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
- The Coordinating Office for Control of Trypanosomiasis in Uganda (COCTU), Kampala, Uganda
| | - Alexandra P. Shaw
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Avia-GIS, Zoersel, Belgium
| | - Susan C. Welburn
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
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6
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Fielding HR, McKinley TJ, Delahay RJ, Silk MJ, McDonald RA. Characterization of potential superspreader farms for bovine tuberculosis: A review. Vet Med Sci 2020; 7:310-321. [PMID: 32937038 PMCID: PMC8025614 DOI: 10.1002/vms3.358] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/22/2020] [Accepted: 08/29/2020] [Indexed: 11/24/2022] Open
Abstract
Background Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’. Objectives and Methods We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number ‐ Rf: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain. Results Farms exhibit marked heterogeneity in contact rates arising from between‐farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within‐herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites. Conclusions Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real‐time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.
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Affiliation(s)
- Helen R Fielding
- Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, UK
| | | | - Richard J Delahay
- National Wildlife Management Centre, Animal and Plant Health Agency, Stonehouse, Gloucestershire, UK
| | - Matthew J Silk
- Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, UK
| | - Robbie A McDonald
- Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, UK
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7
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Ezanno P, Andraud M, Beaunée G, Hoch T, Krebs S, Rault A, Touzeau S, Vergu E, Widgren S. How mechanistic modelling supports decision making for the control of enzootic infectious diseases. Epidemics 2020; 32:100398. [PMID: 32622313 DOI: 10.1016/j.epidem.2020.100398] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/07/2020] [Accepted: 05/29/2020] [Indexed: 12/28/2022] Open
Abstract
Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.
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Affiliation(s)
- P Ezanno
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - M Andraud
- Unité épidémiologie et bien-être du porc, Anses Laboratoire de Ploufragan-Plouzané, Ploufragan, France.
| | - G Beaunée
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - T Hoch
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Krebs
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - A Rault
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Touzeau
- INRAE, CNRS, Université Côte d'Azur, ISA, France; Inria, INRAE, CNRS, Université Paris Sorbonne, Université Côte d'Azur, BIOCORE, France.
| | - E Vergu
- INRAE, Université Paris-Saclay, MaIAGE, 78350 Jouy-en-Josas, France.
| | - S Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden.
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8
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Thompson RN, Thompson CP, Pelerman O, Gupta S, Obolski U. Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic. Philos Trans R Soc Lond B Biol Sci 2020; 374:20180274. [PMID: 31056047 DOI: 10.1098/rstb.2018.0274] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains-a low virulence (LV) strain outbreak followed by a high virulence (HV) strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the HV strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the HV strain, driven by exposure to the LV strain. As a result, major epidemics of the HV strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.
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Affiliation(s)
- R N Thompson
- 1 Mathematical Institute, University of Oxford , Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG , UK.,2 Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS , UK.,3 Christ Church, University of Oxford , St Aldate's, Oxford OX1 1DP , UK
| | - C P Thompson
- 2 Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS , UK
| | - O Pelerman
- 4 The Chaim Rosenberg School of Jewish Studies, Tel Aviv University , Tel Aviv 69978 , Israel
| | - S Gupta
- 2 Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS , UK
| | - U Obolski
- 2 Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS , UK.,5 School of Public Health , Tel Aviv University, Tel Aviv , Israel.,6 Porter School of the Environment and Earth Sciences, Tel Aviv University , Israel
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9
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Hall RJ. Modeling the Effects of Resource-Driven Immune Defense on Parasite Transmission in Heterogeneous Host Populations. Integr Comp Biol 2020; 59:1253-1263. [PMID: 31127280 DOI: 10.1093/icb/icz074] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Individuals experience heterogeneous environmental conditions that can affect within-host processes such as immune defense against parasite infection. Variation among individuals in parasite shedding can cause some hosts to contribute disproportionately to population-level transmission, but we currently lack mechanistic theory that predicts when environmental conditions can result in large disease outbreaks through the formation of immunocompromised superspreading individuals. Here, I present a within-host model of a microparasite's interaction with the immune system that links an individual host's resource intake to its infectious period. For environmental scenarios driving population-level heterogeneity in resource intake (resource scarcity and resource subsidy relative to baseline availability), I generate a distribution of infectious periods and simulate epidemics on these heterogeneous populations. I find that resource scarcity can result in large epidemics through creation of superspreading individuals, while resource subsidies can reduce or prevent transmission of parasites close to their invasion threshold by homogenizing resource allocation to immune defense. Importantly, failure to account for heterogeneity in competence can result in under-prediction of outbreak size, especially when parasites are close to their invasion threshold. More generally, this framework suggests that differences in conditions experienced by individual hosts can lead to superspreading via differences in resource allocation to immune defense alone, even in the absence of other heterogeneites such as host contacts.
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Affiliation(s)
- Richard J Hall
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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10
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Li Y, Huang B, Shen C, Cai C, Wang Y, Edwards J, Zhang G, Robertson ID. Pig trade networks through live pig markets in Guangdong Province, China. Transbound Emerg Dis 2020; 67:1315-1329. [PMID: 31903722 PMCID: PMC7228257 DOI: 10.1111/tbed.13472] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 12/27/2019] [Accepted: 12/27/2019] [Indexed: 11/28/2022]
Abstract
This study used social network analysis to investigate the indirect contact network between counties through the movement of live pigs through four wholesale live pig markets in Guangdong Province, China. All 14,118 trade records for January and June 2016 were collected from the markets and the patterns of pig trade in these markets analysed. Maps were developed to show the movement pathways. Evaluating the network between source counties was the primary objective of this study. A 1‐mode network was developed. Characteristics of the trading network were explored, and the degree, betweenness and closeness were calculated for each source county. Models were developed to compare the impacts of different disease control strategies on the potential magnitude of an epidemic spreading through this network. The results show that pigs from 151 counties were delivered to the four wholesale live pig markets in January and/or June 2016. More batches (truckloads of pigs sourced from one or more piggeries) were traded in these markets in January (8,001) than in June 2016 (6,117). The pigs were predominantly sourced from counties inside Guangdong Province (90%), along with counties in Hunan, Guangxi, Jiangxi, Fujian and Henan provinces. The major source counties (46 in total) contributed 94% of the total batches during the two‐month study period. Pigs were sourced from piggeries located 10 to 1,417 km from the markets. The distribution of the nodes' degrees in both January and June indicates a free‐scale network property, and the network in January had a higher clustering coefficient (0.54 vs. 0.39) and a shorter average pathway length (1.91 vs. 2.06) than that in June. The most connected counties of the network were in the central, northern and western regions of Guangdong Province. Compared with randomly removing counties from the network, eliminating counties with higher betweenness, degree or closeness resulted in a greater reduction of the magnitude of a potential epidemic. The findings of this study can be used to inform targeted control interventions for disease spread through this live pig market trade network in south China.
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Affiliation(s)
- Yin Li
- School of Veterinary Medicine, Murdoch University, Perth, WA, Australia.,China Animal Health and Epidemiology Center, Qingdao, China
| | - Baoxu Huang
- School of Veterinary Medicine, Murdoch University, Perth, WA, Australia.,China Animal Health and Epidemiology Center, Qingdao, China
| | - Chaojian Shen
- China Animal Health and Epidemiology Center, Qingdao, China
| | - Chang Cai
- Research and Innovation Office, Murdoch University, Murdoch, WA, Australia.,China Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology, Zhejiang Agricultural and Forestry University, Hangzhou, China
| | - Youming Wang
- China Animal Health and Epidemiology Center, Qingdao, China
| | - John Edwards
- School of Veterinary Medicine, Murdoch University, Perth, WA, Australia.,China Animal Health and Epidemiology Center, Qingdao, China
| | - Guihong Zhang
- South China Agriculture University, Guangzhou, China
| | - Ian D Robertson
- School of Veterinary Medicine, Murdoch University, Perth, WA, Australia.,China-Australia Joint Research and Training Centre for Veterinary Epidemiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
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11
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Alarcón LV, Cipriotti PA, Monterubbianessi M, Perfumo C, Mateu E, Allepuz A. Network analysis of pig movements in Argentina: Identification of key farms in the spread of infectious diseases and their biosecurity levels. Transbound Emerg Dis 2019; 67:1152-1163. [PMID: 31785089 DOI: 10.1111/tbed.13441] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 11/29/2022]
Abstract
This study uses network analysis to evaluate how swine movements in Argentina could contribute to disease spread. Movement data for the 2014-2017 period were obtained from Argentina's online livestock traceability registry and categorized as follows: animals of high genetic value sent to other farms, animals to or from markets, animals sent to finisher operations and slaughterhouse. A network analysis was carried out considering the first three movement types. First, descriptive, centrality and cohesion measures were calculated for each movement type and year. Next, to determine whether networks had a small-world topology, these were compared with the results from random Erdös-Rényi network simulations. Then, the basic reproductive number (R0 ) of the genetic network, the group of farms with higher potential for disease spread standing at the top of the production chain, was calculated to identify farms acting as super-spreaders. Finally, their external biosecurity scores were evaluated. The genetic network in Argentina presented a scale-free and small-world topology. Thus, we estimate that disease spread would be fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super-spreaders in the genetic network for all years, while other 55 were super-spreaders at least once, from an average of 1,613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R0 indicating that few farms have a key role in disease spread. When biosecurity scores of the most relevant super-spreaders were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina's pig production network. These results highlight the usefulness of establishing targeted surveillance and intervention programmes, emphasizing the need for better biosecurity scores in Argentinean swine production units, especially in super-spreader farms.
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Affiliation(s)
- Laura V Alarcón
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Pablo A Cipriotti
- Facultad de Agronomía - IFEVA, Universidad de Buenos Aires/CONICET, Buenos Aires, Argentina
| | - Mariela Monterubbianessi
- National Service for Health and AgriFood Quality (SENASA), Ministerio de Producción y Trabajo, Buenos Aires, Argentina
| | - Carlos Perfumo
- Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Enric Mateu
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alberto Allepuz
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, Barcelona, Spain
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12
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Network analysis of swine movements in a multi-site pig production system in Iowa, USA. Prev Vet Med 2019; 174:104856. [PMID: 31786406 DOI: 10.1016/j.prevetmed.2019.104856] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 10/23/2019] [Accepted: 11/19/2019] [Indexed: 11/21/2022]
Abstract
Pig production in the United States is based on multi-site systems in which pigs are transported between farms after the conclusion of each particular production phase. Although ground transportation is a critical component of the pork supply chain, it might constitute a potential route of infectious disease dissemination. Here, we used a time series network analysis to: (1) describe pig movement flow in a multi-site production system in Iowa, USA, (2) conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and (3) assess the potential impact of each farm type on disease dissemination across the system. Movement reports from 2014-2016 were provided by Iowa Select Farms, Iowa Fall, IA. A total of 76,566 shipments across sites was analyzed, and time series network analyses with temporal resolution of 1, 3, 6, 12, and 36 months were considered. The general topological properties of networks with resolution of 1, 3, 6, and 12 months were compared with the whole period static network (36 months) and included the following features: number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Small-world and scale-free topologies, centrality parameters, and percolation analysis were investigated for the networks with 1-month window. Networks' robustness to interventions was assessed by using the Basic Reproduction Number (R0). Centrality parameters indicate that gilt development units (GDU), nursery, and sow farms have more central role in the pig production hierarchical structure. Therefore, they are potentially major factors of introduction and spread of diseases over the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to be infected. Percolation analysis combined with general properties (i.e. heavy-tailed distributions and degree disassortative) suggested that networks with 1-month time resolution were highly responsive to interventions. Furthermore, the characteristics of a disease should have strong implications in the biosecurity practices across production sites. For instance, biosecurity practices should be focused on sow farms for highly contagious disease (e.g., foot and mouth disease), while it should target nursery sites in the case of a less contagious diseases (i.e. mycobacterial infections). Understanding the patterns of swine movements is crucial for the swine industry decision-making in the case of an epidemic, as well as to design cost-effective approaches to monitor, prevent, control and eradicate infectious diseases in multi-site systems.
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Saleetid N, Green DM. Network structure and risk-based surveillance algorithms for live shrimp movements in Thailand. Transbound Emerg Dis 2019; 66:2450-2461. [PMID: 31389195 DOI: 10.1111/tbed.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/19/2019] [Accepted: 06/27/2019] [Indexed: 11/29/2022]
Abstract
Live shrimp movements pose a potential route for site-to-site transmission of acute hepatopancreatic necrosis disease (AHPND) and other shrimp diseases. We present the first application of network theory to study shrimp epizootiology, providing quantitative information about the live shrimp movement network of Thailand (LSMN), and supporting practical and policy implementations of disease surveillance and control measures. We examined the LSMN over a 13-month period from March 2013 to March 2014, with data obtained from the Thailand Department of Fisheries. The LSMN had a mixture of characteristics both limiting and facilitating disease spread. Importantly, the LSMN exhibited power-law distributions of in and out degrees with exponents of 2.87 and 2.17, respectively. This characteristic indicates that the LSMN behaves like a scale-free network and suggests that an effective strategy to control disease spread in the Thai shrimp farming sector can be achieved by removing a small number of targeted inter-site connections (arcs between nodes). Specifically, a disease-control algorithm based on betweenness centrality (defined as the number of shortest paths between node pairs that traverse a given arc) is proposed here to prioritize targets for disease surveillance and control measures.
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Affiliation(s)
- Nattakan Saleetid
- Department of Fisheries, Kasetsart University Campus, Bangkok, Thailand
| | - Darren Michael Green
- Institute of Aquaculture, Faculty of Natural Sciences, University of Stirling, Stirling, UK
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14
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Ring SC, Purfield DC, Good M, Breslin P, Ryan E, Blom A, Evans RD, Doherty ML, Bradley DG, Berry DP. Variance components for bovine tuberculosis infection and multi-breed genome-wide association analysis using imputed whole genome sequence data. PLoS One 2019; 14:e0212067. [PMID: 30763354 PMCID: PMC6375599 DOI: 10.1371/journal.pone.0212067] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 01/25/2019] [Indexed: 11/18/2022] Open
Abstract
Bovine tuberculosis (bTB) is an infectious disease of cattle generally caused by Mycobacterium bovis, a bacterium that can elicit disease humans. Since the 1950s, the objective of the national bTB eradication program in Republic of Ireland was the biological extinction of bTB; that purpose has yet to be achieved. Objectives of the present study were to develop the statistical methodology and variance components to undertake routine genetic evaluations for resistance to bTB; also of interest was the detection of regions of the bovine genome putatively associated with bTB infection in dairy and beef breeds. The novelty of the present study, in terms of research on bTB infection, was the use of beef breeds in the genome-wide association and the utilization of imputed whole genome sequence data. Phenotypic bTB data on 781,270 animals together with imputed whole genome sequence data on 7,346 of these animals' sires were available. Linear mixed models were used to quantify variance components for bTB and EBVs were validated. Within-breed and multi-breed genome-wide associations were undertaken using a single-SNP regression approach. The estimated genetic standard deviation (0.09), heritability (0.12), and repeatability (0.30) substantiate that genetic selection help to eradicate bTB. The multi-breed genome-wide association analysis identified 38 SNPs and 64 QTL regions associated with bTB infection; two QTL regions (both on BTA23) identified in the multi-breed analysis overlapped with the within-breed analyses of Charolais, Limousin, and Holstein-Friesian. Results from the association analysis, coupled with previous studies, suggest bTB is controlled by an infinitely large number of loci, each having a small effect. The methodology and results from the present study will be used to develop national genetic evaluations for bTB in the Republic of Ireland. In addition, results can also be used to help uncover the biological architecture underlying resistance to bTB infection in cattle.
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Affiliation(s)
- S. C. Ring
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - D. C. Purfield
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
| | - M. Good
- Department of Agriculture, Food and the Marine, Dublin 2, Ireland
| | - P. Breslin
- Department of Agriculture, Food and the Marine, Dublin 2, Ireland
| | - E. Ryan
- Department of Agriculture, Food and the Marine, Dublin 2, Ireland
| | - A. Blom
- Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland
| | - R. D. Evans
- Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland
| | - M. L. Doherty
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - D. G. Bradley
- Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin, Ireland
| | - D. P. Berry
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
- * E-mail:
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15
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Fielding HR, McKinley TJ, Silk MJ, Delahay RJ, McDonald RA. Contact chains of cattle farms in Great Britain. ROYAL SOCIETY OPEN SCIENCE 2019; 6:180719. [PMID: 30891255 PMCID: PMC6408381 DOI: 10.1098/rsos.180719] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 01/23/2019] [Indexed: 05/28/2023]
Abstract
Network analyses can assist in predicting the course of epidemics. Time-directed paths or 'contact chains' provide a measure of host-connectedness across specified timeframes, and so represent potential pathways for spread of infections with different epidemiological characteristics. We analysed networks and contact chains of cattle farms in Great Britain using Cattle Tracing System data from 2001 to 2015. We focused on the potential for between-farm transmission of bovine tuberculosis, a chronic infection with potential for hidden spread through the network. Networks were characterized by scale-free type properties, where individual farms were found to be influential 'hubs' in the network. We found a markedly bimodal distribution of farms with either small or very large ingoing and outgoing contact chains (ICCs and OCCs). As a result of their cattle purchases within 12-month periods, 47% of British farms were connected by ICCs to more than 1000 other farms and 16% were connected to more than 10 000 other farms. As a result of their cattle sales within 12-month periods, 66% of farms had OCCs that reached more than 1000 other farms and 15% reached more than 10 000 other farms. Over 19 000 farms had both ICCs and OCCs reaching more than 10 000 farms for two or more years. While farms with more contacts in their ICCs or OCCs might play an important role in disease spread, farms with extensive ICCs and OCCs might be particularly important by being at higher risk of both acquiring and disseminating infections.
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Affiliation(s)
- Helen R. Fielding
- Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK
| | - Trevelyan J. McKinley
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK
| | - Matthew J. Silk
- Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK
| | - Richard J. Delahay
- Animal and Plant Health Agency, Woodchester Park, Nympsfield, Stonehouse GL10 3UJ, UK
| | - Robbie A. McDonald
- Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK
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Kinsley AC, Perez AM, Craft ME, Vanderwaal KL. Characterization of swine movements in the United States and implications for disease control. Prev Vet Med 2019; 164:1-9. [PMID: 30771888 DOI: 10.1016/j.prevetmed.2019.01.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 01/02/2019] [Accepted: 01/03/2019] [Indexed: 11/18/2022]
Abstract
Understanding between-farm movement patterns is an essential component in developing effective surveillance and control programs in livestock populations. Quantitative knowledge on movement patterns is particularly important for the commercial swine industry, in which large numbers of pigs are frequently moved between farms. Here, we described the annual movement patterns between swine farms in three production systems of the United States and identified farms that may be targeted to increase the efficacy of infectious disease control strategies. Research results revealed a high amount of variability in movement patterns across production systems, indicating that quantities captured from one production system and applied to another may lead to invalid estimations of disease spread. Furthermore, we showed that targeting farms based on their mean infection potential, a metric that captured the temporal sequence of movements, substantially reduced the potential for transmission of an infectious pathogen in the contact network and performed consistently well across production systems. Specifically, we found that by targeting farms based on their mean infection potential, we could reduce the potential spread of an infectious pathogen by 80% when removing approximately 25% of farms in each of the production systems. Whereas other metrics, such as degree, required 26-35% of farms to be removed in two of the production systems to reach the same outcome; this outcome was not achievable in one of the production systems. Our results demonstrate the importance of fine-scale temporal movement data and the need for in-depth understanding of the contact structure in developing more efficient disease surveillance and response strategies in swine production systems.
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Affiliation(s)
- A C Kinsley
- University of Minnesota, Department of Veterinary Population Medicine, 1988 Fitch Ave., St. Paul, MN, 55108, USA.
| | - A M Perez
- University of Minnesota, Department of Veterinary Population Medicine, 1988 Fitch Ave., St. Paul, MN, 55108, USA.
| | - M E Craft
- University of Minnesota, Department of Veterinary Population Medicine, 1988 Fitch Ave., St. Paul, MN, 55108, USA.
| | - K L Vanderwaal
- University of Minnesota, Department of Veterinary Population Medicine, 1988 Fitch Ave., St. Paul, MN, 55108, USA.
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17
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Ring SC, Graham DA, Sayers RG, Byrne N, Kelleher MM, Doherty ML, Berry DP. Genetic variability in the humoral immune response to bovine herpesvirus-1 infection in dairy cattle and genetic correlations with performance traits. J Dairy Sci 2018; 101:6190-6204. [PMID: 29705421 DOI: 10.3168/jds.2018-14481] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 03/13/2018] [Indexed: 01/24/2023]
Abstract
Bovine herpesvirus-1 (BoHV-1) is a viral pathogen of global significance that is known to instigate several diseases in cattle, the most notable of which include infectious bovine rhinotracheitis and bovine respiratory disease. The genetic variability in the humoral immune response to BoHV-1 has, to our knowledge, not ever been quantified. Therefore, the objectives of the present study were to estimate the genetic parameters for the humoral immune response to BoHV-1 in Irish female dairy cattle, as well as to investigate the genetic relationship between the humoral immune response to BoHV-1 with milk production performance, fertility performance, and animal mortality. Information on antibody response to BoHV-1 was available to the present study from 2 BoHV-1 sero-prevalence research studies conducted between the years 2010 to 2015, inclusive; after edits, BoHV-1 antibody test results were available on a total of 7,501 female cattle from 58 dairy herds. National records of milk production (i.e., 305-d milk yield, fat yield, protein yield, and somatic cell score; n = 1,211,905 milk-recorded cows), fertility performance (i.e., calving performance, pregnancy diagnosis, and insemination data; n = 2,365,657 cows) together with animal mortality data (i.e., birth, farm movement, death, slaughter, and export events; n = 12,853,257 animals) were also available. Animal linear mixed models were used to quantify variance components for BoHV-1 as well as to estimate genetic correlations among traits. The estimated genetic parameters for the humoral immune response to BoHV-1 in the present study (i.e., heritability range: 0.09 to 0.16) were similar to estimates previously reported for clinical signs of bovine respiratory disease in dairy and beef cattle (i.e., heritability range: 0.05 to 0.11). Results from the present study suggest that breeding for resistance to BoHV-1 infection could reduce the incidence of respiratory disease in cattle while having little or no effect on genetic selection for milk yield or milk constituents (i.e., genetic correlations ranged from -0.13 to 0.17). Moreover, even though standard errors were large, results also suggest that breeding for resistance to BoHV-1 infection may indirectly improve fertility performance while also reducing the incidence of mortality in older animals (i.e., animals >182 d of age). Results can be used to inform breeding programs of potential genetic gains achievable for resistance to BoHV-1 infection in cattle.
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Affiliation(s)
- S C Ring
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61C996; School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland D04W6F6
| | - D A Graham
- Animal Health Ireland, 4-5 The Archways, Carrick on Shannon, Co. Leitrim, Ireland N41WN27
| | - R G Sayers
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61C996
| | - N Byrne
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61C996
| | - M M Kelleher
- Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland P72X050
| | - M L Doherty
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland D04W6F6
| | - D P Berry
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61C996.
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18
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Vidondo B. Amplification of the basic reproduction number in cattle farm networks. PLoS One 2018; 13:e0191257. [PMID: 29672512 PMCID: PMC5909513 DOI: 10.1371/journal.pone.0191257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 12/29/2017] [Indexed: 11/19/2022] Open
Abstract
The popularly known 20-80 rule or Pareto rule states that 20% of efforts leads to 80% of results. This rule has been applied to the study of infection transmission in contact networks, and specifically, contact networks between cattle farms. Woolhouse and collaborators showed that targeting interventions for disease control and prevention to the 20% of the farms that contribute the most to the basic reproduction number (Ro), could reduce it by 80%. The rule results from the number of incoming and outgoing contacts per farm being highly heterogeneous. Besides, Woolhouse and collaborators showed that this high contact heterogeneity, together with a high positive correlation between the number of incoming and outgoing animal movements per farm leads to an amplification in the Ro. Two previous studies carried out with Scottish cattle transport data found either no correlation or only a weak correlation (rho up to 0.33) when using weighted data. Using data from the contacts between Swiss cattle farms in 2015, we found that the 20-80 rule applies with respect to Ro, although the proportion of highly active farms is smaller (11%). Besides, a positive strong correlation (rho = 0.64, weighted data) between the incoming and outgoing contacts of farms exists. This means that the amplification of Ro (due to the contact heterogeneities and the existing correlation) in cattle contact networks can be much higher than known until now. Our results highlight the importance of an effective active surveillance, more so than in other countries were these amplification mechanisms are absent.
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Affiliation(s)
- Beatriz Vidondo
- Veterinary Public Health Institute, University of Bern, Bern, Switzerland
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Abstract
Transmissibility is the defining characteristic of infectious diseases. Quantifying transmission matters for understanding infectious disease epidemiology and designing evidence-based disease control programs. Tracing individual transmission events can be achieved by epidemiological investigation coupled with pathogen typing or genome sequencing. Individual infectiousness can be estimated by measuring pathogen loads, but few studies have directly estimated the ability of infected hosts to transmit to uninfected hosts. Individuals' opportunities to transmit infection are dependent on behavioral and other risk factors relevant given the transmission route of the pathogen concerned. Transmission at the population level can be quantified through knowledge of risk factors in the population or phylogeographic analysis of pathogen sequence data. Mathematical model-based approaches require estimation of the per capita transmission rate and basic reproduction number, obtained by fitting models to case data and/or analysis of pathogen sequence data. Heterogeneities in infectiousness, contact behavior, and susceptibility can have substantial effects on the epidemiology of an infectious disease, so estimates of only mean values may be insufficient. For some pathogens, super-shedders (infected individuals who are highly infectious) and super-spreaders (individuals with more opportunities to transmit infection) may be important. Future work on quantifying transmission should involve integrated analyses of multiple data sources.
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20
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Rossi G, Smith RL, Pongolini S, Bolzoni L. Modelling farm-to-farm disease transmission through personnel movements: from visits to contacts, and back. Sci Rep 2017; 7:2375. [PMID: 28539663 PMCID: PMC5443770 DOI: 10.1038/s41598-017-02567-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 04/12/2017] [Indexed: 11/09/2022] Open
Abstract
Infectious diseases in livestock can be transmitted through fomites: objects able to convey infectious agents. Between-farm spread of infections through fomites is mostly due to indirect contacts generated by on-farm visits of personnel that can carry pathogens on their clothes, equipment, or vehicles. However, data on farm visitors are often difficult to obtain because of the heterogeneity of their nature and privacy issues. Thus, models simulating disease spread between farms usually rely on strong assumptions about the contribution of indirect contacts on infection spread. By using data on veterinarian on-farm visits in a dairy farm system, we built a simple simulation model to assess the role of indirect contacts on epidemic dynamics compared to cattle movements (i.e. direct contacts). We showed that including in the simulation model only specific subsets of the information available on indirect contacts could lead to outputs widely different from those obtained with the full-information model. Then, we provided a simple preferential attachment algorithm based on the probability to observe consecutive on-farm visits from the same operator that allows overcoming the information gaps. Our results suggest the importance of detailed data and a deeper understanding of visit dynamics for the prevention and control of livestock diseases.
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Affiliation(s)
- Gianluigi Rossi
- Department of Pathobiology, College of Veterinary Medicine, University of Illinois, 2001 S. Lincoln Avenue, 61802, Urbana, IL, USA.
| | - Rebecca L Smith
- Department of Pathobiology, College of Veterinary Medicine, University of Illinois, 2001 S. Lincoln Avenue, 61802, Urbana, IL, USA
| | - Stefano Pongolini
- Risk Analysis Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Via dei Mercati, 13/A, I-43126, Parma, Italy
| | - Luca Bolzoni
- Risk Analysis Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Via dei Mercati, 13/A, I-43126, Parma, Italy
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21
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Rossi G, De Leo GA, Pongolini S, Natalini S, Zarenghi L, Ricchi M, Bolzoni L. The Potential Role of Direct and Indirect Contacts on Infection Spread in Dairy Farm Networks. PLoS Comput Biol 2017; 13:e1005301. [PMID: 28125610 PMCID: PMC5268397 DOI: 10.1371/journal.pcbi.1005301] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 12/12/2016] [Indexed: 11/19/2022] Open
Abstract
Animals' exchanges are considered the most effective route of between-farm infectious disease transmission. However, despite being often overlooked, the infection spread due to contaminated equipment, vehicles, or personnel proved to be important for several livestock epidemics. This study investigated the role of indirect contacts in a potential infection spread in the dairy farm network of the Province of Parma (Northern Italy). We built between-farm contact networks using data on cattle exchange (direct contacts), and on-farm visits by veterinarians (indirect contacts). We compared the features of the contact structures by using measures on static and temporal networks. We assessed the disease spreading potential of the direct and indirect network structures in the farm system by using data on the infection state of farms by paratuberculosis. Direct and indirect networks showed non-trivial differences with respect to connectivity, contact distribution, and super-spreaders identification. Furthermore, our analyses on paratuberculosis data suggested that the contributions of direct and indirect contacts on diseases spread are apparent at different spatial scales. Our results highlighted the potential role of indirect contacts in between-farm disease spread and underlined the need for a deeper understanding of these contacts to develop better strategies for prevention of livestock epidemics.
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Affiliation(s)
- Gianluigi Rossi
- Dipartimento di Bioscienze, Università degli studi di Parma, Parco Area delle Scienze, Parma, Italy
- Risk Analysis Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Via dei Mercati, Parma, Italy
| | - Giulio A. De Leo
- Dipartimento di Bioscienze, Università degli studi di Parma, Parco Area delle Scienze, Parma, Italy
- Stanford University, Hopkins Marine Station, Pacific Grove, CA, United States of America
| | - Stefano Pongolini
- Risk Analysis Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Via dei Mercati, Parma, Italy
| | - Silvano Natalini
- Servizio Veterinario e Igiene Alimenti, Assessorato Politiche per la Salute Regione Emilia-Romagna, Viale Aldo Moro, Bologna, Italy
| | - Luca Zarenghi
- Servizio Igiene degli Allevamenti e Produzioni Zootecniche, AUSL di Parma, Via Vasari, Parma, Italy
| | - Matteo Ricchi
- National Reference Centre for Paratuberculosis, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Strada Faggiola 1, loc. Gariga—Podenzano (PC), Italy
| | - Luca Bolzoni
- Risk Analysis Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Via dei Mercati, Parma, Italy
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Social network analysis of cattle movement in Kampong Cham, Kampong Speu and Takeo, Cambodia. Acta Trop 2016; 159:44-9. [PMID: 27012718 DOI: 10.1016/j.actatropica.2016.03.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Revised: 03/13/2016] [Accepted: 03/20/2016] [Indexed: 11/21/2022]
Abstract
The aim of this study is to provide insight the network of cattle movement in Kampong Cham, Kampong Speu and Takeo, Cambodia. A cross-sectional study was carried out from July 2014 to August 2014, using questionnaires. It was implemented with 435 interviewees (24.4%, 24.6% and 51.0% from Kampong Cham, Kampong Speu and Takeo, respectively) using one-step snowball sampling. The findings suggest that the key players in all three provinces are producers who raise their cattle as backyard animals. In all three provinces the key players in spreading disease are probably the middlemen, collectors, brokers or traders. The network of cattle movement is presented as a strong component of varying size in each location. In this network we found three cut-points in both Kampong Cham and Kampong Speu. The network in each province indicates a random pattern of node distribution. The results of our study are useful to relevant authorities and researchers to understand the spread of infectious diseases into different areas. The middlemen, collectors, brokers and traders need to be controlled as first priority in order to reduce the magnitude of the spread of disease.
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Ribeiro-Lima J, Schwabenlander S, Oakes M, Thompson B, Wells SJ. Risk profiling of cattle farms as a potential tool in risk-based surveillance for Mycobacterium bovis infection among cattle in tuberculosis-free areas. J Am Vet Med Assoc 2016; 248:1404-13. [PMID: 27270064 DOI: 10.2460/javma.248.12.1404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To develop a cattle herd risk-profiling system that could potentially inform risk-based surveillance strategies for Mycobacterium bovis infection in cattle and provide information that could be used to help direct resource allocation by a state agency for this purpose. DESIGN Cross-sectional study. SAMPLE Records for any size movement (importation) of cattle into Minnesota from other US states during 2009 (n = 7,185) and 2011 (8,107). PROCEDURES Data from certificates of veterinary inspection were entered into a spreadsheet. Movement data were summarized at premises and county levels, and for each level, the distribution of cattle moved and number of movements were evaluated. Risk profiling (assessment and categorization of risk for disease introduction) for each import movement was performed on the basis of known risk factors. Latent class analysis was used to assign movements to risk classifications with adjustment on the basis of expert opinions from personnel knowledgeable about bovine tuberculosis; these data were used to classify premises as very high, high, medium, or low risk for disease introduction. RESULTS In each year, approximately 1,500 premises imported cattle, typically beef and feeder types, with the peak of import movements during the fall season. The risk model identified 4 risk classes for cattle movements. Approximately 500 of the estimated 27,406 (2%) cattle premises in Minnesota were in the very high or high risk groups for either year; greatest density of these premises was in the southeast and southwest regions of the state. CONCLUSIONS AND CLINICAL RELEVANCE A risk-profiling approach was developed that can be applied in targeted surveillance efforts for bovine tuberculosis, particularly in disease-free areas.
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Marquetoux N, Stevenson MA, Wilson P, Ridler A, Heuer C. Using social network analysis to inform disease control interventions. Prev Vet Med 2016; 126:94-104. [PMID: 26883965 DOI: 10.1016/j.prevetmed.2016.01.022] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 01/21/2016] [Accepted: 01/25/2016] [Indexed: 11/28/2022]
Abstract
Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes. This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by "removing" a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics. This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations.
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Affiliation(s)
- Nelly Marquetoux
- EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, New Zealand.
| | - Mark A Stevenson
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Werribee, Victoria, Australia
| | - Peter Wilson
- Institute of Veterinary, Animal and Biomedical Sciences, Massey University, New Zealand
| | - Anne Ridler
- Institute of Veterinary, Animal and Biomedical Sciences, Massey University, New Zealand
| | - Cord Heuer
- EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, New Zealand
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VanderWaal KL, Picasso C, Enns EA, Craft ME, Alvarez J, Fernandez F, Gil A, Perez A, Wells S. Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control. Prev Vet Med 2015; 123:12-22. [PMID: 26708252 DOI: 10.1016/j.prevetmed.2015.12.003] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 11/16/2015] [Accepted: 12/08/2015] [Indexed: 11/30/2022]
Abstract
Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.
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Affiliation(s)
- Kimberly L VanderWaal
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States.
| | - Catalina Picasso
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States; Animal Health Bureau, Ministry of Livestock, Agriculture, and Fisheries, 1476 Constituyente, Montevideo 11200, Uruguay.
| | - Eva A Enns
- Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN 55455, United States.
| | - Meggan E Craft
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States.
| | - Julio Alvarez
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States.
| | - Federico Fernandez
- Animal Health Bureau, Ministry of Livestock, Agriculture, and Fisheries, 1476 Constituyente, Montevideo 11200, Uruguay.
| | - Andres Gil
- Facultad de Veterinaria, Universidad de la Republica, 1550 Alberto Lasplaces, Montevideo 11100, Uruguay.
| | - Andres Perez
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States.
| | - Scott Wells
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN 55108, United States.
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26
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Ma ZS. Power law analysis of the human microbiome. Mol Ecol 2015; 24:5428-45. [DOI: 10.1111/mec.13394] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 08/10/2015] [Accepted: 09/21/2015] [Indexed: 01/14/2023]
Affiliation(s)
- Zhanshan Sam Ma
- Computational Biology and Medical Ecology Lab; State Key Laboratory of Genetic Resources and Evolution; Kunming Institute of Zoology; The Chinese Academy of Sciences; Kunming 650223 China
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27
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Sample size considerations for livestock movement network data. Prev Vet Med 2015; 122:399-405. [PMID: 26276397 DOI: 10.1016/j.prevetmed.2015.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2015] [Revised: 06/29/2015] [Accepted: 07/21/2015] [Indexed: 11/24/2022]
Abstract
The movement of animals between farms contributes to infectious disease spread in production animal populations, and is increasingly investigated with social network analysis methods. Tangible outcomes of this work include the identification of high-risk premises for targeting surveillance or control programs. However, knowledge of the effect of sampling or incomplete network enumeration on these studies is limited. In this study, a simulation algorithm is presented that provides an estimate of required sampling proportions based on predicted network size, density and degree value distribution. The algorithm may be applied a priori to ensure network analyses based on sampled or incomplete data provide population estimates of known precision. Results demonstrate that, for network degree measures, sample size requirements vary with sampling method. The repeatability of the algorithm output under constant network and sampling criteria was found to be consistent for networks with at least 1000 nodes (in this case, farms). Where simulated networks can be constructed to closely mimic the true network in a target population, this algorithm provides a straightforward approach to determining sample size under a given sampling procedure for a network measure of interest. It can be used to tailor study designs of known precision, for investigating specific livestock movement networks and their impact on disease dissemination within populations.
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Controlling infectious disease through the targeted manipulation of contact network structure. Epidemics 2015; 12:11-9. [PMID: 26342238 PMCID: PMC4728197 DOI: 10.1016/j.epidem.2015.02.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Revised: 02/24/2015] [Accepted: 02/25/2015] [Indexed: 11/21/2022] Open
Abstract
Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation.
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Ribeiro-Lima J, Enns EA, Thompson B, Craft ME, Wells SJ. From network analysis to risk analysis--An approach to risk-based surveillance for bovine tuberculosis in Minnesota, US. Prev Vet Med 2014; 118:328-40. [PMID: 25577678 DOI: 10.1016/j.prevetmed.2014.12.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 11/14/2014] [Accepted: 12/04/2014] [Indexed: 10/24/2022]
Abstract
Bovine tuberculosis (bTB) was first detected in 2005 in cattle in northwestern Minnesota (MN) through slaughter surveillance. By the end of 2008, 12 cattle herds were infected with bTB, and the main cause for infection was determined to be the movement of infected animals between herds. Bovine tuberculosis was contained in a smaller area in northwestern Minnesota classified as modified accredited (MA), corresponding to a prevalence inferior to 0.1% in cattle. From January 2008 to 2011, all cattle movements within the bTB MA were recorded electronically. The primary objectives of this study were to characterize cattle movements within this region and identify cattle herds with higher risk of bTB introduction based on network parameters and known risk factors from the published literature. During the period that data was collected, 57,460 cattle were moved in 3762 movements corresponding to permits issued to 682 premises, mostly representing private farms, sale yards, slaughter facilities and county or state fairs. Although sale yards represented less than 2% of the premises (nodes), 60% of the movements were to or from a sale yard. The network showed an overall density of 0.4%, a clustering coefficient of 14.6% and a betweenness centralization index of 12.7%, reflecting the low connectivity of this cattle network. The degree distribution showed that 20% of nodes performed 90% of the movements. Farms were ranked based on the total risk score and divided into high, medium, and low risk groups based on the score and its variability. The higher risk group included 14% (n=50) of the farms, corresponding to 80% of the cumulative risk for the farms in the bTB area. This analysis provides a baseline description about the contact structure of cattle movements in an area previously infected with bTB and develops a framework for a targeted surveillance approach for bTB to support future surveillance decisions.
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Affiliation(s)
- J Ribeiro-Lima
- Department of Veterinary Population Medicine, University of Minnesota College of Veterinary Medicine, St. Paul, MN, United States.
| | - E A Enns
- Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, United States
| | - B Thompson
- Minnesota Board of Animal Health, St. Paul, MN, United States
| | - M E Craft
- Department of Veterinary Population Medicine, University of Minnesota College of Veterinary Medicine, St. Paul, MN, United States
| | - S J Wells
- Department of Veterinary Population Medicine, University of Minnesota College of Veterinary Medicine, St. Paul, MN, United States
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30
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Gates MC, Humphry RW, Gunn GJ, Woolhouse MEJ. Not all cows are epidemiologically equal: quantifying the risks of bovine viral diarrhoea virus (BVDV) transmission through cattle movements. Vet Res 2014; 45:110. [PMID: 25323831 PMCID: PMC4206702 DOI: 10.1186/s13567-014-0110-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Accepted: 10/08/2014] [Indexed: 11/10/2022] Open
Abstract
Many economically important cattle diseases spread between herds through livestock movements. Traditionally, most transmission models have assumed that all purchased cattle carry the same risk of generating outbreaks in the destination herd. Using data on bovine viral diarrhoea virus (BVDV) in Scotland as a case example, this study provides empirical and theoretical evidence that the risk of disease transmission varies substantially based on the animal and herd demographic characteristics at the time of purchase. Multivariable logistic regression analysis revealed that purchasing pregnant heifers and open cows sold with a calf at foot were associated with an increased risk of beef herds being seropositive for BVDV. Based on the results from a dynamic within-herd simulation model, these findings may be partly explained by the age-related probability of animals being persistently infected with BVDV as well as the herd demographic structure at the time of animal introductions. There was also evidence that an epidemiologically important network statistic, "betweenness centrality" (a measure frequently associated with the potential for herds to acquire and transmit disease), was significantly higher for herds that supplied these particular types of replacement beef cattle. The trends for dairy herds were not as clear, although there was some evidence that open heifers and open lactating cows were associated with an increased risk of BVDV. Overall, these findings have important implications for developing simulation models that more accurately reflect the industry-level transmission dynamics of infectious cattle diseases.
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Affiliation(s)
- M Carolyn Gates
- Epidemiology Group, Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Ashworth Laboratories, Kings Buildings, West Mains Road, Edinburgh, EH9 3JT, UK.
| | - Roger W Humphry
- Epidemiology Research Unit, SRUC, Drummondhill, Stratherrick Road, Inverness, IV2 4JZ, UK.
| | - George J Gunn
- Epidemiology Research Unit, SRUC, Drummondhill, Stratherrick Road, Inverness, IV2 4JZ, UK.
| | - Mark E J Woolhouse
- Epidemiology Group, Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Ashworth Laboratories, Kings Buildings, West Mains Road, Edinburgh, EH9 3JT, UK.
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31
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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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32
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Eight challenges in modelling infectious livestock diseases. Epidemics 2014; 10:1-5. [PMID: 25843373 DOI: 10.1016/j.epidem.2014.08.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 08/14/2014] [Accepted: 08/18/2014] [Indexed: 02/02/2023] Open
Abstract
The transmission of infectious diseases of livestock does not differ in principle from disease transmission in any other animals, apart from that the aim of control is ultimately economic, with the influence of social, political and welfare constraints often poorly defined. Modelling of livestock diseases suffers simultaneously from a wealth and a lack of data. On the one hand, the ability to conduct transmission experiments, detailed within-host studies and track individual animals between geocoded locations make livestock diseases a particularly rich potential source of realistic data for illuminating biological mechanisms of transmission and conducting explicit analyses of contact networks. On the other hand, scarcity of funding, as compared to human diseases, often results in incomplete and partial data for many livestock diseases and regions of the world. In this overview of challenges in livestock disease modelling, we highlight eight areas unique to livestock that, if addressed, would mark major progress in the area.
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A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature 2014; 511:228-31. [DOI: 10.1038/nature13529] [Citation(s) in RCA: 157] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2012] [Accepted: 05/27/2014] [Indexed: 11/08/2022]
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Pautasso M, Jeger MJ. Network epidemiology and plant trade networks. AOB PLANTS 2014; 6:plu007. [PMID: 24790128 PMCID: PMC4038442 DOI: 10.1093/aobpla/plu007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 02/11/2014] [Indexed: 05/29/2023]
Abstract
Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.
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Affiliation(s)
- Marco Pautasso
- Forest Pathology and Dendrology, Institute of Integrative Biology, ETHZ, Zurich, Switzerland
| | - Mike J. Jeger
- Division of Ecology and Evolution & Centre for Environmental Policy, Imperial College London, London, UK
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35
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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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Smieszek T, Barclay VC, Seeni I, Rainey JJ, Gao H, Uzicanin A, Salathé M. How should social mixing be measured: comparing web-based survey and sensor-based methods. BMC Infect Dis 2014; 14:136. [PMID: 24612900 PMCID: PMC3984737 DOI: 10.1186/1471-2334-14-136] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 02/19/2014] [Indexed: 11/25/2022] Open
Abstract
Background Contact surveys and diaries have conventionally been used to measure contact networks in different settings for elucidating infectious disease transmission dynamics of respiratory infections. More recently, technological advances have permitted the use of wireless sensor devices, which can be worn by individuals interacting in a particular social context to record high resolution mixing patterns. To date, a direct comparison of these two different methods for collecting contact data has not been performed. Methods We studied the contact network at a United States high school in the spring of 2012. All school members (i.e., students, teachers, and other staff) were invited to wear wireless sensor devices for a single school day, and asked to remember and report the name and duration of all of their close proximity conversational contacts for that day in an online contact survey. We compared the two methods in terms of the resulting network densities, nodal degrees, and degree distributions. We also assessed the correspondence between the methods at the dyadic and individual levels. Results We found limited congruence in recorded contact data between the online contact survey and wireless sensors. In particular, there was only negligible correlation between the two methods for nodal degree, and the degree distribution differed substantially between both methods. We found that survey underreporting was a significant source of the difference between the two methods, and that this difference could be improved by excluding individuals who reported only a few contact partners. Additionally, survey reporting was more accurate for contacts of longer duration, and very inaccurate for contacts of shorter duration. Finally, female participants tended to report more accurately than male participants. Conclusions Online contact surveys and wireless sensor devices collected incongruent network data from an identical setting. This finding suggests that these two methods cannot be used interchangeably for informing models of infectious disease dynamics.
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Affiliation(s)
- Timo Smieszek
- Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.
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McDaniel CJ, Cardwell DM, Moeller RB, Gray GC. Humans and cattle: a review of bovine zoonoses. Vector Borne Zoonotic Dis 2014; 14:1-19. [PMID: 24341911 PMCID: PMC3880910 DOI: 10.1089/vbz.2012.1164] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Infectious disease prevention and control has been among the top public health objectives during the last century. However, controlling disease due to pathogens that move between animals and humans has been challenging. Such zoonotic pathogens have been responsible for the majority of new human disease threats and a number of recent international epidemics. Currently, our surveillance systems often lack the ability to monitor the human-animal interface for emergent pathogens. Identifying and ultimately addressing emergent cross-species infections will require a "One Health" approach in which resources from public veterinary, environmental, and human health function as part of an integrative system. Here we review the epidemiology of bovine zoonoses from a public health perspective.
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Affiliation(s)
- Clinton J. McDaniel
- College of Public Health and Health Professions and Emerging Pathogens Institute, University of Florida, Gainesville, Florida
| | - Diana M. Cardwell
- College of Public Health and Health Professions and Emerging Pathogens Institute, University of Florida, Gainesville, Florida
| | - Robert B. Moeller
- California Animal Health and Food Safety Laboratory System, Tulare Laboratory, University of California, Tulare, California
| | - Gregory C. Gray
- College of Public Health and Health Professions and Emerging Pathogens Institute, University of Florida, Gainesville, Florida
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38
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Miller E, Huppert A. The effects of host diversity on vector-borne disease: the conditions under which diversity will amplify or dilute the disease risk. PLoS One 2013; 8:e80279. [PMID: 24303003 PMCID: PMC3841118 DOI: 10.1371/journal.pone.0080279] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2013] [Accepted: 10/11/2013] [Indexed: 11/29/2022] Open
Abstract
Multihost vector-borne infectious diseases form a significant fraction of the global infectious disease burden. In this study we explore the relationship between host diversity, vector behavior, and disease risk. To this end, we have developed a new dynamic model which includes two distinct host species and one vector species with variable preferences. With the aid of the model we were able to compute the basic reproductive rate, R 0, a well-established measure of disease risk that serves as a threshold parameter for disease outbreak. The model analysis reveals that the system has two different qualitative behaviors: (i) the well-known dilution effect, where the maximal R0 is obtained in a community which consists a single host (ii) a new amplification effect, denoted by us as diversity amplification, where the maximal R0 is attained in a community which consists both hosts. The model analysis extends on previous results by underlining the mechanism of both, diversity amplification and the dilution, and specifies the exact conditions for their occurrence. We have found that diversity amplification occurs where the vector prefers the host with the highest transmission ability, and dilution is obtained when the vector does not show any preference, or it prefers to bite the host with the lower transmission ability. The mechanisms of dilution and diversity amplification are able to account for the different and contradictory patterns often observed in nature (i.e., in some cases disease risk is increased while in other is decreased when the diversity is increased). Implication of the diversity amplification mechanism also challenges current premises about the interaction between biodiversity, climate change, and disease risk and calls for retrospective thinking in planning intervention policies aimed at protecting the preferred host species.
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Affiliation(s)
- Ezer Miller
- The Hebrew University of Jerusalem, The Faculty of Medicine, Microbiology and Molecular Genetics Department, Jerusalem, Israel
- Gertner Institute for Epidemiology and Health Policy Research, Biostatistics Unit, Ramat Gan, Israel
| | - Amit Huppert
- Gertner Institute for Epidemiology and Health Policy Research, Biostatistics Unit, Ramat Gan, Israel
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39
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Raizman EA, Rasmussen HB, King LE, Ihwagi FW, Douglas-Hamilton I. Feasibility study on the spatial and temporal movement of Samburu's cattle and wildlife in Kenya using GPS radio-tracking, remote sensing and GIS. Prev Vet Med 2013; 111:76-80. [PMID: 23711505 DOI: 10.1016/j.prevetmed.2013.04.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Revised: 04/20/2013] [Accepted: 04/26/2013] [Indexed: 10/26/2022]
Abstract
The study was conducted to assess the technical feasibility of studying the spatial and temporal interaction of traditionally herded livestock and wildlife using global positioning system (GPS) tracking technology in Northern Kenya. Two types of collars were used on nine cows: radio frequency and global system for mobile communications (GSM) collars and GPS-satellite (SAT) collars. Full results of cattle tracking were available for eight cows (3 GSM and 5 SAT) tracked between July 2008 and September 2010. A cumulative total of 1556 tracking days was recorded over the 17 month period. On average cows walked 10,203 m/day (average total monthly distance walked was 234 km). Significant seasonal differences were found; on average cows walked 9.607 m and 10,392 m per day in the rainy and the dry seasons, respectively. This difference was also significant for total monthly and daily distance walked between the dry and the rainy season. On average cows walked daily 9607 m and 10,392 m on the rainy and the dry season respectively. During the dry months a 48 h cycle was observed with cows walking 15-25 km to water every 2nd day but only 5-8 km/day between watering days. There was a 24% overlap of cattle range with both elephants and zebras. This study demonstrated the feasibility of tracking cattle using radio collars. It shows the complexity of spatial use by cattle and wildlife. Such information can be used to understand the dynamics of disease transmission between livestock and wildlife.
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Affiliation(s)
- E A Raizman
- Department of Comparative Pathobiology, School of Veterinary Medicine, Purdue University, W. Lafayette, IN 47907, USA.
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40
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Interventions for avian influenza A (H5N1) risk management in live bird market networks. Proc Natl Acad Sci U S A 2013; 110:9177-82. [PMID: 23650388 DOI: 10.1073/pnas.1220815110] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Highly pathogenic avian influenza virus subtype H5N1 is endemic in Asia, with live bird trade as a major disease transmission pathway. A cross-sectional survey was undertaken in northern Vietnam to investigate the structure of the live bird market (LBM) contact network and the implications for virus spread. Based on the movements of traders between LBMs, weighted and directed networks were constructed and used for social network analysis and individual-based modeling. Most LBMs were connected to one another, suggesting that the LBM network may support large-scale disease spread. Because of cross-border trade, it also may promote transboundary virus circulation. However, opportunities for disease control do exist. The implementation of thorough, daily disinfection of the market environment as well as of traders' vehicles and equipment in only a small number of hubs can disconnect the network dramatically, preventing disease spread. These targeted interventions would be an effective alternative to the current policy of a complete ban of LBMs in some areas. Some LBMs that have been banned still are very active, and they likely have a substantial impact on disease dynamics, exhibiting the highest levels of susceptibility and infectiousness. The number of trader visits to markets, information that can be collected quickly and easily, may be used to identify LBMs suitable for implementing interventions. This would not require prior knowledge of the force of infection, for which laboratory-confirmed surveillance would be necessary. These findings are of particular relevance for policy development in resource-scarce settings.
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41
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Caillaud D, Craft ME, Meyers LA. Epidemiological effects of group size variation in social species. J R Soc Interface 2013; 10:20130206. [PMID: 23576784 DOI: 10.1098/rsif.2013.0206] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Contact patterns in group-structured populations determine the course of infectious disease outbreaks. Network-based models have revealed important connections between group-level contact patterns and the dynamics of epidemics, but these models typically ignore heterogeneities in within-group composition. Here, we analyse a flexible mathematical model of disease transmission in a hierarchically structured wildlife population, and find that increased variation in group size reduces the epidemic threshold, making social animal populations susceptible to a broader range of pathogens. Variation in group size also increases the likelihood of an epidemic for mildly transmissible diseases, but can reduce the likelihood and expected size of an epidemic for highly transmissible diseases. Further, we introduce the concept of epidemiological effective group size, which we define to be the group size of a hypothetical population containing groups of identical size that has the same epidemic threshold as an observed population. Using data from the Serengeti Lion Project, we find that pride-living Serengeti lions are epidemiologically comparable to a homogeneous population with up to 20 per cent larger prides.
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Affiliation(s)
- Damien Caillaud
- Section of Integrative Biology, The University of Texas at Austin, 1 University Station, Austin, TX 78712, USA.
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42
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Smieszek T, Salathé M. A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks. BMC Med 2013; 11:35. [PMID: 23402633 PMCID: PMC3616813 DOI: 10.1186/1741-7015-11-35] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Accepted: 02/12/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Infectious disease outbreaks in communities can be controlled by early detection and effective prevention measures. Assessing the relative importance of each individual community member with respect to these two processes requires detailed knowledge about the underlying social contact network on which the disease can spread. However, mapping social contact networks is typically too resource-intensive to be a practical possibility for most communities and institutions. METHODS Here, we describe a simple, low-cost method - called collocation ranking - to assess individual importance for early detection and targeted intervention strategies that are easily implementable in practice. The method is based on knowledge about individual collocation which is readily available in many community settings such as schools, offices, hospitals, and so on. We computationally validate our method in a school setting by comparing the outcome of the method against computational predictions based on outbreak simulations on an empirical high-resolution contact network. We compare collocation ranking to other methods for assessing the epidemiological importance of the members of a population. To this end, we select subpopulations of the school population by applying these assessment methods to the population and adding individuals to the subpopulation according to their individual rank. Then, we assess how suited these subpopulations are for early detection and targeted intervention strategies. RESULTS Likelihood and timing of infection during an outbreak are important features for early detection and targeted intervention strategies. Subpopulations selected by the collocation ranking method show a substantially higher average infection probability and an earlier onset of symptoms than randomly selected subpopulations. Furthermore, these subpopulations selected by the collocation ranking method were close to the optimum. CONCLUSIONS Our results indicate that collocation ranking is a highly effective method to assess individual importance, providing critical low-cost information for the development of sentinel surveillance systems and prevention strategies.
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Affiliation(s)
- Timo Smieszek
- Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.
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43
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Vallée E, Waret-Szkuta A, Chaka H, Duboz R, Balcha M, Goutard F. Analysis of traditional poultry trader networks to improve risk-based surveillance. Vet J 2013; 195:59-65. [DOI: 10.1016/j.tvjl.2012.05.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2010] [Revised: 05/16/2012] [Accepted: 05/20/2012] [Indexed: 11/16/2022]
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44
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Snow L, Warner R, Cheney T, Wearing H, Stokes M, Harris K, Teale C, Coldham N. Risk factors associated with extended spectrum beta-lactamase Escherichia coli (CTX-M) on dairy farms in North West England and North Wales. Prev Vet Med 2012; 106:225-34. [DOI: 10.1016/j.prevetmed.2012.03.009] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2011] [Revised: 03/08/2012] [Accepted: 03/17/2012] [Indexed: 11/16/2022]
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45
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Bajardi P, Barrat A, Savini L, Colizza V. Optimizing surveillance for livestock disease spreading through animal movements. J R Soc Interface 2012; 9:2814-25. [PMID: 22728387 DOI: 10.1098/rsif.2012.0289] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems.
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Affiliation(s)
- Paolo Bajardi
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Turin, Italy
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46
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Tinsley M, Lewis FI, Brülisauer F. Network modeling of BVD transmission. Vet Res 2012; 43:11. [PMID: 22325043 PMCID: PMC3295666 DOI: 10.1186/1297-9716-43-11] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2011] [Accepted: 02/10/2012] [Indexed: 11/10/2022] Open
Abstract
Endemic diseases of cattle, such as bovine viral diarrhea, have significant impact on production efficiency of food of animal origin with consequences for animal welfare and climate change reduction targets. Many modeling studies focus on the local scale, examining the on-farm dynamics of this infectious disease. However, insight into prevalence and control across a network of farms ultimately requires a network level approach. Here, we implement understanding of infection dynamics, gained through these detailed on-farm modeling studies, to produce a national scale model of bovine viral diarrhea virus transmission. The complex disease epidemiology and on-farm dynamics are approximated using SIS dynamics with each farm treated as a single unit. Using a top down approach, we estimate on-farm parameters associated with contraction and subsequent clearance from infection at herd level. We examine possible control strategies associated with animal movements between farms and find measures targeted at a small number of high-movement farms efficient for rapid and sustained prevalence reduction.
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Affiliation(s)
- Mark Tinsley
- C, Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, USA.
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47
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Mathematical Models of Infectious Diseases in Livestock: Concepts and Application to the Spread of Highly Pathogenic Avian Influenza Virus Strain Type H5N1. HEALTH AND ANIMAL AGRICULTURE IN DEVELOPING COUNTRIES 2012. [PMCID: PMC7120485 DOI: 10.1007/978-1-4419-7077-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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48
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Farm-level reproduction number during an epidemic of infectious salmon anemia virus in southern Chile in 2007–2009. Prev Vet Med 2011; 102:175-84. [DOI: 10.1016/j.prevetmed.2011.07.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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49
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Perez A, Alkhamis M, Carlsson U, Brito B, Carrasco-Medanic R, Whedbee Z, Willeberg P. Global animal disease surveillance. Spat Spatiotemporal Epidemiol 2011; 2:135-45. [PMID: 22748173 PMCID: PMC7185519 DOI: 10.1016/j.sste.2011.07.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Development and implementation of global animal disease surveillance has been limited by the lack of information systems that enable near real-time data capturing, sharing, analysis, and related decision- and policy-making. The objective of this paper is to describe requirements for global animal disease surveillance, including design and functionality of tools and methods for visualization and analysis of animal disease data. The paper also explores the potential application of techniques for spatial and spatio-temporal analysis on global animal disease surveillance, including for example, landscape genetics, social network analysis, and Bayesian modeling. Finally, highly pathogenic avian influenza data from Denmark and Sweden are used to illustrate the potential application of a novel system (Disease BioPortal) for data sharing, visualization, and analysis for regional and global surveillance efforts.
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Affiliation(s)
- A Perez
- Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, USA.
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50
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Bajardi P, Barrat A, Natale F, Savini L, Colizza V. Dynamical patterns of cattle trade movements. PLoS One 2011; 6:e19869. [PMID: 21625633 PMCID: PMC3097215 DOI: 10.1371/journal.pone.0019869] [Citation(s) in RCA: 155] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2011] [Accepted: 04/06/2011] [Indexed: 11/24/2022] Open
Abstract
Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions.
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Affiliation(s)
- Paolo Bajardi
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy
- Centre de Physique Théorique (Centre National de la Recherche Scientifique UMR 6207), Marseille, France
| | - Alain Barrat
- Centre de Physique Théorique (Centre National de la Recherche Scientifique UMR 6207), Marseille, France
- Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy
| | - Fabrizio Natale
- European Commission, Joint Research Center, Institute for the Protection and Security of the Citizen, Ispra, Italy
| | | | - Vittoria Colizza
- INSERM, U707, Paris, France
- UPMC Université Paris 06, Faculté de Médecine Pierre et Marie Curie, UMR S 707, Paris, France
- Institute for Scientific Interchange (ISI), Torino, Italy
- * E-mail:
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