6,763 research outputs found

    Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

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    Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7 figure

    Simulation modeling of zoonotic diseases between swine and human populations for informing policy decisions

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    Approximately 60% of human pathogens and emerging infectious diseases are zoonotic. Simulation models are increasingly being used to investigate the spread of diseases, evaluate intervention strategies and guide the decisions of policy makers. In this thesis a systematic review of modeling methods and approaches used for zoonotic influenza in animals and humans was conducted, and knowledge gaps were identified. Furthermore, the disease spread and intervention parameters used in these studies were summarized for ready reference in future work. Building on this review work, the research presented in this thesis evaluated the effects of transmissibility of the pandemic H1N1 2009 (pH1N1) virus at the swine- human interface and the control strategies against its spread in swine and human populations as a case study for zoonotic disease modeling. The feasibility of North American Animal Disease Spread Model (NAADSM) for modeling directly transmitted zoonoses was also assessed. Population data based on swine herds and households (categorized as rural households with or without swine workers, and urban households without swine workers) of a county in Ontario, Canada was used. The swine workers served as a bridging population for the spread of the virus between swine herds and households. Scenarios based on the combinations of the transmissibility of the virus (low (L), medium (M), and high (H)) from swine-to-human and human-to-swine (LL, ML, HL, MM, HM, LL), and targeted vaccination of swine worker households (0% to 60%) were evaluated. The results showed that lowering the influenza transmissibility at the interface to low level and providing higher vaccine coverage (60%) had significant beneficial effects on all outcome measures. However, these measures had little or negligible impact on the total number of rural and urban households infected. A set of models evaluating the combination of control strategies indicated that a moderate speed of the detection (within 5 to 10 days of the first infection), combined with the quarantine of detected units alone, contained the outbreak within the swine population in most simulations. However, a zone-based quarantine strategy was more effective when the detection was delayed until around three weeks after initial infection. Ring vaccination had no added beneficial effect. This work suggested that NAADSM can be used for modeling the directly transmitted zoonotic diseases under similar simplifying assumptions adopted in these studies. However, this needs to be evaluated further with more accurate parameters and influenza outbreak data. To fill in some of the gaps identified in the review study, network analyses of swine shipments among farms, and between farms and abattoirs were conducted. This provided network metrics and parameters necessary for disease modeling and risk-based disease management in swine in Ontario for the first time. Finally, agent-based network models assessing the spread and control of pH1N1 in swine established the importance of explicitly incorporating appropriate contact network structures into such models to increase their validity. It also demonstrated the benefits of targeted control strategies against highly connected farms. In conclusion, the modeling tools developed in this thesis can assist decision makers in preparedness and response of outbreaks of infectious diseases as more information become available for the parameterization of models

    Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model

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    Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes

    The effectiveness of policies to control a human influenza pandemic : a literature review

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    The studies reviewed in this paper indicate that with adequate preparedness planning and execution it is possible to contain pandemic influenza outbreaks where they occur, for viral strains of moderate infectiousness. For viral strains of higher infectiousness, containment may be difficult, but it may be possible to mitigate the effects of the spread of pandemic influenza within a country and/or internationally with a combination of policies suited to the origins and nature of the initial outbreak. These results indicate the likelihood of containment success in'frontline risk'countries, given specific resource availability and level of infectiousness; as well as mitigation success in'secondary'risk countries, given the assumption of inevitable international transmission through air travel networks. However, from the analysis of the modeling results on interventions in the U.S. and U.K. after a global pandemic starts, there is a basis for arguing that the emphasis in the secondary risk countries could shift from mitigation towards containment. This follows since a mitigation-focused strategy in such developed countries presupposes that initial outbreak containment in these countries will necessarily fail. This is paradoxical if containment success at similar infectiousness of the virus is likely in developing countries with lower public health resources, based on results using similar modeling methodologies. Such a shift in emphasis could have major implications for global risk management for diseases of international concern such as pandemic influenza or a SARS-like disease.Avian Flu,Disease Control&Prevention,Health Monitoring&Evaluation,Population Policies,HIV AIDS

    Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

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    Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

    Analysing livestock network data for infectious diseases control:an argument for routine data collection in emerging economies

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    Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

    Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations

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    This is the final version. Available on open access from the Royal Society via the DOI in this record.The emergence and spread of infections can contribute to the decline and extinction of populations, particularly in conjunction with anthropogenic environmental change. The importance of heterogeneity in processes of transmission, resistance and tolerance is increasingly well understood in theory, but empirical studies that consider both the demographic and behavioural implications of infection are scarce. Non-random mixing of host individuals can impact the demographic thresholds that determine the amplification or attenuation of disease prevalence. Risk assessment and management of disease in threatened wildlife populations must therefore consider not just host density, but also the social structure of host populations. Here we integrate the most recent developments in epidemiological research from a demographic and social network perspective and synthesise the latest developments in social network modelling for wildlife disease, to explore their applications to disease management in populations in decline and at risk of extinction. We use simulated examples to support our key points and reveal how disease-management strategies can and should exploit both behavioural and demographic information to prevent or control the spread of disease. Our synthesis highlights the importance of considering the combined impacts of demographic and behavioural processes in epidemics to successful disease management in a conservation context.Natural Environment Research Council (NERC)Animal and Plant Health AgencyUniversity of Exete
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