24,500 research outputs found

    Multi-state epidemic processes on complex networks

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    Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyze steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.Comment: 7 figures, 1 tabl

    Transmission routes of African swine fever virus to domestic pigs: current knowledge and future research directions

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    African swine fever (ASF) is a major threat to the pig industry in Europe. Since 2007, ASF outbreaks have been ongoing in the Caucasus, Eastern Europe and the Baltic countries, causing severe economic losses for many pig farmers and pork producers. In addition, the number of ASF cases in wild boar populations has dramatically increased over the past few years. Evidence supports direct contact with infectious domestic pigs and wild boars, and consumption of contaminated feed, as the main transmission routes of ASF virus (ASFV) to domestic pigs. However, significant knowledge gaps highlight the urgent need for research to investigate the dynamics of indirect transmission via the environment, the minimal infective doses for contaminated feed ingestion, the probability of effective contacts between infectious wild boars and domestic pigs, the potential for recovered animals to become carriers and a reservoir for transmission, the potential virus persistence within wild boar populations and the influence of human behaviour for the spread of ASFV. This will provide an improved scientific basis to optimise current interventions and develop new tools and strategies to reduce the risk of ASFV transmission to domestic pigs.ISSN:0042-490

    Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses

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    Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior

    A class of pairwise models for epidemic dynamics on weighted networks

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    In this paper, we study the SISSIS (susceptible-infected-susceptible) and SIRSIR (susceptible-infected-removed) epidemic models on undirected, weighted networks by deriving pairwise-type approximate models coupled with individual-based network simulation. Two different types of theoretical/synthetic weighted network models are considered. Both models start from non-weighted networks with fixed topology followed by the allocation of link weights in either (i) random or (ii) fixed/deterministic way. The pairwise models are formulated for a general discrete distribution of weights, and these models are then used in conjunction with network simulation to evaluate the impact of different weight distributions on epidemic threshold and dynamics in general. For the SIRSIR dynamics, the basic reproductive ratio R0R_0 is computed, and we show that (i) for both network models R0R_{0} is maximised if all weights are equal, and (ii) when the two models are equally matched, the networks with a random weight distribution give rise to a higher R0R_0 value. The models are also used to explore the agreement between the pairwise and simulation models for different parameter combinations

    Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

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    Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were recorded. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data provides information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies

    Epidemiological models with parametric heterogeneity: Deterministic theory for closed populations

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    We present a unified mathematical approach to epidemiological models with parametric heterogeneity, i.e., to the models that describe individuals in the population as having specific parameter (trait) values that vary from one individuals to another. This is a natural framework to model, e.g., heterogeneity in susceptibility or infectivity of individuals. We review, along with the necessary theory, the results obtained using the discussed approach. In particular, we formulate and analyze an SIR model with distributed susceptibility and infectivity, showing that the epidemiological models for closed populations are well suited to the suggested framework. A number of known results from the literature is derived, including the final epidemic size equation for an SIR model with distributed susceptibility. It is proved that the bottom up approach of the theory of heterogeneous populations with parametric heterogeneity allows to infer the population level description, which was previously used without a firm mechanistic basis; in particular, the power law transmission function is shown to be a consequence of the initial gamma distributed susceptibility and infectivity. We discuss how the general theory can be applied to the modeling goals to include the heterogeneous contact population structure and provide analysis of an SI model with heterogeneous contacts. We conclude with a number of open questions and promising directions, where the theory of heterogeneous populations can lead to important simplifications and generalizations.Comment: 26 pages, 6 figures, submitted to Mathematical Modelling of Natural Phenomen

    Friend and foe: factors influencing the movement of the bacterium Helicobacter pylori along the parasitism-mutualism continuum.

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    Understanding the transition of bacterial species from commensal to pathogen, or vice versa, is a key application of evolutionary theory to preventative medicine. This requires working knowledge of the molecular interaction between hosts and bacteria, ecological interactions among microbes, spatial variation in bacterial prevalence or host life history, and evolution in response to these factors. However, there are very few systems for which such broad datasets are available. One exception is the gram-negative bacterium, Helicobacter pylori, which infects upwards of 50% of the global human population. This bacterium is associated with a wide breadth of human gastrointestinal disease, including numerous cancers, inflammatory disorders, and pathogenic infections, but is also known to confer fitness benefits to its host both indirectly, through interactions with other pathogens, and directly. Outstanding questions are therefore why, when, and how this bacterium transitions along the parasitism-mutualism continuum. We examine known virulence factors, genetic predispositions of the host, and environmental contributors that impact progression of clinical disease and help define geographical trends in disease incidence. We also highlight the complexity of the interaction and discuss future therapeutic strategies for disease management and public health in light of the longstanding evolutionary history between the bacterium and its human host
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