24,500 research outputs found
Multi-state epidemic processes on complex networks
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
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
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
In this paper, we study the (susceptible-infected-susceptible) and
(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 dynamics, the basic reproductive ratio is
computed, and we show that (i) for both network models 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 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
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
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.
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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