234 research outputs found
Inductive Reasoning Games as Influenza Vaccination Models: Mean Field Analysis
We define and analyze an inductive reasoning game of voluntary yearly
vaccination in order to establish whether or not a population of individuals
acting in their own self-interest would be able to prevent influenza epidemics.
We find that epidemics are rarely prevented. We also find that severe epidemics
may occur without the introduction of pandemic strains. We further address the
situation where market incentives are introduced to help ameliorating
epidemics. Surprisingly, we find that vaccinating families exacerbates
epidemics. However, a public health program requesting prepayment of
vaccinations may significantly ameliorate influenza epidemics.Comment: 20 pages, 7 figure
Health Newscasts for Increasing Influenza Vaccination Coverage: An Inductive Reasoning Game Approach
Both pandemic and seasonal influenza are receiving more attention from mass media than ever before. Topics such as epidemic severity and vaccination are changing the way in which we perceive the utility of disease prevention. Voluntary influenza vaccination has been recently modeled using inductive reasoning games. It has thus been found that severe epidemics may occur because individuals do not vaccinate and, instead, attempt to benefit from the immunity of their peers. Such epidemics could be prevented by voluntary vaccination if incentives were offered. However, a key assumption has been that individuals make vaccination decisions based on whether there was an epidemic each influenza season; no other epidemiological information is available to them. In this work, we relax this assumption and investigate the consequences of making more informed vaccination decisions while no incentives are offered. We obtain three major results. First, individuals will not cooperate enough to constantly prevent influenza epidemics through voluntary vaccination no matter how much they learned about influenza epidemiology. Second, broadcasting epidemiological information richer than whether an epidemic occurred may stabilize the vaccination coverage and suppress severe influenza epidemics. Third, the stable vaccination coverage follows the trend of the perceived benefit of vaccination. However, increasing the amount of epidemiological information released to the public may either increase or decrease the perceived benefit of vaccination. We discuss three scenarios where individuals know, in addition to whether there was an epidemic, (i) the incidence, (ii) the vaccination coverage and (iii) both the incidence and the vaccination coverage, every influenza season. We show that broadcasting both the incidence and the vaccination coverage could yield either better or worse vaccination coverage than broadcasting each piece of information on its own
On spinless null propagation in five-dimensional space-times with approximate space-like Killing symmetry
From regional pulse vaccination to global disease eradication: insights from a mathematical model of Poliomyelitis
Mass-vaccination campaigns are an important strategy in the global fight
against poliomyelitis and measles. The large-scale logistics required for these
mass immunisation campaigns magnifies the need for research into the
effectiveness and optimal deployment of pulse vaccination. In order to better
understand this control strategy, we propose a mathematical model accounting
for the disease dynamics in connected regions, incorporating seasonality,
environmental reservoirs and independent periodic pulse vaccination schedules
in each region. The effective reproduction number, , is defined and proved
to be a global threshold for persistence of the disease. Analytical and
numerical calculations show the importance of synchronising the pulse
vaccinations in connected regions and the timing of the pulses with respect to
the pathogen circulation seasonality. Our results indicate that it may be
crucial for mass-vaccination programs, such as national immunisation days, to
be synchronised across different regions. In addition, simulations show that a
migration imbalance can increase and alter how pulse vaccination should
be optimally distributed among the patches, similar to results found with
constant-rate vaccination. Furthermore, contrary to the case of constant-rate
vaccination, the fraction of environmental transmission affects the value of
when pulse vaccination is present.Comment: Added section 6.1, made other revisions, changed titl
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
The spread of infectious diseases crucially depends on the pattern of
contacts among individuals. Knowledge of these patterns is thus essential to
inform models and computational efforts. Few empirical studies are however
available that provide estimates of the number and duration of contacts among
social groups. Moreover, their space and time resolution are limited, so that
data is not explicit at the person-to-person level, and the dynamical aspect of
the contacts is disregarded. Here, we want to assess the role of data-driven
dynamic contact patterns among individuals, and in particular of their temporal
aspects, in shaping the spread of a simulated epidemic in the population.
We consider high resolution data of face-to-face interactions between the
attendees of a conference, obtained from the deployment of an infrastructure
based on Radio Frequency Identification (RFID) devices that assess mutual
face-to-face proximity. The spread of epidemics along these interactions is
simulated through an SEIR model, using both the dynamical network of contacts
defined by the collected data, and two aggregated versions of such network, in
order to assess the role of the data temporal aspects.
We show that, on the timescales considered, an aggregated network taking into
account the daily duration of contacts is a good approximation to the full
resolution network, whereas a homogeneous representation which retains only the
topology of the contact network fails in reproducing the size of the epidemic.
These results have important implications in understanding the level of
detail needed to correctly inform computational models for the study and
management of real epidemics
An Agent-Based Model to study the epidemiological and evolutionary dynamics of Influenza viruses
<p>Abstract</p> <p>Background</p> <p>Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.</p> <p>Results</p> <p>Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example.</p> <p>Conclusions</p> <p>We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution.</p
Basin boundary, edge of chaos, and edge state in a two-dimensional model
In shear flows like pipe flow and plane Couette flow there is an extended
range of parameters where linearly stable laminar flow coexists with a
transient turbulent dynamics. When increasing the amplitude of a perturbation
on top of the laminar flow, one notes a a qualitative change in its lifetime,
from smoothly varying and short one on the laminar side to sensitively
dependent on initial conditions and long on the turbulent side. The point of
transition defines a point on the edge of chaos. Since it is defined via the
lifetimes, the edge of chaos can also be used in situations when the turbulence
is not persistent. It then generalises the concept of basin boundaries, which
separate two coexisting attractors, to cases where the dynamics on one side
shows transient chaos and almost all trajectories eventually end up on the
other side. In this paper we analyse a two-dimensional map which captures many
of the features identified in laboratory experiments and direct numerical
simulations of hydrodynamic flows. The analysis of the map shows that different
dynamical situations in the edge of chaos can be combined with different
dynamical situations in the turbulent region. Consequently, the model can be
used to develop and test further characterisations that are also applicable to
realistic flows.Comment: 24 pages, 9 color figure
The Role of Environmental Transmission in Recurrent Avian Influenza Epidemics
Avian influenza virus (AIV) persists in North American wild waterfowl, exhibiting
major outbreaks every 2–4 years. Attempts to explain the patterns of
periodicity and persistence using simple direct transmission models are
unsuccessful. Motivated by empirical evidence, we examine the contribution of an
overlooked AIV transmission mode: environmental transmission. It is known that
infectious birds shed large concentrations of virions in the environment, where
virions may persist for a long time. We thus propose that, in addition to direct
fecal/oral transmission, birds may become infected by ingesting virions that
have long persisted in the environment. We design a new host–pathogen
model that combines within-season transmission dynamics, between-season
migration and reproduction, and environmental variation. Analysis of the model
yields three major results. First, environmental transmission provides a
persistence mechanism within small communities where epidemics cannot be
sustained by direct transmission only (i.e., communities smaller than the
critical community size). Second, environmental
transmission offers a parsimonious explanation of the 2–4 year
periodicity of avian influenza epidemics. Third, very low levels of
environmental transmission (i.e., few cases per year) are sufficient for avian
influenza to persist in populations where it would otherwise vanish
The spatial resolution of epidemic peaks
The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city); population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods). Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible
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