2,944 research outputs found
Variance in System Dynamics and Agent Based Modelling Using the SIR Model of Infectious Disease
Classical deterministic simulations of epidemiological processes, such as
those based on System Dynamics, produce a single result based on a fixed set of
input parameters with no variance between simulations. Input parameters are
subsequently modified on these simulations using Monte-Carlo methods, to
understand how changes in the input parameters affect the spread of results for
the simulation. Agent Based simulations are able to produce different output
results on each run based on knowledge of the local interactions of the
underlying agents and without making any changes to the input parameters. In
this paper we compare the influence and effect of variation within these two
distinct simulation paradigms and show that the Agent Based simulation of the
epidemiological SIR (Susceptible, Infectious, and Recovered) model is more
effective at capturing the natural variation within SIR compared to an
equivalent model using System Dynamics with Monte-Carlo simulation. To
demonstrate this effect, the SIR model is implemented using both System
Dynamics (with Monte-Carlo simulation) and Agent Based Modelling based on
previously published empirical data.Comment: Proceedings of the 26th European Conference on Modelling and
Simulation (ECMS), Koblenz, Germany, May 2012, pp 9-15, 201
The effects of heterogeneity on stochastic cycles in epidemics
Models of biological processes are often subject to different sources of
noise. Developing an understanding of the combined effects of different types
of uncertainty is an open challenge. In this paper, we study a variant of the
susceptible-infective-recovered model of epidemic spread, which combines both
agent-to-agent heterogeneity and intrinsic noise. We focus on epidemic cycles,
driven by the stochasticity of infection and recovery events, and study in
detail how heterogeneity in susceptibilities and propensities to pass on the
disease affects these quasi-cycles. While the system can only be described by a
large hierarchical set of equations in the transient regime, we derive a
reduced closed set of equations for population-level quantities in the
stationary regime. We analytically obtain the spectra of quasi-cycles in the
linear-noise approximation. We find that the characteristic frequency of these
cycles is typically determined by population averages of susceptibilities and
infectivities, but that their amplitude depends on higher-order moments of the
heterogeneity. We also investigate the synchronisation properties and phase lag
between different groups of susceptible and infected individuals.Comment: Main text 16 pages, 9 figures. Supplement 5 page
Variance in system dynamics and agent based modelling using the SIR model of infectious diseases
Classical deterministic simulations of epidemiological processes, such as those based on System Dynamics, produce a single result based on a fixed set of input parameters with no variance between simulations. Input parameters are subsequently modified on these simulations using Monte-Carlo methods, to understand how changes in the input parameters affect the spread of results for the simulation. Agent Based simulations are able to produce different output results on each run based on knowledge of the local interactions of the underlying agents and without making any changes to the input parameters. In this paper we compare the influence and effect of variation within these two distinct simulation paradigms and show that the Agent Based simulation of the epidemiological SIR (Susceptible, Infectious, and Recovered) model is more effective at capturing the natural variation within SIR compared to an equivalent model using System Dynamics with Monte-Carlo simulation. To demonstrate this effect, the SIR model is implemented using both System Dynamics (with Monte-Carlo simulation) and Agent Based Modelling based on previously published empirical data
A network epidemic model with preventive rewiring: comparative analysis of the initial phase
This paper is concerned with stochastic SIR and SEIR epidemic models on
random networks in which individuals may rewire away from infected neighbors at
some rate (and reconnect to non-infectious individuals with
probability or else simply drop the edge if ), so-called
preventive rewiring. The models are denoted SIR- and SEIR-, and
we focus attention on the early stages of an outbreak, where we derive
expression for the basic reproduction number and the expected degree of
the infectious nodes using two different approximation approaches. The
first approach approximates the early spread of an epidemic by a branching
process, whereas the second one uses pair approximation. The expressions are
compared with the corresponding empirical means obtained from stochastic
simulations of SIR- and SEIR- epidemics on Poisson and
scale-free networks. Without rewiring of exposed nodes, the two approaches
predict the same epidemic threshold and the same for both types of
epidemics, the latter being very close to the mean degree obtained from
simulated epidemics over Poisson networks. Above the epidemic threshold,
pairwise models overestimate the value of computed from simulations,
which turns out to be very close to the one predicted by the branching process
approximation. When exposed individuals also rewire with (perhaps
unaware of being infected), the two approaches give different epidemic
thresholds, with the branching process approximation being more in agreement
with simulations.Comment: 25 pages, 7 figure
The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system
Heterogeneity in host populations is an important factor affecting the ability of a pathogen to invade, yet the quantitative investigation of its effects on epidemic spread is still an open problem. In this paper, we test recent theoretical results, which extend the established âpercolation paradigmâ to the spread of a pathogen in discrete heterogeneous host populations. In particular, we test the hypothesis that the probability of epidemic invasion decreases when host heterogeneity is increased. We use replicated experimental microcosms, in which the ubiquitous pathogenic fungus Rhizoctonia solani grows through a population of discrete nutrient sites on a lattice, with nutrient sites representing hosts. The degree of host heterogeneity within different populations is adjusted by changing the proportion and the nutrient concentration of nutrient sites. The experimental data are analysed via Bayesian inference methods, estimating pathogen transmission parameters for each individual population. We find a significant, negative correlation between heterogeneity and the probability of pathogen invasion, thereby validating the theory. The value of the correlation is also in remarkably good agreement with the theoretical predictions. We briefly discuss how our results can be exploited in the design and implementation of disease control strategies
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