27,784 research outputs found
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
Regression approaches for Approximate Bayesian Computation
This book chapter introduces regression approaches and regression adjustment
for Approximate Bayesian Computation (ABC). Regression adjustment adjusts
parameter values after rejection sampling in order to account for the imperfect
match between simulations and observations. Imperfect match between simulations
and observations can be more pronounced when there are many summary statistics,
a phenomenon coined as the curse of dimensionality. Because of this imperfect
match, credibility intervals obtained with regression approaches can be
inflated compared to true credibility intervals. The chapter presents the main
concepts underlying regression adjustment. A theorem that compares theoretical
properties of posterior distributions obtained with and without regression
adjustment is presented. Last, a practical application of regression adjustment
in population genetics shows that regression adjustment shrinks posterior
distributions compared to rejection approaches, which is a solution to avoid
inflated credibility intervals.Comment: Book chapter, published in Handbook of Approximate Bayesian
Computation 201
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene
regulation and chemical signalling. Therefore, characterising stochastic
effects in biochemical systems is essential to understand the complex dynamics
of living things. Mathematical idealisations of biochemically reacting systems
must be able to capture stochastic phenomena. While robust theory exists to
describe such stochastic models, the computational challenges in exploring
these models can be a significant burden in practice since realistic models are
analytically intractable. Determining the expected behaviour and variability of
a stochastic biochemical reaction network requires many probabilistic
simulations of its evolution. Using a biochemical reaction network model to
assist in the interpretation of time course data from a biological experiment
is an even greater challenge due to the intractability of the likelihood
function for determining observation probabilities. These computational
challenges have been subjects of active research for over four decades. In this
review, we present an accessible discussion of the major historical
developments and state-of-the-art computational techniques relevant to
simulation and inference problems for stochastic biochemical reaction network
models. Detailed algorithms for particularly important methods are described
and complemented with MATLAB implementations. As a result, this review provides
a practical and accessible introduction to computational methods for stochastic
models within the life sciences community
Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model
Food webs, networks of feeding relationships among organisms, provide
fundamental insights into mechanisms that determine ecosystem stability and
persistence. Despite long-standing interest in the compartmental structure of
food webs, past network analyses of food webs have been constrained by a
standard definition of compartments, or modules, that requires many links
within compartments and few links between them. Empirical analyses have been
further limited by low-resolution data for primary producers. In this paper, we
present a Bayesian computational method for identifying group structure in food
webs using a flexible definition of a group that can describe both functional
roles and standard compartments. The Serengeti ecosystem provides an
opportunity to examine structure in a newly compiled food web that includes
species-level resolution among plants, allowing us to address whether groups in
the food web correspond to tightly-connected compartments or functional groups,
and whether network structure reflects spatial or trophic organization, or a
combination of the two. We have compiled the major mammalian and plant
components of the Serengeti food web from published literature, and we infer
its group structure using our method. We find that network structure
corresponds to spatially distinct plant groups coupled at higher trophic levels
by groups of herbivores, which are in turn coupled by carnivore groups. Thus
the group structure of the Serengeti web represents a mixture of trophic guild
structure and spatial patterns, in contrast to the standard compartments
typically identified in ecological networks. From data consisting only of nodes
and links, the group structure that emerges supports recent ideas on spatial
coupling and energy channels in ecosystems that have been proposed as important
for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting
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