51,882 research outputs found
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
Epidemic spreading on preferred degree adaptive networks
We study the standard SIS model of epidemic spreading on networks where
individuals have a fluctuating number of connections around a preferred degree
. Using very simple rules for forming such preferred degree networks,
we find some unusual statistical properties not found in familiar
Erd\H{o}s-R\'{e}nyi or scale free networks. By letting depend on the
fraction of infected individuals, we model the behavioral changes in response
to how the extent of the epidemic is perceived. In our models, the behavioral
adaptations can be either `blind' or `selective' -- depending on whether a node
adapts by cutting or adding links to randomly chosen partners or selectively,
based on the state of the partner. For a frozen preferred network, we find that
the infection threshold follows the heterogeneous mean field result
and the phase diagram matches the predictions of
the annealed adjacency matrix (AAM) approach. With `blind' adaptations,
although the epidemic threshold remains unchanged, the infection level is
substantially affected, depending on the details of the adaptation. The
`selective' adaptive SIS models are most interesting. Both the threshold and
the level of infection changes, controlled not only by how the adaptations are
implemented but also how often the nodes cut/add links (compared to the time
scales of the epidemic spreading). A simple mean field theory is presented for
the selective adaptations which capture the qualitative and some of the
quantitative features of the infection phase diagram.Comment: 21 pages, 7 figure
Mathematical Modeling of Trending Topics on Twitter
Created in 2006, Twitter is an online social networking service in which users share and read 140-character messages called Tweets. The site has approximately 288 million monthly active users who produce about 500 million Tweets per day. This study applies dynamical and statistical modeling strategies to quantify the spread of information on Twitter. Parameter estimates for the rates of infection and recovery are obtained using Bayesian Markov Chain Monte Carlo (MCMC) methods. The methodological strategy employed is an extension of techniques traditionally used in an epidemiological and biomedical context (particularly in the spread of infectious disease). This study, which addresses information spread, presents case studies pertaining to the prevalence of several “trending” topics on Twitter over time. The study introduces a framework to compare information dynamics on Twitter based on the topical area as well as a framework for the prediction of topic prevalence. Additionally, methodological and results-based comparisons are drawn between the spread of information and the spread of infectious disease
Network Inoculation: Heteroclinics and phase transitions in an epidemic model
In epidemiological modelling, dynamics on networks, and in particular
adaptive and heterogeneous networks have recently received much interest. Here
we present a detailed analysis of a previously proposed model that combines
heterogeneity in the individuals with adaptive rewiring of the network
structure in response to a disease. We show that in this model qualitative
changes in the dynamics occur in two phase transitions. In a macroscopic
description one of these corresponds to a local bifurcation whereas the other
one corresponds to a non-local heteroclinic bifurcation. This model thus
provides a rare example of a system where a phase transition is caused by a
non-local bifurcation, while both micro- and macro-level dynamics are
accessible to mathematical analysis. The bifurcation points mark the onset of a
behaviour that we call network inoculation. In the respective parameter region
exposure of the system to a pathogen will lead to an outbreak that collapses,
but leaves the network in a configuration where the disease cannot reinvade,
despite every agent returning to the susceptible class. We argue that this
behaviour and the associated phase transitions can be expected to occur in a
wide class of models of sufficient complexity.Comment: 26 pages, 11 figure
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
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Emerging Challenges and Opportunities in Infectious Disease Epidemiology.
Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners
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