1,427 research outputs found
Predicting epidemic evolution on contact networks from partial observations
The massive employment of computational models in network epidemiology calls
for the development of improved inference methods for epidemic forecast. For
simple compartment models, such as the Susceptible-Infected-Recovered model,
Belief Propagation was proved to be a reliable and efficient method to identify
the origin of an observed epidemics. Here we show that the same method can be
applied to predict the future evolution of an epidemic outbreak from partial
observations at the early stage of the dynamics. The results obtained using
Belief Propagation are compared with Monte Carlo direct sampling in the case of
SIR model on random (regular and power-law) graphs for different observation
methods and on an example of real-world contact network. Belief Propagation
gives in general a better prediction that direct sampling, although the quality
of the prediction depends on the quantity under study (e.g. marginals of
individual states, epidemic size, extinction-time distribution) and on the
actual number of observed nodes that are infected before the observation time
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
Temporal Fidelity in Dynamic Social Networks
It has recently become possible to record detailed social interactions in
large social systems with high resolution. As we study these datasets, human
social interactions display patterns that emerge at multiple time scales, from
minutes to months. On a fundamental level, understanding of the network
dynamics can be used to inform the process of measuring social networks. The
details of measurement are of particular importance when considering dynamic
processes where minute-to-minute details are important, because collection of
physical proximity interactions with high temporal resolution is difficult and
expensive. Here, we consider the dynamic network of proximity-interactions
between approximately 500 individuals participating in the Copenhagen Networks
Study. We show that in order to accurately model spreading processes in the
network, the dynamic processes that occur on the order of minutes are essential
and must be included in the analysis
Data-Centric Epidemic Forecasting: A Survey
The COVID-19 pandemic has brought forth the importance of epidemic
forecasting for decision makers in multiple domains, ranging from public health
to the economy as a whole. While forecasting epidemic progression is frequently
conceptualized as being analogous to weather forecasting, however it has some
key differences and remains a non-trivial task. The spread of diseases is
subject to multiple confounding factors spanning human behavior, pathogen
dynamics, weather and environmental conditions. Research interest has been
fueled by the increased availability of rich data sources capturing previously
unobservable facets and also due to initiatives from government public health
and funding agencies. This has resulted, in particular, in a spate of work on
'data-centered' solutions which have shown potential in enhancing our
forecasting capabilities by leveraging non-traditional data sources as well as
recent innovations in AI and machine learning. This survey delves into various
data-driven methodological and practical advancements and introduces a
conceptual framework to navigate through them. First, we enumerate the large
number of epidemiological datasets and novel data streams that are relevant to
epidemic forecasting, capturing various factors like symptomatic online
surveys, retail and commerce, mobility, genomics data and more. Next, we
discuss methods and modeling paradigms focusing on the recent data-driven
statistical and deep-learning based methods as well as on the novel class of
hybrid models that combine domain knowledge of mechanistic models with the
effectiveness and flexibility of statistical approaches. We also discuss
experiences and challenges that arise in real-world deployment of these
forecasting systems including decision-making informed by forecasts. Finally,
we highlight some challenges and open problems found across the forecasting
pipeline.Comment: 67 pages, 12 figure
Belief Propagation approach to epidemics prediction on networks
In my thesis I study the problem of predicting the evolution of the epidemic spreading on networks when incomplete information, in form of a partial observation, is available. I focus on the irreversible process described by the discrete time version of the Susceptible-Infected-Recovered (SIR) model on networks. Because of its intrinsic stochasticity, forecasting the SIR process is very difficult, even if the structure of individuals contact pattern is known. In today's interconnected and interdependent society, infectious diseases pose the threat of a worldwide epidemic spreading, hence governments and public health systems maintain surveillance programs to report and control the emergence of new disease event ranging from the seasonal influenza to the more severe HIV or Ebola. When new infection cases are discovered in the population it is necessary to provide real-time forecasting of the epidemic evolution. However the incompleteness of accessible data and the intrinsic stochasticity of the contagion pose a major challenge.
The idea behind the work of my thesis is that the correct inference of the contagion process before the detection of the disease permits to use all the available information and, consequently, to obtain reliable predictions. I use the Belief Propagation approach for the prediction of SIR epidemics when a partial observation is available. In this case the reconstruction of the past dynamics can be efficiently performed by this method and exploited to analyze the evolution of the disease. Although the Belief Propagation provides exact results on trees, it turns out that is still a good approximation on general graphs. In this cases Belief Propagation may present convergence related issues, especially on dense networks. Moreover, since this approach is based on a very general principle, it can be adapted to study a wide range of issues, some of which I analyze in the thesis
Modern temporal network theory: A colloquium
The power of any kind of network approach lies in the ability to simplify a
complex system so that one can better understand its function as a whole.
Sometimes it is beneficial, however, to include more information than in a
simple graph of only nodes and links. Adding information about times of
interactions can make predictions and mechanistic understanding more accurate.
The drawback, however, is that there are not so many methods available, partly
because temporal networks is a relatively young field, partly because it more
difficult to develop such methods compared to for static networks. In this
colloquium, we review the methods to analyze and model temporal networks and
processes taking place on them, focusing mainly on the last three years. This
includes the spreading of infectious disease, opinions, rumors, in social
networks; information packets in computer networks; various types of signaling
in biology, and more. We also discuss future directions.Comment: Final accepted versio
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