Time evolution of predictability of epidemics on networks

@article{Holme2015TimeEO,
  title={Time evolution of predictability of epidemics on networks},
  author={Petter Holme and Taro Takaguchi},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2015},
  volume={91 4},
  pages={
          042811
        }
}
  • Petter Holme, T. Takaguchi
  • Published 15 December 2014
  • Environmental Science
  • Physical review. E, Statistical, nonlinear, and soft matter physics
Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different… 
View on PubMed
arxiv.org
11 Citations
Background Citations
2
Methods Citations
2

Figures and Tables from this paper

11 Citations

Citation Type

Citation Type

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…
Predicting epidemic evolution on contact networks from partial observations
TLDR
It is shown that the Belief Propagation method can be applied to predict the future evolution of an epidemic outbreak from partial observations at the early stage of the dynamics.
Information content of contact-pattern representations and predictability of epidemic outbreaks
TLDR
This paper studies both outbreak sizes from unknown sources, and from known states of ongoing outbreaks, using empirical proximity data to investigate the effect of successive inclusions of more information in the temporal network structure of the data sets.
Interplay between the local information based behavioral responses and the epidemic spreading in complex networks
TLDR
This study introduces a new class of individuals, SF, to the classical susceptible-infected-recovered model and indicates that, with the increasing of the response rate, the epidemic threshold is enhanced and the prevalence of epidemic is reduced.
Spreading to localized targets in complex networks
TLDR
Simulation results show that the proposed reversed local path algorithm outperforms the existing methods in identifying the influential nodes with respect to localized targets in complex networks and can effectively avoid infecting the non-target nodes in the spreading process.
The spreading ability of nodes towards localized targets in complex networks
TLDR
Simulation results show that the reversed local path algorithm outperforms the existing methods in identifying the influential nodes with respect to these localized targets and the influential spreaders identified by the method can effectively avoid infecting the non-target nodes in the spreading process.
Quantitative Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission
TLDR
This work studies the COVID-19 epidemics in Brazilian cities using early-time approximations of the SIR model in networks to reveal that social isolation and, especially, the use of masks can effectively reduce the transmission rate of CO VID-19 in Brazil.
Exploring Limits to Prediction in Complex Social Systems
TLDR
It is shown that although higher predictive power is possible in theory, such performance requires a homogeneous system and perfect ex-ante knowledge of it: even a small degree of uncertainty in estimating product quality or slight variation in quality across products leads to substantially more restrictive bounds on predictability.
Prediction of competitive diffusion on complex networks
Dynamics of information diffusion and its applications on complex networks
...
...

References

SHOWING 1-10 OF 50 REFERENCES
Extinction Times of Epidemic Outbreaks in Networks
TLDR
This work characterize how the maximal extinction time in SIR simulations on networks depend on the network structure, and finds that the average distances in isolated components, weighted by the component size, is a good predictor of the maximal time to extinction.
Epidemic outbreaks in complex heterogeneous networks
TLDR
The large connectivity fluctuations usually found in these networks strengthen considerably the incidence of epidemic outbreaks and defines a new epidemiological framework characterized by a highly heterogeneous response of the system to the introduction of infected individuals with different connectivity.
Networks and epidemic models
TLDR
A variety of methods are described that allow the mixing network, or an approximation to the network, to be ascertained and how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control are suggested.
Superspreading and the effect of individual variation on disease emergence
TLDR
It is shown that contact tracing data from eight directly transmitted diseases shows that the distribution of individual infectiousness around R0 is often highly skewed, and implications for outbreak control are explored, showing that individual-specific control measures outperform population-wide measures.
Law of large numbers for the SIR epidemic on a random graph with given degrees
TLDR
The main result is that, conditional on a large outbreak, the evolutions of certain quantities of interest, such as the fraction of infective vertices, converge to deterministic functions of time.
Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data
TLDR
This work introduces a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks, and is the first tool for disease outbreak reconstruction from genetic data widely available as free software, the R package outbreaker.
Metapopulation mean life time within complex networks
TLDR
A graph-theoretical framework for analysing the dispersal network is provided and this model is applied to the small-world network of the Great Barrier Reef as well as to conservation planning in general.
Networks: An Introduction
TLDR
This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Collective dynamics of ‘small-world’ networks
TLDR
Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
The Contact Network of Inpatients in a Regional Healthcare System. A Longitudinal Case Study
TLDR
The structure of contacts between inpatients are found to exhibit structural properties such as a high level of transitivity, assortativity, and variation by number of contacts, which are likely to be of importance for the transmission of less contagious diseases.
...
...