Eight challenges for network epidemic models

Pellis, Lorenzo; Ball, Frank; Bansal, Shweta; Eames, Ken T. D.; House, Thomas; Isham, Valerie; Trapman, Pieter

doi:10.1016/j.epidem.2014.07.003

Cited by 177 publications

(145 citation statements)

References 37 publications

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“…In particular, in network models there are difficulties for computing the reproduction number and the prevalence of the disease. Consequently, little can be learned of the effect of the immune response on these quantities outside of extensive simulations [52].…”

Section: A Network Epidemic Modelsmentioning

confidence: 99%

Coupling Within-Host and Between-Host Infectious Diseases Models

Martcheva

Tuncer

Mary

2015

BIOMATH

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Abstract-Biological processes occur at distinct but interlinked scales of organization. Yet, mathematical models are often focused on a single scale. Recently, there has been a significant interest in creating and using models that link the within-host dynamics and population level dynamics of infectious diseases. These types of multi-scale models, called immuno-epidemiological models, fall in four categories, dependent on the type of the epidemiological component of the model: network or individual based models (IBM), "nested" agesince-infection structured models, ordinary differential equation (ODE) models, and "size-structured" models. Immuno-epidemiological multi-scale models have been used to address a variety of questions, including what is the impact of within-host dynamics on population-level quantities such as reproduction number and prevalence, as well as questions related to evolution of the pathogen or co-evolution of the pathogen and the host. Here we review the literature on immuno-epidemiological modeling as well as the main insights these models have created.

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Section: A Network Epidemic Modelsmentioning

confidence: 99%

Coupling Within-Host and Between-Host Infectious Diseases Models

Martcheva

Tuncer

Mary

2015

BIOMATH

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“…While early models did not include complex population structure, modeling approaches now frequently let the epidemic spread take place on a network, which enables greater realism than a model in which all individuals mix homogeneously. It does, however, pose many technical problems for model analysis, particularly the question of how heterogeneity in the number of links each individual participates in-their degree-influences the epidemic [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Stochastic epidemic dynamics on extremely heterogeneous networks

2016

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Networks of contacts capable of spreading infectious diseases are often observed to be highly heterogeneous, with the majority of individuals having fewer contacts than the mean, and a significant minority having relatively very many contacts. We derive a two-dimensional diffusion model for the full temporal behavior of the stochastic susceptible-infectious-recovered (SIR) model on such a network, by making use of a time-scale separation in the deterministic limit of the dynamics. This low-dimensional process is an accurate approximation to the full model in the limit of large populations, even for cases when the time-scale separation is not too pronounced, provided the maximum degree is not of the order of the population size.

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“…In this case, using measures of social mixing, such as number of contacts per day, can be highly consistent across regions [31,32], representative for most connectivities relevant to disease spread [33,34], and need not to be dataintensive [35].…”

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confidence: 99%

High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Nguyen

Mikolajczyk

Hernandez‐Vargas

2017

Preprint

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Background: Transmission in epidemics of infectious diseases is characterized by a high level of subject-specific elements. These include heterogeneous infection conditions, time-dependent transmission potential, and age-dependent contact structure. These insights are often lost in epidemic models using population data. Here we submit an approach that can capture these details, paving the way for studying epidemics in a more mechanistic and realistic way. Methods: Using experimental data, we formulated mathematical models of a pathogen infection dynamics from which we can simulate its transmission potential mechanistically. The models were then embedded in our implement of an age-specific contact network structure that allows to express all elements relevant to the transmission process. This approach is illustrated here with an example of Ebola virus (EBOV). Results:The results showed that within-host infection dynamics can capture EBOV's transmission parameters as good as approaches using population data. Population age-structure, contact distribution and patterns can also be captured with our network generating algorithm. This framework opens vast opportunities for the investigations of each element involved in the epidemic process. Here, estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting questions on current estimates which are not adjusted for this factor. Assessments of mass vaccination strategies showed that a time window from five months before to one week after the start of an epidemic appeared to be effective. Noticeably, compared to a non-intervention scenario, a low vaccination coverage of 33% could reduce number of cases by ten to hundred times as well as lessen the case-fatality rate. Conclusions: This is the first effort coupling directly within-host infection model into an age-structured epidemic network model, adding more realistic elements in simulating epidemic processes. Experimental data at the within-host infection are shown able to capture upfront key parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. Population of interest in epidemic assessments could be modeled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios are underway to explore multilevel aspects in infectious diseases epidemics.Keywords: high-resolution; epidemic; simulation; within-host infection; age-structure; contact network CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al. Page 2 of 22 BackgroundEpidemics of infectious diseases are listed among the potential catastrophes and can be potentially misused as mass destruction weapons [1]. Overwhelming research efforts have been developed to early predict the danger ...

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Eight challenges for network epidemic models

Cited by 177 publications

References 37 publications

Coupling Within-Host and Between-Host Infectious Diseases Models

Coupling Within-Host and Between-Host Infectious Diseases Models

Stochastic epidemic dynamics on extremely heterogeneous networks

High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

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