Six challenges in measuring contact networks for use in modelling

Eames, Ken T. D.; Bansal, Shweta; Frost, Simon D. W.; Riley, Steven

doi:10.1016/j.epidem.2014.08.006

Cited by 83 publications

(80 citation statements)

References 36 publications

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“…Even with 50% of edges, cases or nodes missing, the k-test often achieved higher power than degree-based tests with complete data. Missing edges had less impact on power than other types of missing data, which is reassuring given that interaction data are often the most under-sampled type of data in practice [32,55]. For the k-test, missing cases resulted in a greater reduction in power than missing nodes.…”

Section: Discussionmentioning

confidence: 86%

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Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

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Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

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Section: Discussionmentioning

confidence: 86%

“…Not only are temporal data often unavailable, but also other forms of data inaccuracy often arise due to missing data [32]. Collection of data for constructing networks and identifying infected nodes may be incomplete.…”

Section: Introductionmentioning

confidence: 99%

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…Transmission modeling has provided numerous insights into the underlying processes; however, it is difficult to calibrate transmission models and ascertain their reliability for several reasons. These include: uncertainty about the key variables (age, household structure, contact rates) essential to characterize transmission of each infection [13,14]; data limitations including variation in reporting rates of infection as a function of age and other variables [15,16], and the need for possibly unrealistic simplifying assumptions in order to make transmission rate parameters identifiable from available data [13]. Therefore, it is desirable to have statistical tools that make use of limited data, usually obtained from detected cases during infectious disease outbreaks, to make conclusions about the role of different population groups in propagating the spread of infection, the impact of vaccination, and other phenomena.…”

Section: Trends Relative To the Stage Of The Epidemic Curvementioning

confidence: 99%

Temporally Varying Relative Risks for Infectious Diseases

et al. 2017

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Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, though they are often modified by other, non-temporal factors. For infectious diseases, in which overall incidence often varies substantially over time, the patterns of temporal changes in relative risks can inform our understanding of basic epidemiologic questions. For example, recent work suggests that temporal changes in relative risks of infection over the course of an epidemic cycle can both be used to identify population groups that drive infectious disease outbreaks, and help elucidate differences in the effect of vaccination against infection (that is relevant to transmission control) compared with its effect against disease episodes (that reflects individual protection). Patterns of change in the in age groups affected over the course of seasonal outbreaks can provide clues to the types of pathogens that could be responsible for diseases for which an infectious cause is suspected. Changing apparent efficacy of vaccines during trials may provide clues to the vaccine's mode of action and/or indicate risk heterogeneity in the trial population. Declining importance of unusual behavioral risk factors may be a signal of increased local transmission of an infection. We review these developments and the related public health implications.

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“…Through the study of theoretical network models, epidemiologists now understand that contact network structure has a profound influence on epidemic dynamics and whether or not control strategies will be effective [8, 11–14]. Yet studying the structure of contact networks empirically through methods such as contact tracing is difficult and costly, meaning we often know little about the structure of contact networks underlying real-world epidemics [15, 16]. …”

Section: Introductionmentioning

confidence: 99%

Phylodynamics on local sexual contact networks

et al. 2017

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Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

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Six challenges in measuring contact networks for use in modelling

Cited by 83 publications

References 36 publications

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Temporally Varying Relative Risks for Infectious Diseases

Phylodynamics on local sexual contact networks

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