Dynamic network prediction

Goyal, Ravi; Gruttola, Victor De

doi:10.1017/nws.2020.24

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Furthermore, the CCM framework has already been expanded to dynamic networks. 65 In addition to addressing the complexities in the PIRC data, there is a need to develop statistical methods in several areas. The first is assessing MCMC algorithms when modeling a large number of network model parameters, which is possible for CCMs and demonstrated in our simulation studies.…”

Section: Discussionmentioning

confidence: 99%

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Estimating contact network properties by integrating multiple data sources associated with infectious diseases

Goyal

Carnegie²,

Slipher

et al. 2023

Statistics in Medicine

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To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS‐CoV‐2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

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Section: Discussionmentioning

confidence: 99%

“…For example, one advantage of the CCM framework is that known biases in selection can be incorporated directly in the formulation of

{\pi}_0\left(\theta \right)

. Furthermore, the CCM framework has already been expanded to dynamic networks 65 …”

Section: Discussionmentioning

confidence: 99%

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

Goyal

Carnegie²,

Slipher

et al. 2023

Statistics in Medicine

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“…In addition, there are computational limits to the size of networks that can be analyzed using BERGMs. A potential future direction is investigating complex features using the PLOS ONE congruence class model (CCM) for networks [30][31][32][33][34]. CCMs form a broad class that includes as special cases such common network models as the Erdős-Re ´nyi-Gilbert and stochastic block models as well as many ERGMs.…”

Section: Plos Onementioning

confidence: 99%

Identification of system-level features in HIV migration within a host

Goyal,

De Gruttola,

Gianella

et al. 2023

PLoS ONE

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Objective Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. Method Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran’s Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. Results We observe strong evidence for bi-directional flow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequent than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. Discussion Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.

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“…However, they note that implementing procedures for approximating a solution to the likelihood is more challenging for larger datasets 26 . Even if these computational issues can be addressed, ERGMs are nonetheless severely limited in the types of generative mechanisms that can be modeled 27 . The primary contribution of our approach is to develop a Bayesian model selection approach for networks that broadens the range of potential mechanisms whose role in generating an observed network can be investigated.…”

Section: Introductionmentioning

confidence: 99%

Investigation of patient‐sharing networks using a Bayesian network model selection approach for congruence class models

Goyal

Gruttola

2021

Statistics in Medicine

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A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdős‐Rényi‐Gilbert model, stochastic block model, and many exponential random graph models. Due to the range of models that can be specified as CCMs, our proposed method is better able to select models consistent with generative mechanisms associated with observed networks than are current approaches. In addition, our approach allows for incorporation of prior information. We illustrate the use of this approach to select among several different proposed mechanisms for the structure of patient‐sharing networks; such networks have been found to be associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type or degree.

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Dynamic network prediction

Cited by 6 publications

References 32 publications

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

Identification of system-level features in HIV migration within a host

Investigation of patient‐sharing networks using a Bayesian network model selection approach for congruence class models

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