6. Exponential Family Models for Sampled and Census Network Data

Koehly, Laura M.; Goodreau, Steven M.; Morris, Martina

doi:10.1111/j.0081-1750.2004.00153.x

Cited by 48 publications

(23 citation statements)

References 54 publications

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“…For instance, small worlds (networks characterized by bridges joining otherwise disjoint clusters) can lead to thresholds and rapid disease diffusion to distant subpopulations (Watts and Strogatz 1998; Watts 1999); skewed degree distributions (networks containing individuals with a relatively very high number of partners), can result in epidemics driven by promiscuous individuals (Liljeros et al 2001; for a critical perspective, see Jones and Handcock 2003b; Handcock and Jones 2004). While sophisticated analytic methods have recently become available that allow investigations of networks that deviated from the assumptions of the classical epidemiological model (Koehly, Goodreau, and Morris 2004), their application to context of high HIV-prevalence in SSA has been hampered by a lack of suitable data on the rate of contact between susceptible and infectious individuals ( c̄ ).…”

Section: Background: Network Epidemiologymentioning

confidence: 99%

The Likoma Network Study: Context, data collection and initial results

Helleringer¹,

Köhler²,

Chimbiri³

et al. 2009

DemRes

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The extent and structure of sexual networks have important consequences for the spread of sexually transmitted diseases such as HIV. However, very few datasets currently exist that allow a detailed investigation of sexual networks in sub-Saharan African settings where HIV epidemics have become generalized. In this paper, we describe the context and methods of the Likoma Network Study (LNS), one of the few studies that have collected extensive information on sexual networks in sub-Saharan Africa. We start by reviewing theoretical arguments and empirical studies emphasizing the importance of network structures in the epidemiology of HIV and other sexually transmitted infections (STI). The island setting of this study is described, and we argue that the choice of an island as a research site limited potential biases that may make the collection of sexual network data difficult. We then document our empirical strategy for the collection of sexual network data and the subsequent identification of sexual network partners. A description of the protocol for the collection of biomarker data (HIV infection) is provided. Finally, we present initial results relating to the socioeconomic context of the island, the size and composition of sexual networks, the quality of the sexual network data, the determinants of successful contact tracing during the LNS, and the prevalence of HIV in the study population.

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Section: Background: Network Epidemiologymentioning

confidence: 99%

The Likoma Network Study: Context, data collection and initial results

Helleringer¹,

Köhler²,

Chimbiri³

et al. 2009

DemRes

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“…We take the latter approach in this paper. Koehly, Goodreau, and Morris (2004) discuss the use of conditional log-linear models to analyze egocentric data. Handcock and Gile (2007, 2010) develop a conceptual framework for inference of network parameters from sampled data under a variety of sampling designs.…”

Section: Introductionmentioning

confidence: 99%

Estimating within-household contact networks from egocentric data

Potter¹,

Handcock²,

Longini³

et al. 2011

Ann. Appl. Stat.

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Acute respiratory diseases are transmitted over networks of social contacts. Large-scale simulation models are used to predict epidemic dynamics and evaluate the impact of various interventions, but the contact behavior in these models is based on simplistic and strong assumptions which are not informed by survey data. These assumptions are also used for estimating transmission measures such as the basic reproductive number and secondary attack rates. Development of methodology to infer contact networks from survey data could improve these models and estimation methods. We contribute to this area by developing a model of within-household social contacts and using it to analyze the Belgian POLYMOD data set, which contains detailed diaries of social contacts in a 24-hour period. We model dependency in contact behavior through a latent variable indicating which household members are at home. We estimate age-specific probabilities of being at home and age-specific probabilities of contact conditional on two members being at home. Our results differ from the standard random mixing assumption. In addition, we find that the probability that all members contact each other on a given day is fairly low: 0.49 for households with two 0–5 year olds and two 19–35 year olds, and 0.36 for households with two 12–18 year olds and two 36+ year olds. We find higher contact rates in households with 2–3 members, helping explain the higher influenza secondary attack rates found in households of this size.

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“…In other cases, one may encounter unbalanced numbers when using egocentric data for a variety of reasons (e.g., sampling, reporting errors), even though any bounded empirical population with two modes would in theory have to balance if a census were taken. When faced with this, network modelers must consider how they wish to address this (e.g., by averaging the two modes’ mean degree somehow) before proceeding (see Morris 1991; Koehly, Goodreau, and Morris 2004 for examples).…”

Section: Base Modelsmentioning

confidence: 99%

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

Jenness¹,

Goodreau²,

Morris³

2018

J. Stat. Soft.

Self Cite

143

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Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers.

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6. Exponential Family Models for Sampled and Census Network Data

Cited by 48 publications

References 54 publications

The Likoma Network Study: Context, data collection and initial results

The Likoma Network Study: Context, data collection and initial results

Estimating within-household contact networks from egocentric data

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

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