Estimating within-household contact networks from egocentric data

Potter, Gail E.; Handcock, Mark S.; Longini, Ira M.; Halloran, M. Elizabeth

doi:10.1214/11-aoas474

Cited by 28 publications

(35 citation statements)

References 36 publications

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“…The development of statistical techniques to infer detailed and realistically complex network models for face-to-face contacts based on available survey data is a relatively new area. Recent work with the multicountry European POLYMOD study, a diary-based survey of contact behavior, has inferred within-household contact networks (Potter et al, 2011a) and age-based mixing matrices (Mossong et al, 2008; Hens et al, 2009b), but we do not yet have a clear picture of the entire contact network, nor a complete understanding of the relevant network structures for epidemic transmission.…”

Section: Introductionmentioning

confidence: 95%

Estimating within-school contact networks to understand influenza transmission

Potter¹,

Handcock²,

Longini³

et al. 2012

Ann. Appl. Stat.

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Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools, and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than non-friends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network. We then compare a targeted antiviral prophylaxis intervention strategy and a grade closure intervention strategy under random mixing and network-based mixing. We find that random mixing overestimates the effect of targeted antiviral prophylaxis on the probability of an epidemic when the probability of transmission in 10 minutes of contact is less than 0.004 and underestimates it when this transmission probability is greater than 0.004. We found the same pattern for the final size of an epidemic, with a threshold transmission probability of 0.005. We also find random mixing overestimates the effect of a grade closure intervention on the probability of an epidemic and final size for all transmission probabilities. Our findings have implications for policy recommendations based on models assuming random mixing, and can inform further development of network-based models.

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Section: Introductionmentioning

confidence: 95%

Estimating within-school contact networks to understand influenza transmission

Potter¹,

Handcock²,

Longini³

et al. 2012

Ann. Appl. Stat.

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“…When we do this, we mean sampling from a social network and not the large body of work dedicated to making inferences about social network structures from more limited data (e.g., Potter Handcock et al 2011). …”

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confidence: 99%

Network Sampling with Memory

Mouw

Verdery

2012

Sociological Methodology

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Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)—the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a “List” mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a “Search” mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS.

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“…Much effort is thus being invested to improve our understanding of real-world contacts between human beings (Ames et al 2011; Potter et al 2011; Oliveira and Gama 2012). Contacts are interactions between individuals which create a network (Table 1).…”

Section: Contact Patternsmentioning

confidence: 99%

Network epidemiology and plant trade networks

Pautasso¹,

Jeger

2014

AoB PLANTS

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Epidemic models in complex networks are helping us better understand infectious disease outbreaks. This review focuses on the application of new developments in network epidemiology to the study and management of plant diseases. The main aspects covered are: 1) surveys of social networks, 2) models and data about human mobility, 3) epidemic models in directed and hierarchical networks, 4) studies of dynamic networks, and 5) spatial epidemic simulations integrating network data. Because of the increasing amounts of traded plant commodities and the associated rise in introduced plant pests and pathogens, network theory has a great potential in plant science.

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Estimating within-household contact networks from egocentric data

Cited by 28 publications

References 36 publications

Estimating within-school contact networks to understand influenza transmission

Estimating within-school contact networks to understand influenza transmission

Network Sampling with Memory

Network epidemiology and plant trade networks

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