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Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.

Potter GE, Smieszek T, Sailer K - Netw Sci (Camb Univ Press) (2015)

Bottom Line: We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns.Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions.Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

View Article: PubMed Central - PubMed

Affiliation: California Polytechnic State University, San Luis Obispo, CA, USA; Center for Statistics and Quantitative Infectious Disease, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

ABSTRACT

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

No MeSH data available.


Related in: MedlinePlus

Mean final size (minus index case) by transmission probability per minute of contact and for different contact networks, based on simulations. “Original” refers to the empirically measured network with reporting inconsistencies resolved; “Full model” refers to the model in Table 6; “Shuffled edges” are network models with the same density and duration distribution as the full model, but with randomly allocated edges; “Random mixing” is a random mixing scenario.
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Figure 4: Mean final size (minus index case) by transmission probability per minute of contact and for different contact networks, based on simulations. “Original” refers to the empirically measured network with reporting inconsistencies resolved; “Full model” refers to the model in Table 6; “Shuffled edges” are network models with the same density and duration distribution as the full model, but with randomly allocated edges; “Random mixing” is a random mixing scenario.

Mentions: Figures 3, 4, and 5 show the mean final size for epidemic simulations based on various models. We estimated 95% confidence intervals for the mean final outbreak size using a nonparametric bootstrap (1,000 bootstrap resamples were drawn), but these were so narrow that we omit them from the graphs.


Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.

Potter GE, Smieszek T, Sailer K - Netw Sci (Camb Univ Press) (2015)

Mean final size (minus index case) by transmission probability per minute of contact and for different contact networks, based on simulations. “Original” refers to the empirically measured network with reporting inconsistencies resolved; “Full model” refers to the model in Table 6; “Shuffled edges” are network models with the same density and duration distribution as the full model, but with randomly allocated edges; “Random mixing” is a random mixing scenario.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663701&req=5

Figure 4: Mean final size (minus index case) by transmission probability per minute of contact and for different contact networks, based on simulations. “Original” refers to the empirically measured network with reporting inconsistencies resolved; “Full model” refers to the model in Table 6; “Shuffled edges” are network models with the same density and duration distribution as the full model, but with randomly allocated edges; “Random mixing” is a random mixing scenario.
Mentions: Figures 3, 4, and 5 show the mean final size for epidemic simulations based on various models. We estimated 95% confidence intervals for the mean final outbreak size using a nonparametric bootstrap (1,000 bootstrap resamples were drawn), but these were so narrow that we omit them from the graphs.

Bottom Line: We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns.Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions.Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

View Article: PubMed Central - PubMed

Affiliation: California Polytechnic State University, San Luis Obispo, CA, USA; Center for Statistics and Quantitative Infectious Disease, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

ABSTRACT

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

No MeSH data available.


Related in: MedlinePlus