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Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.
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Learning socio-organizational network structure in buildings with ambient sensing data
Impact Statement The structure of social and organizational relationships in commercial building workplaces is a key component of work processes. Understanding this structure—typically described as a network of relational ties—can help designers of workspaces and managers of workplaces make decisions that promote the success of organizations. These networks are complex, and as a result, our traditional means of measuring them are time and cost intensive. In this paper, we present a novel method, the Interaction Model, for learning these network structures automatically through sensing data. When we compare the learned network to network data obtained through a survey, we find statistically significant correlation, demonstrating the success of our method. Two key strengths of our proposed method are, first, that it uncovers network patterns quickly, requiring just 10 weeks of data, and, second, that it is interpretable, relying on intuitive opportunities for social interaction. Data-driven inference of the structure of human systems within our built environment will enable the design and operation of engineered built spaces that promote our human-centered objectives.
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- PAR ID:
- 10294630
- Date Published:
- Journal Name:
- Data-Centric Engineering
- Volume:
- 1
- ISSN:
- 2632-6736
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/dce.2020.9
