publication . Preprint . 2015

Physical Proximity and Spreading in Dynamic Social Networks

Stopczynski, Arkadiusz; Pentland, Alex Sandy; Lehmann, Sune;
Open Access English
  • Published: 22 Sep 2015
Abstract
Most infectious diseases spread on a dynamic network of human interactions. Recent studies of social dynamics have provided evidence that spreading patterns may depend strongly on detailed micro-dynamics of the social system. We have recorded every single interaction within a large population, mapping out---for the first time at scale---the complete proximity network for a densely-connected system. Here we show the striking impact of interaction-distance on the network structure and dynamics of spreading processes. We create networks supporting close (intimate network, up to ~1m) and longer distance (ambient network, up to ~10m) modes of transmission. The intima...
Subjects
free text keywords: Physics - Physics and Society, Computer Science - Social and Information Networks
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32 references, page 1 of 3

1. Salathe´, M. et al. A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences 107, 22020-22025 (2010). [OpenAIRE]

2. Brankston, G., Gitterman, L., Hirji, Z., Lemieux, C. & Gardam, M. Transmission of influenza a in human beings. The Lancet infectious diseases 7, 257-265 (2007). [OpenAIRE]

3. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in southeast asia. Nature 437, 209-214 (2005).

4. Stehle´, J. et al. Simulation of an seir infectious disease model on the dynamic contact network of conference attendees. BMC medicine 9, 87 (2011).

5. Rocha, L. E., Liljeros, F. & Holme, P. Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS computational biology 7, e1001109 (2011).

6. Vanhems, P. et al. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS one 8, e73970 (2013). [OpenAIRE]

7. Holme, P. & Liljeros, F. Birth and death of links control disease spreading in empirical contact networks. Scientific reports 4 (2014). [OpenAIRE]

8. Balcan, D. et al. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106, 21484-21489 (2009). [OpenAIRE]

9. Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439, 462-465 (2006). [OpenAIRE]

10. Hufnagel, L., Brockmann, D. & Geisel, T. Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the United States of America 101, 15124-15129 (2004). [OpenAIRE]

11. Colizza, V., Barrat, A., Barthe´lemy, M. & Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America 103, 2015-2020 (2006). [OpenAIRE]

12. Wesolowski, A. et al. Quantifying the impact of human mobility on malaria. Science 338, 267-270 (2012). [OpenAIRE]

13. Onnela, J.-P. et al. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences 104, 7332-7336 (2007). [OpenAIRE]

14. Stopczynski, A. et al. Measuring large-scale social networks with high resolution. PLoS ONE 9, e95978 (2014). URL http://dx.doi.org/10.1371/journal.pone.0095978. [OpenAIRE]

15. Sekara, V. & Lehmann, S. The strength of friendship ties in proximity sensor data. PloS one 9, e100915 (2014).

32 references, page 1 of 3
Abstract
Most infectious diseases spread on a dynamic network of human interactions. Recent studies of social dynamics have provided evidence that spreading patterns may depend strongly on detailed micro-dynamics of the social system. We have recorded every single interaction within a large population, mapping out---for the first time at scale---the complete proximity network for a densely-connected system. Here we show the striking impact of interaction-distance on the network structure and dynamics of spreading processes. We create networks supporting close (intimate network, up to ~1m) and longer distance (ambient network, up to ~10m) modes of transmission. The intima...
Subjects
free text keywords: Physics - Physics and Society, Computer Science - Social and Information Networks
Download from
32 references, page 1 of 3

1. Salathe´, M. et al. A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences 107, 22020-22025 (2010). [OpenAIRE]

2. Brankston, G., Gitterman, L., Hirji, Z., Lemieux, C. & Gardam, M. Transmission of influenza a in human beings. The Lancet infectious diseases 7, 257-265 (2007). [OpenAIRE]

3. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in southeast asia. Nature 437, 209-214 (2005).

4. Stehle´, J. et al. Simulation of an seir infectious disease model on the dynamic contact network of conference attendees. BMC medicine 9, 87 (2011).

5. Rocha, L. E., Liljeros, F. & Holme, P. Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS computational biology 7, e1001109 (2011).

6. Vanhems, P. et al. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS one 8, e73970 (2013). [OpenAIRE]

7. Holme, P. & Liljeros, F. Birth and death of links control disease spreading in empirical contact networks. Scientific reports 4 (2014). [OpenAIRE]

8. Balcan, D. et al. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106, 21484-21489 (2009). [OpenAIRE]

9. Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439, 462-465 (2006). [OpenAIRE]

10. Hufnagel, L., Brockmann, D. & Geisel, T. Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the United States of America 101, 15124-15129 (2004). [OpenAIRE]

11. Colizza, V., Barrat, A., Barthe´lemy, M. & Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America 103, 2015-2020 (2006). [OpenAIRE]

12. Wesolowski, A. et al. Quantifying the impact of human mobility on malaria. Science 338, 267-270 (2012). [OpenAIRE]

13. Onnela, J.-P. et al. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences 104, 7332-7336 (2007). [OpenAIRE]

14. Stopczynski, A. et al. Measuring large-scale social networks with high resolution. PLoS ONE 9, e95978 (2014). URL http://dx.doi.org/10.1371/journal.pone.0095978. [OpenAIRE]

15. Sekara, V. & Lehmann, S. The strength of friendship ties in proximity sensor data. PloS one 9, e100915 (2014).

32 references, page 1 of 3
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