Skip to main content Accessibility help
×
Home

Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission

  • M. STRÖMGREN (a1), E. HOLM (a1), Ö. DAHLSTRÖM (a2), J. EKBERG (a3) (a4), H. ERIKSSON (a5), A. SPRECO (a3) (a5) and T. TIMPKA (a3) (a4) (a5)...

Summary

This study aims to develop a typology of generic meeting places based on social contact and mixing of relevance for infectious disease transmission. Data were collected by means of a contact diary survey conducted on a representative sample of the Swedish population. The typology is derived from a cluster analysis accounting for four dimensions associated with transmission risk: visit propensity and its characteristics in terms of duration, number of other persons present and likelihood of physical contact. In the analysis, we also study demographic, socio-economic and geographical differences in the propensity of visiting meeting places. The typology identifies the family venue, the fixed activity site, the family vehicle, the trading plaza and the social network hub as generic meeting places. The meeting place typology represents a spatially explicit account of social contact and mixing relevant to infectious disease modelling, where the social context of the outbreak can be highlighted in light of the actual infectious disease.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission
      Available formats
      ×

Copyright

Corresponding author

*Author for correspondence: M. Strömgren, Department of Geography and Economic History, Umeå University, 90187 Umeå, Sweden. (E-mail: magnus.stromgren@umu.se)

References

Hide All
1. Cliff, A, Haggett, P. Time, travel and infection. British Medical Bulletin 2004; 69: 8799.
2. Colizza, V, et al. 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 2006; 103: 20152020.
3. Blower, SM, et al. The intrinsic transmission dynamics of tuberculosis epidemics. Nature Medicine 1995; 1: 815821.
4. Vynnycky, E, Fine, PEM. The annual risk of infection with Mycobacterium tuberculosis in England and Wales since 1901. The International Journal of Tuberculosis and Lung Disease 1997; 1: 389396.
5. Dye, C, Williams, BG. Eliminating human tuberculosis in the twenty-first century. Journal of the Royal Society Interface 2008; 5: 653662.
6. Abu-Raddad, LJ, et al. Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics. Proceedings of the National Academy of Sciences of the United States of America 2009; 106: 1398013985.
7. Longini, IM, et al. Simulation studies of influenza epidemics: assessment of parameter estimation and sensitivity. International Journal of Epidemiology 1984; 13: 496501.
8. Glass, RJ, et al. Targeted social distancing design for pandemic influenza. Emerging Infectious Diseases 2006; 12: 16711681.
9. Wu, JT, et al. Reducing the impact of the next influenza pandemic using household-based public health interventions. PLoS Medicine 2006; 3: e361.
10. Halloran, ME, et al. Modeling targeted layered containment of an influenza pandemic in the United States. Proceedings of the National Academy of Sciences of the United States of America 2008; 105: 46394644.
11. Nsoesie, EO, et al. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza and Other Respiratory Viruses 2014; 8: 309316.
12. Timpka, T, et al. Population-based simulations of influenza pandemics: validity and significance for public health policy. Bulletin of the World Health Organization 2009; 87: 305311.
13. Janes, CR, et al. Emerging infectious diseases: the role of social sciences. Lancet 2012; 380: 18841886.
14. Hägerstrand, T. Innovation Diffusion as a Spatial Process. Chicago, IL: University of Chicago Press, 1967.
15. Hudson, JC. Diffusion in a central place system. Geographical Analysis 1969; 1: 4558.
16. Eyler, JM. The changing assessments of John Snow's and William Farr's cholera studies. Sozial- und Präventivmedizin 2001; 46: 225232.
17. Mossong, J, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine 2008; 5: e74.
18. Kwok, KO, et al. Social contacts and the locations in which they occur as risk factors for influenza infection. Proceedings of the Royal Society of London B: Biological Sciences 2014; 281: 20140709.
19. Sayer, LC. Gender, time and inequality: trends in women's and men's paid work, unpaid work and free time. Social Forces 2005; 84: 285303.
20. Danon, L, et al. Social encounter networks: characterizing Great Britain. Proceedings of the Royal Society of London B: Biological Sciences 2013; 280: 20131037.
21. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum, 1988.
22. Haugen, K, et al. Proximity, accessibility and choice: a matter of taste or condition? Papers in Regional Science 2012; 91: 6584.
23. Statistics Sweden (http://www.scb.se/statistik/_publikationer/OV9999_2011A01_BR_X20BR1101.pdf). Accessed 26 April 2017.
24. Brown, LD, Cai, TT, Dasgupta, A. Confidence intervals for a binomial proportion and asymptotic expansions. Annals of Statistics 2002; 30: 160201.
25. Romesburg, C. Cluster Analysis for Researchers. Raleigh, NC: Lulu Press, 2004.
26. Ajelli, M, Litvinova, M. Estimating contact patterns relevant to the spread of infectious diseases in Russia. Journal of Theoretical Biology 2017; 419: 17.
27. Merler, S, Ajelli, M. Deciphering the relative weights of demographic transition and vaccination in the decrease of measles incidence in Italy. Proceedings of the Royal Society of London B: Biological Sciences 2014; 281: 20132676.
28. Polanyi, K. The economy as instituted process. In: Polanyi, K, Arensberg, CM, Pearson, HW, eds. Trade and Market in the Early Empires: Economies in History and Theory. Glencoe, IL: Free Press, 1957, pp. 243269.
29 Kretzschmar, M, Mikolajczyk, RT. Contact profiles in eight European countries and implications for modelling the spread of airborne infectious diseases. PLoS ONE 2009; 4: e5931.
30. Elveback, LR, et al. An influenza simulation model for immunization studies. American Journal of Epidemiology 1976; 103: 152165.
31. Longini, IM, et al. Containing pandemic influenza with antiviral agents. American Journal of Epidemiology 2004; 159: 623633.
32. Halloran, ME, et al. Community interventions and the epidemic prevention potential. Vaccine 2002; 20: 32543262.
33. Potter, GE, et al. Estimating within-school contact networks to understand influenza transmission. Annals of Applied Statistics 2012; 6: 126.
34. Read, JM, Eames, KT, Edmunds, WJ. Dynamic social networks and the implications for the spread of infectious disease. Journal of the Royal Society Interface 2008; 5: 10011007.
35. Zagheni, E, et al. Using time-use data to parameterize models for the spread of close-contact infectious diseases. American Journal of Epidemiology 2008; 168: 10821090.
36. Iozzi, F, et al. Little Italy: an agent-based approach to the estimation of contact patterns-fitting predicted matrices to serological data. PLoS Computational Biology 2010; 6: e1001021.
37. Fumanelli, L, et al. Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread. PLoS Computational Biology 2012; 8: e1002673.
38. Tang, JW, et al. Aerosol-transmitted infections: a new consideration for public health and infection control teams. Current Treatment Options in Infectious Diseases 2015; 7: 126.
39. Frieden, TR, et al. Ebola 2014: new challenges, new global response and responsibility. New England Journal of Medicine 2014; 371: 11771180.
40. Moore, J, Carrasco, JA, Tudela, A. Exploring the links between personal networks, time use, and the spatial distribution of social contacts. Transportation 2013; 40: 773788.
41. Bolton, KJ, et al. Influence of contact definitions in assessment of the relative importance of social settings in disease transmission risk. PLoS ONE 2012; 7: e30893.
42. Read, JM, et al. Close encounters of the infectious kind: methods to measure social mixing behaviour. Epidemiology and Infection 2004; 140: 21172130.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed