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. 2017 Aug 11;7(1):7974.
doi: 10.1038/s41598-017-08241-1.

Social Contact Patterns Relevant to the Spread of Respiratory Infectious Diseases in Hong Kong

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Free PMC article

Social Contact Patterns Relevant to the Spread of Respiratory Infectious Diseases in Hong Kong

Kathy Leung et al. Sci Rep. .
Free PMC article

Abstract

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
The distribution of the number of reported contacts and total contact duration.
Figure 2
Figure 2
The relation between participants and their contacts. Participants could report the relationship with their contacts in one of the following categories: household members, classmates or schoolmates, workmates, others and unknown. (a) All participants. (b) Participants using paper questionnaires. (c) Participants using online questionnaires.
Figure 3
Figure 3
The proportion of contacts and contact duration involving physical contacts. The proportion of contacts and contact duration (in person hours) that were physical or non-physical by (a,b) duration, (c,d) location, and (e,f) frequency of contacts.
Figure 4
Figure 4
The correlation between contact duration, location and frequency. The correlation between (a) duration and location, (b) duration and frequency, and (c,d) location and frequency of contacts.
Figure 5
Figure 5
Contact matrix of reported contacts consisting of the average number of contacts and mean contact duration per day per participant. (ad) All reported contacts; (eh) Contacts reported in paper questionnaires; (il) Contacts reported in online questionnaires; (mp) Reported contacts weighted by inverse probability of treatment weighting (IPTW) using propensity scores for mode of questionnaire. The original contact matrices {c ij} show the average number of contacts or mean contact person hours in age group i reported by participants from age group j; the symmetrized contact matrices are calculated using {cij^}=cij+cji2.
Figure 6
Figure 6
Smoothed contact matrix of all reported contacts and total contact durations. The smoothed contact matrices are constructed based on all contact data using kernel density estimation with a Gaussian kernel: (a) original number of reported contacts, (b) symmetrized number of reported contacts, (c) original contact person hours and (d) symmetrized contact person hours. The original contact matrices {c ij} show the average number of contacts or mean contact person hours in age group i reported by participants from age group j; the symmetrized contact matrices are calculated using {cij^}=cij+cji2. Inverse probability of treatment weighting (IPTW) was applied with the propensity scores for mode of survey. The bandwidth was optimized by default to estimate normal densities in MATLAB 9.0 and boundary bias were corrected with simple reflection of data.

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References

    1. Beutels P, Shkedy Z, Aerts M, Van Damme P. Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface. Epidemiology and Infection. 2006;134:1158–1166. doi: 10.1017/S0950268806006418. - DOI - PMC - PubMed
    1. McCaw JM, et al. Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks. BMC Infectious Diseases. 2010;10:166. doi: 10.1186/1471-2334-10-166. - DOI - PMC - PubMed
    1. Read JM, Edmunds WJ, Riley S, Lessler J, Cummings DAT. Close encounters of the infectious kind: methods to measure social mixing behaviour. Epidemiology and Infection. 2012;140:2117–2130. doi: 10.1017/S0950268812000842. - DOI - PMC - PubMed
    1. Mossong J, et al. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLOS Medicine. 2008;5:e74. doi: 10.1371/journal.pmed.0050074. - DOI - PMC - PubMed
    1. Danon L, House TA, Read JM, Keeling MJ. Social encounter networks: collective properties and disease transmission. Journal of The Royal Society Interface. 2012;9:2826–2833. doi: 10.1098/rsif.2012.0357. - DOI - PMC - PubMed

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