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Quantifying heterogeneous contact patterns in Japan: a social contact survey

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  • Quantifying heterogeneous contact patterns in Japan: a social contact survey

    Theor Biol Med Model. 2019 Mar 20;16(1):6. doi: 10.1186/s12976-019-0102-8.
    Quantifying heterogeneous contact patterns in Japan: a social contact survey.

    Munasinghe L1, Asai Y1, Nishiura H2.
    Author information

    Abstract

    BACKGROUND:

    Social contact surveys can greatly help in quantifying the heterogeneous patterns of infectious disease transmission. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza.
    METHODS:

    An internet-based questionnaire survey was conducted, covering all 47 prefectures in Japan and including a total of 1476 households. The social contact matrix was quantified assuming reciprocity and using the maximum likelihood method. By imposing several parametric assumptions for the next-generation matrix, the empirical seroepidemiological data of influenza A (H1N1) 2009 was analysed and we estimated the basic reproduction number, R0.
    RESULTS:

    In total, the reported number of contacts on weekdays was 10,682 whereas that on weekend days was 8867. Strong age-dependent assortativity was identified. Forty percent of weekday contacts took place at schools or workplaces, but that declined to 14% on weekends. Accounting for the age-dependent heterogeneity with the known social contact matrix, the minimum value of the Akaike information criterion was obtained and R0 was estimated at 1.45 (95% confidence interval: 1.42, 1.49).
    CONCLUSIONS:

    Survey datasets will be useful for parameterizing the heterogeneous transmission model of various directly transmitted infectious diseases in Japan. Age-dependent assortativity, especially among children, along with numerous contacts in school settings on weekdays implies the potential effectiveness of school closure.


    KEYWORDS:

    Cumulative incidence; Epidemic; Epidemiological model; Influenza; Mathematical model

    PMID: 30890153 DOI: 10.1186/s12976-019-0102-8
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