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. 2019 Mar 20;16(1):6.
doi: 10.1186/s12976-019-0102-8.

Quantifying heterogeneous contact patterns in Japan: a social contact survey

Lankeshwara Munasinghe  1 , Yusuke Asai  1 , Hiroshi Nishiura  2
Affiliations

Quantifying heterogeneous contact patterns in Japan: a social contact survey

Lankeshwara Munasinghe et al. Theor Biol Med Model. .

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.

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Conflict of interest statement

Authors’ information

The authors are experts with an interest in infectious disease epidemiology and also theoretical epidemiology, and the corresponding author is the chairperson and team leader of the Department of Hygiene, Hokkaido University Graduate School of Medicine.

Ethics approval and consent to participate

The purpose of the study was explained to participants and they were ensured that the extent of use of the survey information was limited to the present study. Informed consent was obtained via the internet webpage, and participants had the right to withdraw at any time during the study period. The Medical Ethics Committees at the Graduate School of Medicine, The University of Tokyo approved this study (approval ID: 10478). As for the seroepidemiological data, we used publicly available data in the present study [28]. The datasets had already been fully anonymized and did not include any identifiable information. Thus, ethical approval was not required for the analysis of seroepidemiological data.

Consent for publication

Not applicable – all details relating to participants were de-identified prior to inclusion in this study.

Competing interests

The authors declare that co-author H. Nishiura is the Editor-in-Chief of Theoretical Biology and Medical Modelling. This does not alter the authors’ adherence to all policies of Theoretical Biology and Medical Modelling on sharing data and materials.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Distributions of contact frequency and age of sample population in Japan. a Log-log plot of contact frequency distribution. Logarithm of the proportion of the sample population was taken against the number of contacts (contact frequency) per day. b Age distribution of the study sample (bars) by age group and sex. Dashed lines represent the age distributions of the entire population of Japan as of 1 November 2016, overlaid with the sample population
Fig. 2
Fig. 2
Contact matrix on weekdays and weekend days. a Average weekday and b weekend contact rate with discrete gradations. Age-dependent contact heterogeneity is approximately captured by these matrices. The number in each cell represents the contact rate per person
Fig. 3
Fig. 3
Contact matrix within the household and in the community (weekday contact). Colour bars indicate the mean number of contacts. a Non-household contact matrix represents the estimated mean number of contacts per day between respondents (i.e., survey participant) and persons other than their household members. b Household contact matrix represents the estimated mean number of contacts per day between respondents (i.e., survey participants) and their household members. Household is defined as the same unit of living space, and household members are the individuals who share that living space, regardless of blood relationship
Fig. 4
Fig. 4
Comparison between observed and estimated age-specific proportions of infected individuals during 2009 influenza A (H1N1) pandemic. Age-specific proportions of infection, or the so-called population attack rate or final size, during the 2009 influenza A (H1N1) pandemic, illustrated by age. Estimates were obtained by imposing various assumptions of age-dependent contact patterns, including homogeneous (or random) mixing, separable mixing (i.e., contributions of contactor and contactee are separable), age-independent susceptibility (i.e., the contact matrix was used, but the entire next-generation matrix was assumed proportional to that matrix), and age-dependent susceptibility (i.e., contact matrix plus age-dependent susceptibility per contact were estimated)
Fig. 5
Fig. 5
Age-dependent relative susceptibility against the 2009 influenza A (H1N1). Maximum likelihood estimates of the age-dependent relative susceptibility are shown, taking the age group 0–4 years as the reference group with the value 1.0. Dashed lines represent lower and upper 95% confidence intervals derived from the profile likelihood
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