Dataset Information

Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection.

ABSTRACT: We aimed to model the incidence of infection with Mycobacterium tuberculosis among adults using data on infection incidence in children, disease prevalence in adults, and social contact patterns. We conducted a cross-sectional face-to-face survey of adults in 2011, enumerating "close" (shared conversation) and "casual" (shared indoor space) social contacts in 16 Zambian communities and 8 South African communities. We modeled the incidence of M. tuberculosis infection in all age groups using these contact patterns, as well as the observed incidence of M. tuberculosis infection in children and the prevalence of tuberculosis disease in adults. A total of 3,528 adults participated in the study. The reported rates of close and casual contact were 4.9 per adult per day (95% confidence interval: 4.6, 5.2) and 10.4 per adult per day (95% confidence interval: 9.3, 11.6), respectively. Rates of close contact were higher for adults in larger households and rural areas. There was preferential mixing of close contacts within age groups and within sexes. The estimated incidence of M. tuberculosis infection in adults was 1.5-6 times higher (2.5%-10% per year) than that in children. More than 50% of infections in men, women, and children were estimated to be due to contact with adult men. We conclude that estimates of infection incidence based on surveys in children might underestimate incidence in adults. Most infections may be due to contact with adult men. Treatment and control of tuberculosis in men is critical to protecting men, women, and children from tuberculosis.

SUBMITTER: Dodd PJ

PROVIDER: S-EPMC4706676 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection.

Dodd Peter J PJ Looker Clare C Plumb Ian D ID Bond Virginia V Schaap Ab A Shanaube Kwame K Muyoyeta Monde M Vynnycky Emilia E Godfrey-Faussett Peter P Corbett Elizabeth L EL Beyers Nulda N Ayles Helen H White Richard G RG

American journal of epidemiology 20151208 2

We aimed to model the incidence of infection with Mycobacterium tuberculosis among adults using data on infection incidence in children, disease prevalence in adults, and social contact patterns. We conducted a cross-sectional face-to-face survey of adults in 2011, enumerating "close" (shared conversation) and "casual" (shared indoor space) social contacts in 16 Zambian communities and 8 South African communities. We modeled the incidence of M. tuberculosis infection in all age groups using thes ...[more]

PMID: 26646292

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Project description:BackgroundCOVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries.MethodsIn this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university students); 23-44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations.FindingsWe focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. 4-14). Moreover, the surge in the number of new cases reported on Feb. 12 and 13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient policies, such as Beijing, and those with strict policies, such as Shenzhen.InterpretationWith an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated.FundingGeneral Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Natural Science Foundation of China.

Project description:Mathematical and statistical models can be used to make predictions of how epidemics may progress in the near future and form a central part of outbreak mitigation and control. Renewal equation based models allow inference of epidemiological parameters from historical data and forecast future epidemic dynamics without requiring complex mechanistic assumptions. However, these models typically ignore interaction between age groups, partly due to challenges in parameterising a time varying interaction matrix. Social contact data collected regularly during the COVID-19 epidemic provide a means to inform interaction between age groups in real-time. We developed an age-specific forecasting framework and applied it to two age-stratified time-series: incidence of SARS-CoV-2 infection, estimated from a national infection and antibody prevalence survey; and, reported cases according to the UK national COVID-19 dashboard. Jointly fitting our model to social contact data from the CoMix study, we inferred a time-varying next generation matrix which we used to project infections and cases in the four weeks following each of 29 forecast dates between October 2020 and November 2021. We evaluated the forecasts using proper scoring rules and compared performance with three other models with alternative data and specifications alongside two naive baseline models. Overall, incorporating age interaction improved forecasts of infections and the CoMix-data-informed model was the best performing model at time horizons between two and four weeks. However, this was not true when forecasting cases. We found that age group interaction was most important for predicting cases in children and older adults. The contact-data-informed models performed best during the winter months of 2020-2021, but performed comparatively poorly in other periods. We highlight challenges regarding the incorporation of contact data in forecasting and offer proposals as to how to extend and adapt our approach, which may lead to more successful forecasts in future.

Dataset Information

Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection.

Publications

Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection.

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