Social Networks and Transmission and Control of Influenza
Social Networks and Transmission and Control of Influenza
We expect social networks to change as a result of illness, but social contact data are generally collected from healthy persons. Here we quantified the impact of influenza-like illness on social mixing patterns. We analyzed the contact patterns of persons from England measured when they were symptomatic with influenza-like illness during the 2009 A/H1N1pdm influenza epidemic (2009–2010) and again 2 weeks later when they had recovered. Illness was associated with a reduction in the number of social contacts, particularly in settings outside the home, reducing the reproduction number to about one-quarter of the value it would otherwise have taken. We also observed a change in the age distribution of contacts. By comparing the expected age distribution of cases resulting from transmission by (a)symptomatic persons with incidence data, we estimated the contribution of both groups to transmission. Using this, we calculated the fraction of transmission resulting from (a)symptomatic persons, assuming equal duration of infectiousness. We estimated that 66% of transmission was attributable to persons with symptomatic disease (95% confidence interval: 0.23, 1.00). This has important implications for control: Treating symptomatic persons with antiviral agents or encouraging home isolation would be expected to have a major impact on transmission, particularly since the reproduction number for this strain was low.
Knowledge of social networks is vital when seeking to understand and predict the spread of infectious diseases in human populations. In recent years, concerted efforts have been made to characterize patterns of social mixing within communities using electronic motes, mobile phones, or diaries of self-reported contacts. As a result, it is now common practice for mathematical models of disease spread to incorporate mixing data to describe interactions between different population subgroups. However, at present, most models assume that contact patterns remain constant over time. In reality, it is clear that patterns of contact are changeable. For example, social behavior differs between weekdays and weekends and between school terms and holiday periods. These regular temporal variations in contact patterns can be appropriately included in epidemiologic models, given the increasing availability of data describing them. However, there remains a critical gap in our current knowledge—namely, the impact of illness on social contact patterns. If, as might be expected, people modify their behavior when they are ill, the value to mathematical models of social mixing data collected predominantly from healthy persons is questionable. If illness results in people's taking time off from work or school, avoiding social gatherings, or changing their social behavior in other ways, the behavior of ill persons would be poorly described by commonly collected social contact data. If asymptomatic infections are common or if transmission takes place before symptoms appear, then possible changes in social behavior are less important.
During the 2009 influenza pandemic, we asked persons from England to record their social contacts when they were symptomatic with influenza-like illness (ILI), and again 2 weeks later after they had recovered. We analyzed these data, and here we demonstrate that not only does the number of social contacts change when a person is ill, so does the distribution of contacts across age groups. We show that this has a major impact on the basic reproduction number (R0) and the expected age distribution of cases in the population. It is suspected that the 2009 A/H1N1pdm epidemic resulted in a large number of infections that displayed no symptoms or mild symptoms. Both serological data and modeling work indicated that patients with ILI who sought medical attention were a small fraction of the total number of persons with infections in the United Kingdom. By comparing the observed age distribution of cases with that predicted by the measured contact patterns of asymptomatic and symptomatic persons, we were able to address one of the more intractable problems of influenza epidemiology: What contribution do asymptomatic persons make to overall influenza transmission? The findings have important implications for public health, since the proportion of transmission resulting from symptomatic persons determines how effective treatment with antiviral agents or home isolation will be in limiting spread of the disease.
Abstract and Introduction
Abstract
We expect social networks to change as a result of illness, but social contact data are generally collected from healthy persons. Here we quantified the impact of influenza-like illness on social mixing patterns. We analyzed the contact patterns of persons from England measured when they were symptomatic with influenza-like illness during the 2009 A/H1N1pdm influenza epidemic (2009–2010) and again 2 weeks later when they had recovered. Illness was associated with a reduction in the number of social contacts, particularly in settings outside the home, reducing the reproduction number to about one-quarter of the value it would otherwise have taken. We also observed a change in the age distribution of contacts. By comparing the expected age distribution of cases resulting from transmission by (a)symptomatic persons with incidence data, we estimated the contribution of both groups to transmission. Using this, we calculated the fraction of transmission resulting from (a)symptomatic persons, assuming equal duration of infectiousness. We estimated that 66% of transmission was attributable to persons with symptomatic disease (95% confidence interval: 0.23, 1.00). This has important implications for control: Treating symptomatic persons with antiviral agents or encouraging home isolation would be expected to have a major impact on transmission, particularly since the reproduction number for this strain was low.
Introduction
Knowledge of social networks is vital when seeking to understand and predict the spread of infectious diseases in human populations. In recent years, concerted efforts have been made to characterize patterns of social mixing within communities using electronic motes, mobile phones, or diaries of self-reported contacts. As a result, it is now common practice for mathematical models of disease spread to incorporate mixing data to describe interactions between different population subgroups. However, at present, most models assume that contact patterns remain constant over time. In reality, it is clear that patterns of contact are changeable. For example, social behavior differs between weekdays and weekends and between school terms and holiday periods. These regular temporal variations in contact patterns can be appropriately included in epidemiologic models, given the increasing availability of data describing them. However, there remains a critical gap in our current knowledge—namely, the impact of illness on social contact patterns. If, as might be expected, people modify their behavior when they are ill, the value to mathematical models of social mixing data collected predominantly from healthy persons is questionable. If illness results in people's taking time off from work or school, avoiding social gatherings, or changing their social behavior in other ways, the behavior of ill persons would be poorly described by commonly collected social contact data. If asymptomatic infections are common or if transmission takes place before symptoms appear, then possible changes in social behavior are less important.
During the 2009 influenza pandemic, we asked persons from England to record their social contacts when they were symptomatic with influenza-like illness (ILI), and again 2 weeks later after they had recovered. We analyzed these data, and here we demonstrate that not only does the number of social contacts change when a person is ill, so does the distribution of contacts across age groups. We show that this has a major impact on the basic reproduction number (R0) and the expected age distribution of cases in the population. It is suspected that the 2009 A/H1N1pdm epidemic resulted in a large number of infections that displayed no symptoms or mild symptoms. Both serological data and modeling work indicated that patients with ILI who sought medical attention were a small fraction of the total number of persons with infections in the United Kingdom. By comparing the observed age distribution of cases with that predicted by the measured contact patterns of asymptomatic and symptomatic persons, we were able to address one of the more intractable problems of influenza epidemiology: What contribution do asymptomatic persons make to overall influenza transmission? The findings have important implications for public health, since the proportion of transmission resulting from symptomatic persons determines how effective treatment with antiviral agents or home isolation will be in limiting spread of the disease.
