Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza

Abstract

Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6-18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.here is a large body of theoretical literature on how social networks and population structures may affect the spread of communicable diseases and hence influence the design of optimal control strategies (1-8). Such work often makes use of detailed data on populations (e.g., demographics in households, schools, and workplaces; mobility and land-use data; contact surveys; or time-use data) but then makes assumptions about how transmission rates change with the type of interaction (e.g., as a function of the setting and the spatial or social distance between individuals, etc.).

Prospective Epidemiological Evaluation of Seasonal Influenza in All Elementary Schoolchildren in Matsumoto City, Japan, in 2014/2015

“…Transmission between a household and a community has also been reported (25), as has influenza spread from a household to a school, particularly during pandemic influenza outbreaks (26). It is postulated that influenza spread among schools is affected more by household transmission than by direct transmission between schools.…”

Influenza A Virus Shedding and Infectivity in Households

“…Determining which age groups contribute the most to transmission is important to inform policy making, for example on school closure. Although infectivity can be assessed from epidemiological studies documenting transmission in human populations, this approach may be expensive and resource consuming [1][2][3]. As a consequence, if a good biomarker of infectivity is available, it might be advantageous simply to study the biomarker in a sample of influenza case patients, without having to follow up contacts of those patients.…”

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

“…For example, collective dynamics from people suffering from obesity and smokers, have been assessed using social network [79]. It has also been used in health crises and epidemic studies such as in the case of severe acute respiratory syndrome, H1N1 influenza, tuberculosis outbreaks [80,81], and more recently muted to track Ebola [82]. The rapid availability of social network data can be effectively combined with pervasive health monitoring, for example assessing the current health status of a patient with their interactions with other individuals and the effects that these ones induce in their health status.…”