A practical method to target individuals for outbreak detection and control

Abstract

Identification of individuals or subpopulations that contribute the most to disease transmission is key to target surveillance and control efforts. In a recent study in BMC Medicine, Smieszek and Salathé introduced a novel method based on readily available information about spatial proximity in high schools, to help identify individuals at higher risk of infection and those more likely to be infected early in the outbreak. By combining simulation models for influenza transmission with high-resolution data on school contact patterns, the authors showed that their proximity method compares favorably to more sophisticated methods using detailed contact tracing information. The proximity method is simple and promising, but further research is warranted to confront this method against real influenza outbreak data, and to assess the generalizability of the approach to other important transmission units, such as work, households, and transportation systems.See related research article here http://www.biomedcentral.com/1741-7015/11/35

Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers

“…However, the linker behavior, contrary to the betweenness centrality criterion, may be more easily linked to recognizable human behavior or to individual attributes in the organizational chart, for instance in relation to professional grade or specific activities. In this case, one could a priori discern which individuals are more susceptible to be linkers and play an important role in the event of an outbreak, and therefore use such limited information to design an efficient vaccination strategy entailing only a low cost in terms of necessary information (Smieszek & Salathé, 2013;Chowell & Viboud, 2013). We final note that the linker behavior might also be identified from limited information in other social contexts with communities -schools, hospitals, etc -and provide an important ingredient in agent-based models of epidemic spreading phenomena, as such agents provide crucial gateways between communities.…”

Compensating for population sampling in simulations of epidemic spread on temporal contact networks

“…The understanding of epidemic spreading phenomena has been vastly improved thanks to the use of data-driven models at different scales. High resolution contact data in particular have been used to evaluate epidemic risk or containment policies in specific populations or to perform contact tracing [14,19,20,28,[30][31][32]. In such studies, missing data due to population sampling might represent however a serious issue: individuals absent from a data set are indeed equivalent to immunised individuals when epidemic processes are simulated.…”

“…In the recent years, several data gathering efforts have used such methods to obtain, analyse and publish data sets describing the contact patterns between individuals in various contexts in the form of temporal networks [14,[20][21][22][23][24]: nodes represent individuals and, at each time step, a link is drawn between pairs of individuals who are in contact [25]. Such data has been used to inform models of epidemic spreading phenomena used to evaluate epidemic risks and mitigation strategies in specific, size-limited contexts such as schools or hospitals [14,19,20,22,[26][27][28][29][30][31][32], finding in particular outcomes consistent with observed outbreak data [20] or providing evidence of links between specific contacts and transmission events [19,31].Despite the relevance and interest of such detailed data sets, as illustrated by these recent investigations, they suffer from the intrinsic limitation of the data gathering method: contacts are registered only between participants wearing sensors. Contacts with and between individuals who do not wear sensors are thus missed.…”

Disease Surveillance on Complex Social Networks

“…In general, low R 0 implies a longer lag time between epidemiological events in the surveillance group and corresponding events in the general population, and a larger discrepancy between prevalence in the surveillance group and overall epidemiological activity. Several recent studies have identified epidemiologically relevant measures of centrality that can be estimated from readily obtainable school, social network, and workplace data [ 42 , 43 , 47 , 48 ]. We hypothesize that these more tractable centrality-based sensors may exhibit a similar trade off between timeliness and robustness.…”

“…Rapidity of targeted action during the initial phase of an outbreak is fundamental to the effectively curtailing transmission and minimizing disease burden. In previous work on livestock diseases, a network path based strategy has been proposed for identifying surveillance locations that would provide timely and accurate outbreak data [ 40 ]; in a recent analysis of disease surveillance in a high school population, Smieszek and Salathé introduce a promising sensor selection criteria (total time students spend collocated with other students) that is expected to yield timelier and more accurate information than alternative centrality-based criteria [ 47 , 48 ]. Christakis et al performed an experimental comparison of two social-network-based strategies in a college population [ 46 ].…”