Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

Abstract

BackgroundThe spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population.MethodsWe considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects.ResultsWe show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic.ConclusionsThese results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics.Please see related article BMC Medicine, 2011, 9:88

Temporal networks

“…The spreading of biological viruses depends on temporal patterns of contact, and the flow of information in social networks is influenced by the social activity patterns and rhythms of individuals. Importantly, there is an increasing body of evidence of very large heterogeneity in the timings of such interactions, from burstiness in patterns of communication via electronic and physical mail [7,39,61,69,115,157], mobile telephone calls and text messages [21,71,107,162], instant messaging [95], and patterns in proximity dynamics measured with RFID sensors [24,64,141]. Daily and circadian rhythms of human dynamics [58,67,94,102,103] may also play a role.…”

“…Another type of largescale proximity data comes from hospitals where contacts between two patients that have been admitted to the same ward at the same time are recorded, sometimes including the medical staff [99,154]. Such data is important for studying the dynamics of disease outbreaks [64,141] (such as MRSA) in hospitals and also protocols for wireless ad hoc communication [121]. Similarly, for livestock disease, it is important to know the movement of animals between farms etc.…”

“…The SocioPatterns project has developed a platform that allows physical proximity measurements based on wearable badges equipped with radiofrequency identification devices (RFID) [24]; these devices have been utilized in measurements of dynamic and temporal proximity networks of patients [63], school children [142], and conference attendees [140]. Because the human body acts as a shield for the proximity-sensing RF signals, such sensors only record contacts when the individuals are facing each other, and thus a contact can also be considered as indicative of communication between the individuals [24,64,121,141]. Recording of face-to-face communication events has also been realized with infrared sensor devices [145].…”

Epidemics on a Stochastic Model of Temporal Network

“…It has been observed in different contexts that vertices are not active uniformly in time but often follow different patterns, i.e., the inter-event time T V between two vertex activations is not uniform but follows heterogeneous Time to infect 90% of the population patterns. In particular, the inter-event times on sexual and proximity contacts, email, and cell phone communication patterns are reasonably well described by power-law like T V distributions [10,12,18,20,21]. A power-law T V distribution means that there are bursts, i.e., trains of activations followed by periods of inactivity of various lengths.…”

Smallpox: New Perspectives Regarding Risk Assessment & Management

“…The fraction of repeated contacts in the second day with respect to the first is 12%. With such conditions, outbreaks tend to be explosive (attack rate between 51% and 80%) [28]. In other words, the 2,000 smallpox primary cases -still unknown from the UK health authorities -can contaminate between 51% and 80% of their 30 contacts per day i.e.…”