4 research outputs found
Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks
Contacts between individuals play an important role in determining how
infectious diseases spread. Various methods to gather data on such contacts
co-exist, from surveys to wearable sensors. Comparisons of data obtained by
different methods in the same context are however scarce, in particular with
respect to their use in data-driven models of spreading processes. Here, we use
a combined data set describing contacts registered by sensors and friendship
relations in the same population to address this issue in a case study. We
investigate if the use of the friendship network is equivalent to a sampling
procedure performed on the sensor contact network with respect to the outcome
of simulations of spreading processes: such an equivalence might indeed give
hints on ways to compensate for the incompleteness of contact data deduced from
surveys. We show that this is indeed the case for these data, for a
specifically designed sampling procedure, in which respondents report their
neighbors with a probability depending on their contact time. We study the
impact of this specific sampling procedure on several data sets, discuss
limitations of our approach and its possible applications in the use of data
sets of various origins in data-driven simulations of epidemic processes
How to estimate epidemic risk from incomplete contact diaries data?
Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts
between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the
epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with
respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we
investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show
that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly
available data describing the heterogeneity of the durations of human contacts