86,035 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
Sampling procedure and potential sampling sites
Protocol for collecting environmental samples for Legionella culture during a cluster or outbreak investigation or when cases of disease may be associated with a facility.Sampling should only be performed after a thorough environmental assessment has been done and a sampling plan has been made. This protocol describes how to take standard biofilm swab, bulk water, and filter samples from commonly sampled sites.CS306411-Acdc-sampling-procedure.pd
Respondent driven sampling and sparse graph convergence
We consider a particular respondent-driven sampling procedure governed by a
graphon. By a specific clumping procedure of the sampled vertices we construct
a sequence of sparse graphs. If the sequence of the vertex-sets is stationary
then the sequence of sparse graphs converge to the governing graphon in the
cut-metric. The tools used are concentration inequality for Markov chains and
the Stein-Chen method.Comment: 13 page
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