We develop a method for improved estimation of a participant’s inclusion probability based on their network size (degree) as well as other information.
In this paper we undertake a scientific study of a COVID-19 outbreak in Sukhbaatar, Mongolia. We do so by analyzing data collected via contact tracing of cases by the rapid response team. We use a novel statistical model of the transmission networks …
We develop a method for improved estimation of a participant’s inclusion probability based on their network size (degree) as well as other information.
Background: The rapid increase in the number of coronavirus disease 2019 (COVID-19) cases worldwide has raised concerns of viral transmission from individuals displaying no or delayed clinical symptoms. We quantified the transmission potential of …
Respondent-driven sampling (RDS) is commonly used to study hard-to-reach populations since traditional methods are unable to efficiently survey members due to the typically highly stigmatized nature of the population. The number of people in these …
Respondent-driven sampling (RDS) is a method for sampling from a target population by leveraging social connections. RDS is invaluable to the study of hard-to-reach populations. However, RDS is costly and can be infeasible. RDS is infeasible when …
Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to …
Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to …