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 …
Network-based models for sexually transmitted disease transmission rely on initial partnership networks incorporating structures that may be related to risk of infection. In particular, initial networks should reflect the level of concurrency and …