Novel application of capture-recapture methods to estimate the completeness of contact tracing during a large outbreak of Ebola Virus Disease, Democratic Republic of Congo, 2018-2020 (preprint)

ABSTRACT

Despite its critical role in containing outbreaks, the efficacy of contact tracing (CT), measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of CT by applying unilist capture-recapture methods on data from the 2018-2020 outbreak of Ebola virus disease in the Democratic Republic of Congo. We applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved cases with a) any contacts and b) infected contacts, to compute CT sensitivity. Geometric distributions were the best fitting models. Our results indicate that CT efforts identified almost all (n=792, 99%) of the cases with any contacts, but only half (n=207, 48%) of the cases with infected contacts, suggesting that CT efforts performed well at identifying contacts during the listing stage, but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the current COVID-19 pandemic.