ABSTRACT
Purpose: 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. Methods & Materials: 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. Results: 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. Conclusion: This novel approach can be applied to assess the effectiveness of CT. Importantly, the approach described is disease-agnostic, and can be extended to assess the sensitivity of CT for any disease, including COVID-19, for which CT has been identified as a crucial component of the response activities.
ABSTRACT
While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of COVID-19 (detected and undetected) infections in several European countries. The question being asked is: How many cases have actually occurred? We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow. Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European countries, where the epidemic spreads differently.