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Using next generation matrices to estimate the proportion of cases that are not detected in an outbreak

By H Juliette T Unwin, Anne Cori, Natsuko Imai, Katy A. M. Gaythorpe, Sangeeta Bhatia, Lorenzo Cattarino, Christl A. Donnelly, Neil M. Ferguson, Marc Baguelin

Posted 26 Feb 2021
medRxiv DOI: 10.1101/2021.02.24.21252339

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for many infectious disease outbreaks, such as Ebola and SARS-CoV-2. Unfortunately, these systems are not fully effective, and cases can still go undetected as people may not know or remember all of their contacts or contacts may not be able to be traced. A large proportion of undetected cases suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a novel method for estimating the proportion of cases that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing and case line-lists. We use this method to investigate the proportion of undetected cases in two case studies: the SARS-CoV-2 outbreak in New Zealand during 2020 and the West African Ebola outbreak in Guinea during 2014. We estimate that only 6% of SARS-CoV-2 cases were not detected in New Zealand (95% credible interval: 1.31 - 16.7%), but over 60% of Ebola cases were not detected in Guinea (95% credible interval: 15 - 90%).

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