Speaker
Description
Background: Case reporting in a pandemic or for emerging viral infections depends heavily on testing strategies and as a result the degree of under-reporting of true incidence can vary substantially. We previously developed GInPipe [1], a computational tool that allows estimation of under-reporting levels over time from time-stamped pathogen genome surveillance data, within a few minutes compute time. Results from GInPipe have been validated against simulated outbreaks, actual SARS-CoV-2 incidences from the UK ONS study and viral load data in wastewater [2].
Methods: We provide a rigorous derivation and extension of GInPipe metrics that allow us to estimate infection incidence from the rate of population-level pathogen evolution. Key to this method is the fact that the majority of SARS-Cov-2 transmissions contribute to some gradual evolution in the viral genome. The underlying model assumes that new sequence types emerge in a Poisson process and individual sequence types grow in size according to a Yule process.
Results: We show that our proposed model of SARS-Cov-2 evolution agrees well with real-world genomic surveillance data. Moreover, we show how the frequency-of-frequencies distribution of the number of unique genomic sequences can be used to evaluate the incidence correlate and its uncertainty.
Conclusion & Outlook: We present a rigorous derivation of metrics that allow estimating SARS-CoV-2 incidences from sequencing data. This allows to rigorously deduce confidence intervals from any time series of viral sequencing data. Moreover, the method may be adapted to other respiratory viruses, conditioned that short within-patient evolutionary dynamics constitute the majority of evolutionary steps observed at the population-level.
[1] Smith, M.R., Trofimova, M., Weber, A. et al. Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020. Nat Commun 12, 6009 (2021). https://doi.org/10.1038/s41467-021-26267-y
[2] SARS-CoV-2 Evolution on a Dynamic Immune Landscape
N. Alexia Raharinirina, Nils Gubela, Daniela Börnigen, Maureen Rebecca Smith, Djin-Ye Oh, Matthias Budt, Christin Mache, Claudia Schillings, Stephan Fuchs, Ralf Dürrwald, Thorsten Wolff, Martin Hölzer, Sofia Paraskevopoulou, and Max von Kleist; Nature, 2025 (in print)
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