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A deconvolution approach to modelling surges in COVID-19 cases and deaths by Adam Melnyk; Lena Kozarov; Sebastian Wachsmann-Hogiu is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship betw

Who reads A deconvolution approach to modelling surges in COVID-19 cases and deaths?

Author

Adam Melnyk; Lena Kozarov; Sebastian Wachsmann-Hogiu

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)