Epidemiological Feature Detection: A Latent Mixture Approach to Modelling Surges in COVID-19 Cases and Deaths

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 accepted epidemiological models 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 and highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures.Funding Information: This work was financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant RGPIN-2018-05675 (to S.W.-H.); A.M. acknowledges the FRQNT award for MSc studies and McGill Engineering MEUSMA fellowship; L.K. was financially supported by the McGill SURE undergraduate internship.Declaration of Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.