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COVID-19 has led to excess deaths around the world, however it remainsunclear how the mortality of other causes of death has changed during thepandemic. Aiming at understanding the wider impact of COVID-19 on other deathcauses, we study Italian data set that consists of monthly mortality counts ofdifferent causes from January 2015 to December 2020. Due to the highdimensional nature of the data, we develop a model which combines conventionalPoisson regression with tensor train decomposition to explore the lowerdimensional residual structure of the data. We take a Bayesian approach, imposepriors on model parameters. Posterior inference is performed using an efficientMetropolis-Hastings within Gibbs algorithm. The validity of our approach istested in simulation studies. Our method not only identifies differentialeffects of interventions on cause specific mortality rates through the Poissonregression component, but also offers informative interpretations of therelationship between COVID-19 and other causes of death as well as latentclasses that underline demographic characteristics, temporal patterns andcauses of death respectively.

Bayesian Poisson Regression and Tensor Train Decomposition Model for Learning Mortality Pattern Changes during COVID-19 Pandemic