Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.
State of the art in parallel computing with r.Journal of Statistical Software, 31:1–27
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Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy
Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.