Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models.

We address two computational issues common to open-population -mixture models, hidden integer-valued autoregressive models, and some hidden Markov models. The first issue is computation time, which can be dramatically improved through the use of a fast Fourier transform. The second issue is tractability of the model likelihood function for large numbers of hidden states, which can be solved by improving numerical stability of calculations.

Under-reporting of COVID-19 in the Northern Health Authority region of British Columbia.

Asymptomatic and pauci-symptomatic presentations of COVID-19 along with restrictive testing protocols result in undetected COVID-19 cases. Estimating undetected cases is crucial to understanding the true severity of the outbreak. We introduce a new hierarchical disease dynamics model based on the -mixtures hidden population framework.