  Background: The novel coronavirus pandemic has affected Brazil's Santa
Catarina State (SC) severely. At the time of writing (24 March 2021), over
764,000 cases and over 9,800 deaths by COVID-19 have been confirmed, hospitals
were fully occupied with local news reporting at least 397 people in the
waiting list for an ICU bed. In an attempt to better inform local policy
making, we applied an existing Bayesian algorithm to model the spread of the
pandemic in the seven geographic macro-regions of the state. Here we propose
changes to extend the model and improve its forecasting capabilities.
  Methods: Our four proposed variations of the original method allow accessing
data of daily reported infections and take into account under-reporting of
cases more explicitly. Two of the proposed versions also attempt to model the
delay in test reporting. We simulated weekly forecasting of deaths from the
period from 31/05/2020 until 31/01/2021. First week data were used as a
cold-start to the algorithm, after which weekly calibrations of the model were
able to converge in fewer iterations. Google Mobility data were used as
covariates to the model, as well as to estimate of the susceptible population
at each simulated run.
  Findings: The changes made the model significantly less reactive and more
rapid in adapting to scenarios after a peak in deaths is observed. Assuming
that the cases are under-reported greatly benefited the model in its stability,
and modelling retroactively-added data (due to the "hot" nature of the data
used) had a negligible impact in performance.
  Interpretation: Although not as reliable as death statistics, case
statistics, when modelled in conjunction with an overestimate parameter,
provide a good alternative for improving the forecasting of models, especially
in long-range predictions and after the peak of an infection wave.
