  The need to forecast COVID-19 related variables continues to be pressing as
the epidemic unfolds. Different efforts have been made, with compartmental
models in epidemiology and statistical models such as AutoRegressive Integrated
Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence
models. These efforts have proved useful in some instances by allowing decision
makers to distinguish different scenarios during the emergency, but their
accuracy has been disappointing, forecasts ignore uncertainties and less
attention is given to local areas. In this study, we propose a simple Multiple
Linear Regression model, optimised to use call data to forecast the number of
daily confirmed cases. Moreover, we produce a probabilistic forecast that
allows decision makers to better deal with risk. Our proposed approach
outperforms ARIMA, ETS and a regression model without call data, evaluated by
three point forecast error metrics, one prediction interval and two
probabilistic forecast accuracy measures. The simplicity, interpretability and
reliability of the model, obtained in a careful forecasting exercise, is a
meaningful contribution to decision makers at local level who acutely need to
organise resources in already strained health services. We hope that this model
would serve as a building block of other forecasting efforts that on the one
hand would help front-line personal and decision makers at local level, and on
the other would facilitate the communication with other modelling efforts being
made at the national level to improve the way we tackle this pandemic and other
similar future challenges.

Applications;Computers and Society