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 phone 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, Seasonal Naive, Prophet 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.

随着疫情的发展，预测与 COVID-19 相关的变量的需求仍然紧迫。人们已经做出了不同的努力，包括流行病学中的分区模型和统计模型，例如自回归综合移动平均线 (ARIMA)、指数平滑 (ETS) 或计算智能模型。事实证明，这些努力在某些情况下是有用的，可以让决策者在紧急情况下区分不同的情况，但其准确性令人失望，预测忽略了不确定性，而且对当地地区的关注较少。在本研究中，我们提出了一个简单的多元线性回归模型，经过优化以使用电话数据来预测每日确诊病例数。此外，我们还提供概率预测，使决策者能够更好地应对风险。我们提出的方法优于 ARIMA、ETS、Seasonal Naive、Prophet 和没有呼叫数据的回归模型，通过三点预测误差指标、一个预测区间和两个概率预测准确性度量进行评估。该模型的简单性、可解释性和可靠性是在仔细的预测过程中获得的，对于迫切需要在已经紧张的卫生服务中组织资源的地方决策者来说是一个有意义的贡献。我们希望该模型能够成为其他预测工作的基石，一方面可以帮助地方层面的一线个人和决策者，另一方面可以促进与国家其他建模工作的沟通。水平，以改善我们应对这一流行病和其他类似未来挑战的方式。