ABSTRACT
The COVID-19 pandemic has created an urgency for studies to understand the spread of the virus, in particular, to predict the number of daily cases. This type of investigation depends heavily on the data collected and made available manually. Therefore, data are susceptible to human errors which can cause anomalies in the dataset. Understanding and correcting anomalies in real-world application data is an important task to ensure the reliability of the data analysis and prediction tools. This paper presents a spectral anomaly detection and correction strategy that uses concepts from the graph signal processing (GSP) theory. The main advantage of the introduced strategy is to analyze the variation in the daily number of cases with the proximity relation between the investigated locations. Experiments were carried out with real meteorological and mobility data for predicting the number of COVID-19 cases by the classic prediction model known as autoregressive integrated moving average exogenous (ARIMAX). Then, the anomaly detection method was applied to determine the relationship between the prediction errors and the anomalous variations identified by the tool. The results show a strong relationship between the anomalous variations and the errors made by the model and attest to the increase in the accuracy of the prediction model after the normalization of the anomalies. © 2021 IEEE