  In 2020, Brazil was the leading country in COVID-19 cases in Latin America,
and capital cities were the most severely affected by the outbreak. Climates
vary in Brazil due to the territorial extension of the country, its relief,
geography, and other factors. Since the most common COVID-19 symptoms are
related to the respiratory system, many researchers have studied the
correlation between the number of COVID-19 cases with meteorological variables
like temperature, humidity, rainfall, etc. Also, due to its high transmission
rate, some researchers have analyzed the impact of human mobility on the
dynamics of COVID-19 transmission. There is a dearth of literature that
considers these two variables when predicting the spread of COVID-19 cases. In
this paper, we analyzed the correlation between the number of COVID-19 cases
and human mobility, and meteorological data in Brazilian capitals. We found
that the correlation between such variables depends on the regions where the
cities are located. We employed the variables with a significant correlation
with COVID-19 cases to predict the number of COVID-19 infections in all
Brazilian capitals and proposed a prediction method combining the Ensemble
Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated
Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX.
After analyzing the results poor predictions were further investigated using a
signal processing-based anomaly detection method. Computational tests showed
that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an
improvement of 30.69% in the average root mean squared error (RMSE) was noticed
when applying the EEMD-ARIMAX method to the data normalized after the anomaly
detection.
