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Multiple Output and Multi-Steps Prediction of COVID-19 Spread Using Weather and Vaccination Data

In recent days, the world has seen an expansion of the COVID-19 outbreak, especially the number of deaths. The mobility, the laziness to take necessary precautions, the new variants, and many other factors made the situation worse. It is thus important to study coronavirus spread to prevent people from its severity and face its danger. This paper is an investigation of the COVID-19 outbreak in six different countries. It discusses correlations and statistical analysis of the COVID-19 with weather conditions and vaccinations to explain how these factors impact the coronavirus spread. In addition, it predicts the new cases and deaths of the incoming seven days for Morocco. The predictive models applied in this paper use multi-step and multi-output prediction with both machine learning and deep learning algorithms that are specialized in time series problems. Our model algorithms give decision-makers a smart tool, using input features, to simulate different disaster situations, predict new infections, and expected deaths. It's useful for future pandemics or new variants to be more proactive. Predicting the epidemic situation in the future using the current conditions will allow humanity to be ready for the necessary sanitary measures. This work is typically useful for any time and any pandemic crisis. © 2021 International Information and Engineering Technology Association. All rights reserved.