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Forecasting COVID-19: Vector Autoregression-Based Model.

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Summary
This summary is machine-generated.

This study introduces a vector autoregressive model for accurate COVID-19 spread forecasting. The model effectively predicts new and cumulative cases and deaths in the UAE, Saudi Arabia, and Kuwait.

Keywords:
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Area of Science:

  • Epidemiology
  • Public Health
  • Time Series Analysis

Background:

  • Accurate forecasting of infectious disease spread is crucial for public health.
  • COVID-19 pandemic necessitates robust predictive models for effective management.

Purpose of the Study:

  • To propose and validate a novel forecasting approach for COVID-19 spread.
  • To combine time series data of new cases and deaths for a joint forecasting model.

Main Methods:

  • Utilized a vector autoregressive (VAR) model for time series analysis.
  • Integrated daily new cases and new deaths data into a joint forecasting framework.
  • Applied the model to forecast COVID-19 cases and deaths in the UAE, Saudi Arabia, and Kuwait.

Main Results:

  • The proposed model demonstrated high accuracy in out-of-sample forecasts.
  • Achieved Mean Absolute Percentage Errors (MAPE) of 0.35%, 2.03%, and 3.75% for daily new cases in UAE, Saudi Arabia, and Kuwait, respectively.
  • Obtained very low MAPE (0.0017% to 0.024%) for cumulative case predictions.

Conclusions:

  • The vector autoregressive model provides a valuable tool for pandemic management.
  • The model's superior accuracy compared to existing methods highlights its potential.
  • Accurate forecasting aids in informed public health decision-making during pandemics.