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Forecasting Covid-19: SARMA-ARCH approach.

Firuz Kamalov1, Fadi Thabtah2

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This summary is machine-generated.

Forecasting new Covid-19 cases in the UAE is vital for public health. This study uses advanced statistical models, achieving high accuracy in predicting infection numbers.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Accurate forecasting of infectious disease outbreaks is essential for effective public health policy and resource allocation.
  • Existing research often focuses on total case counts, overlooking the greater volatility and predictive challenges of new infection numbers.
  • The United Arab Emirates (UAE) requires reliable methods to anticipate and manage the spread of COVID-19.

Purpose of the Study:

  • To develop and validate accurate forecasting models for *new* COVID-19 infections in the UAE.
  • To address the limitations of existing models by focusing on the more volatile metric of daily new cases.
  • To provide a tool for health officials to better anticipate and plan for future infection waves.

Main Methods:

  • Construction of seasonal autoregressive moving average (SARIMA) and autoregressive conditional heteroscedasticity (ARCH) models.
  • Analysis of correlation plots and residual diagnostics to ensure model robustness.
  • Utilization of precise population data to enhance the reliability of forecasting outcomes.

Main Results:

  • The developed SARIMA and ARCH models demonstrated a high degree of accuracy in forecasting new COVID-19 cases.
  • The models effectively captured the volatility inherent in daily new infection data.
  • The use of accurate population data contributed to more dependable prediction results.

Conclusions:

  • The proposed forecasting models offer a reliable method for predicting new COVID-19 infections in the UAE.
  • These models can serve as a valuable tool for public health officials in strategic planning and response efforts.
  • Accurate forecasting of new cases is crucial for mitigating the impact of infectious diseases.