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Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average
A M C H Attanayake1, S S N Perera2
1Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
Biomed Research International
|December 21, 2020
Summary
The Alpha-Sutte Indicator approach accurately models and predicts cumulative COVID-19 cases, outperforming ARIMA. This method aids health authorities in managing the pandemic effectively.
Area of Science:
- Epidemiology
- Mathematical modeling
- Public health
Background:
- The COVID-19 pandemic's rapid spread necessitates effective control strategies.
- No definitive cure exists, making accurate case prediction crucial for mitigation efforts.
- Diverse global socioeconomic and geographical factors influence disease transmission.
Purpose of the Study:
- To model and predict cumulative COVID-19 cases using the Alpha-Sutte Indicator approach.
- To compare the efficacy of the Alpha-Sutte Indicator against the ARIMA method.
- To provide reliable short-term forecasts for public health management.
Main Methods:
- Utilized the Alpha-Sutte Indicator approach for COVID-19 case modeling.
- Selected eight diverse countries (USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, South Africa).
- Validated the model using 10% of cumulative case data up to September 26, 2020.
Main Results:
- The Alpha-Sutte Indicator demonstrated superior performance with low Mean Absolute Percentage Errors (MAPE) across all selected countries.
- MAPE values ranged from 0.03% (Pakistan) to 1.28% (Spain).
- Paired t-tests confirmed no statistically significant differences between forecasted and real cases, validating the model's effectiveness.
Conclusions:
- The Alpha-Sutte Indicator approach is highly suitable for short-term forecasting of cumulative COVID-19 incidences.
- The model's accuracy and reliability support its recommendation for public health decision-making.
- Predictions generated can assist healthcare authorities in pandemic control and management strategies.
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