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Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average
Debabrata Dansana1, Raghvendra Kumar1, Janmejoy Das Adhikari1
1Department of Computer Science and Engineering, GIET University, Gunupur, India.
Frontiers in Public Health
|November 16, 2020
Summary
This study forecasts COVID-19 outbreaks using Autoregressive Integrated Moving Average (ARIMA) time series analysis. ARIMA models predict over 120,000 fatal cases by April 1, 2020, aiding pandemic response.
Area of Science:
- Epidemiology
- Time Series Analysis
- Public Health
Background:
- The novel coronavirus (COVID-19) was declared a global pandemic by the WHO on March 11, 2020.
- Originating in Wuhan, China, COVID-19 rapidly spread worldwide, infecting over 198 countries and exceeding 250,000 cases by March 18, 2020.
Purpose of the Study:
- To forecast the trajectory of the COVID-19 outbreak globally using time series analysis.
- To predict the number of confirmed and fatal cases in the near future.
Main Methods:
- Utilized Autoregressive Integrated Moving Average (ARIMA) models for time series forecasting.
- Analyzed COVID-19 data from February 1, 2020, to April 1, 2020.
- Evaluated autocorrelation functions and white noise for confirmed and fatal cases.
Main Results:
- The ARIMA model forecasted approximately 120,000 fatal COVID-19 cases by April 1, 2020.
- Analysis included total confirmed cases, total fatal cases, and time-series characteristics.
- The study assessed the predictive accuracy and patterns within the outbreak data.
Conclusions:
- Time series analysis, specifically ARIMA, provides a valuable tool for forecasting infectious disease outbreaks like COVID-19.
- The findings highlight the potential severity of the pandemic and the need for robust public health interventions.
- Accurate forecasting aids in resource allocation and strategic planning during global health crises.
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