Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in Korea.

Area of Science:

• Epidemiology
• Public Health
• Time Series Analysis

Background:

• The third wave of COVID-19 in Korea (Nov-Dec 2020) saw rising cases and strained hospital resources.
• Increased social distancing measures were implemented, but gatherings posed further risks.
• Accurate forecasting is vital for managing the pandemic's impact.

Purpose of the Study:

• To highlight the significance of prediction timing over merely forecasting case numbers.
• To provide a framework for systematic response to surges.
• To aid government and KDCA in pandemic management.

Main Methods:

• Utilized the Auto Regressive Integrated Moving Average (ARIMA) model for cumulative COVID-19 case prediction.
• Empirically analyzed data, creating five groups based on variability (minimum, maximum, high).
• Subdivided groups into 19 distinct cases for granular forecasting.

Main Results:

• Group and case-by-case predictions accurately identified decreasing and increasing COVID-19 trends.
• The ARIMA model demonstrated effectiveness in capturing pandemic dynamics.
• Forecasts provided actionable insights into trend shifts.

Conclusions:

• Emphasizes the critical role of timely trend prediction in pandemic response.
• Urgent and robust government interventions are necessary to curb transmission.
• The study offers a systematic approach for public health agencies to manage future outbreaks.