COVID-19 forecasts via stock market indicators

Area of Science:

• Epidemiology
• Financial Market Analysis
• Time Series Analysis

Background:

• Accurate forecasting of infectious disease outbreaks is crucial for public health.
• Traditional epidemiological models may not fully capture short-term dynamics.
• Logistical planning for pandemics requires reliable, timely data insights.

Purpose of the Study:

• To investigate the applicability of financial technical analysis tools for analyzing scientific time series data.
• To assess the predictive power of these tools on World Health Organization (WHO) COVID-19 data.
• To evaluate the potential for these methods in identifying pandemic waves and assessing health policy impact.

Main Methods:

• Reinterpreting daily COVID-19 case data using candlestick chart representations.
• Applying popular stock market technical indicators, including MACD and RSI.
• Quantitatively assessing the statistical significance of these technical analyses for prediction.

Main Results:

• Demonstrated statistical significance of MACD, RSI, and candlestick analyses in predicting trends for both financial and COVID-19 data.
• Successfully utilized the novel approach to identify the onset of subsequent COVID-19 pandemic waves.
• Provided a method to quantitatively assess the impact of health policies on case growth.

Conclusions:

• Financial technical analysis tools offer a novel and statistically significant approach to analyzing epidemiological time series data.
• This methodology provides valuable insights for short-term forecasting, resource allocation, and understanding pandemic dynamics.
• The approach can be a valuable addition to epidemiological research and public health preparedness.