Forecasting Influenza Rates Using Machine Learning: A Study of ChatGPT's Predictive Accuracy

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall performance. Accuracy was measured using the statistical analysis Chi-Square Test, to assess the differences between ChatGPT's predicted influenza rates and the actual observed rates. The null hypothesis stated there is no statistically significant difference between ChatGPT's predictions and the actual influenza rates, while the alternative hypothesis proposed a statistically significant difference does exist. The Chi-Square results showed p-values greater than 0.05 for all three metrics tested, positivity rate, flu cases, and deaths, indicating that the null hypothesis couldn't be rejected. The purpose of this study was to determine the potential of using ML models like ChatGPT for public health forecasting. While the model's predictions were not statistically different from actual values, this does not confirm ChatGPT's forecasting accuracy with certainty. However, the findings provide some statistical support for the model's ability to generate estimates that were directionally consistent with observed trends, suggesting potential for informal or supplemental use in early stage forecasting.