Application and Comparative Study of Generative Artificial Intelligence for Epidemic Prediction of Coronavirus Disease.

Background and objectives In the past twenty years, several large-scale coronavirus outbreaks have caused heavy loss of life and serious economic damage worldwide. Current global surveillance suggests that similar epidemics may occur again, making timely and accurate forecasting an urgent priority. Yet, many existing prediction methods, mainly based on traditional statistical or machine learning techniques, still struggle to deliver both speed and precision. This study explores a generative artificial intelligence-driven approach aimed at narrowing these gaps. Methods Nine models (three statistical models, three machine learning models, and three generative artificial intelligence models) were compared using weekly COVID-19 case and death data from the United States (US), the United Kingdom (UK), Germany (GE), and Russia (RU) from March 15, 2020, to April 15, 2023. The statistical models used are simple moving average (SMA), simple exponential smoothing (SES), and the Holt linear trend model (Holt). The machine learning models used are k-nearest neighbor regression (KNN), regression tree (RTree), and multilayer perceptron (MLP). The generative AI models used are ChatGPT, DeepSeek (DS), and Kimi. A custom MATLAB program was used to solve the statistical and machine learning models, and the zero-inference forecasting method was used to solve the generative AI model. According to the stepwise prediction theory, error metrics for one-, two-, and three-step forecasts were calculated: mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE). The forecasting performance of each model was compared by comparing the one-, two-, and three-step predicting error metrics. Results In our analysis, generative AI models consistently delivered the most accurate forecasts. Kimi, in particular, recorded the smallest errors for death predictions and among the lowest for new cases, while DS and ChatGPT also performed well, clearly surpassing the statistical and machine learning approaches in short-term COVID-19 forecasting. Conclusion The results of this study demonstrate that generative AI models demonstrate superior predictive accuracy and robustness in epidemic forecasting compared to traditional statistical and machine learning models. This research is innovative in its application of generative AI technology to public health decision-making, demonstrating its robust epidemic forecasting capabilities. Given these proven advantages, public health authorities can integrate generative AI technology into major infectious disease surveillance systems, promote public health data sharing mechanisms, and incorporate generative AI into epidemic intervention and resource allocation. The implementation of these measures will enable governments and regulatory agencies worldwide to use generative AI to enhance early warning capabilities and improve their response to future infectious disease epidemics.