Prediction of ILI following the COVID-19 pandemic in China by using a partial differential equation

Abstract The COVID-19 outbreak has significantly disrupted the lives of individuals worldwide. Following the lifting of COVID-19 interventions, there is a heightened risk of future outbreaks from other circulating respiratory infections, such as influenza-like illness (ILI). Accurate prediction models for ILI cases are crucial in enabling governments to implement necessary measures and persuade individuals to adopt personal precautions against the disease. This paper aims to provide a forecasting model for ILI cases with actual cases. We propose a specific model utilizing the partial differential equation (PDE) that will be developed and validated using real-world data obtained from the Chinese National Influenza Center. Our model combines the effects of transboundary spread among regions in China mainland and human activities’ impact on ILI transmission dynamics. The simulated results demonstrate that our model achieves excellent predictive performance. Additionally, relevant factors influencing the dissemination are further examined in our analysis. Furthermore, we investigate the effectiveness of travel restrictions on ILI cases. Results can be used to utilize to mitigate the spread of disease.