Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China.

Population movement significantly influences respiratory disease transmission; however, movement restrictions can impose substantial societal burdens. To understand spatiotemporal characteristic of a potential respiratory pandemic in Chinese mainland which could help in precise control, a spatiotemporal transmission model incorporating population movement dynamics was developed. The model was calibrated using Corona Virus Disease of 2019 data collected from an online survey of 3 million respondents conducted in December 2022. With the model, simulated hypothetical respiratory pandemics originating from each province and successfully predicted province-level transmission paths, peaking times and peaking infections. Beijing was identified as the most important location within the transmission network under various scenarios due to its strong mobility and socioeconomic connectivity. A global sensitivity analysis was conducted using the Partial Rank Correlation Coefficient (PRCC) method to evaluate the influence of key disease parameters on transmission dynamics. Transmission rate, progression rate and recovery rate were identified as key parameters influencing transmission characteristics. Additionally, the model provides a valuable predictive tool for understanding the transmission patterns of respiratory pandemics, which enables policymakers to gain insights into epidemic progression, facilitating the development of more targeted and effective control measures. Furthermore, our research offers a methodological framework for predicting epidemic transmission characteristics in advance, contributing to the field of public health.