Incorporating human mobility to enhance epidemic response and estimate real-time reproduction numbers

Human mobility plays a critical role in the transmission dynamics of infectious diseases, influencing both their spread and the effectiveness of control measures. In the process of quantifying the real-time situation of an epidemic, the instantaneous reproduction number R _t appears to be one of the useful metrics widely used by public health researchers, officials, and policy makers. Since individuals can contract infections both within their region of origin and in other regions they visit, ignoring human mobility in the estimation process overlooks its impact on transmission dynamics and can lead to biased estimates of R _t , potentially misrepresenting the true epidemic situation. This study explicitly integrates human mobility data into a disease transmission model based on the renewal equation to estimate instantaneous reproduction numbers that account for spatial connectivity. Furthermore, we incorporate pathogen-specific generation time distribution, observational delay, and latent period, making the framework epidemiologically informed and flexible to a wide range of diseases. The framework is primarily validated using a simulated dataset and applies it to two different mobility settings at two different spatial scales as case studies: England and the north-east region of England. This analysis also investigates the spatial scalability of the framework, indicating that lower spatial resolution can diminish the effect of inter-regional mobility on the disease transmission, and we conclude that utilizing a finer spatial scale is advantageous on the basis of data availability and computational resources to obtain a better picture of the detailed transmission dynamics. This framework uses non-identifiable, publicly available datasets that are widely accessible, which proves the practical applicability and offers an alternative approach of individual-level modeling. These findings emphasize the necessity of incorporating human mobility into infectious disease models to enhance real-time estimations of effective reproduction numbers. Furthermore, a clear grasp of the transmission scenario also supports better-informed and more targeted public health measures.