Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

Fentanyl and its derivatives have been mainstays for the treatment of pain for many years. To accurately detect them in medical applications and customs, a rapid, sensitive, and selective method is urgently needed. In this study, we established a point-of-care-testing (POCT) differential Raman approach for the detection of fentanyl substances in liquid and solid conditions. The silver nanoparticle was prepared and characterized as SERS substrate, which can adsorb fentanyl-related molecules on the rough surface to enhance the Raman signal. Subsequently, 27 kinds of fentanyl-related substances were detected to determine that the POCT spectral resolution is better than 6 cm⁻¹, Raman detection range is 100–3200 cm⁻¹, and the detection limit of the fentanyl-related substances at 1002 cm⁻¹ is 0.1–25 ppb. Furthermore, the Raman characteristic peaks of fentanyl were checked through comparison between theoretical calculations and experiments to obtain a database for rapid on-site inspection. Thus, the fast, accurate, stable POCT approach can be widely applied to monitor drugs and toxins due to its sensitivity, specificity, and abundance database.