Poliovirus (PV), a historically significant enterovirus responsible for severe paralytic diseases, has seen its incidence dramatically reduced through widespread vaccination efforts, propelling global eradication initiatives. Despite the success of traditional oral poliovirus vaccines (OPVs) and inactivated poliovirus vaccines (IPVs), challenges such as vaccine-derived virus reversion and biosafety concerns during vaccine production persist. Virus-like particle (VLP) vaccines, which mimic native viral structures without containing viral genomes, offer enhanced safety profiles and robust immunogenicity, positioning them as promising candidates for next-generation poliovirus vaccines, especially in the post-certification era. This review systematically summarizes current progress in poliovirus VLP vaccine research, including the diverse expression systems employed for VLP production, strategies for peptide assembly and stabilization, and evaluations of antigenicity and immunogenicity. Additionally, it highlights structural analyses utilizing cutting-edge cryo-electron microscopy. By integrating recent developments in genetic engineering, structural biology, and immunology, this article discusses the advantages and challenges associated with poliovirus VLP vaccines and explores future directions aimed at supporting the global goal of a poliovirus-free world. This comprehensive overview aims to provide a theoretical foundation and technical guidance to facilitate the development and deployment of safer and more effective poliovirus vaccines.

At present, there are no researches that classify the epidemic intensity of Norovirus GII AGE conbineed case and environmental surveillance data at the same time. With reference to the experience of epidemiological level classification of infectious disease and actual epidemiological status of Norovirus (NoV) AGE in Chaoyang District, the epidemic intensity of NoV GII was divided into five grades with increasing intensity from grade 1 to 5, which corresponds to non-epidemic risk, general risk, moderate risk, high risk and ultra-high risk respectively. If the synthetic index of 2 consecutive monitoring weeks in the epidemic season of 2023-2024 exceeds a certain threshold, an early warning for the corresponding epidemic intensity is issued and recommendations on corresponding control measures will be given. This study established and quantified the criteria for the epidemic intensity of Norovirus GII AGE based on case surveillance data and environmental surveillance data, it provides a reference for other methods to carry out relevant studies in the future. However, mathematical models can’t completely replace professional experience. Therefore, when making decisions with early warning models in practice, it is necessary to refer to the opinions of professional and experienced experts for avoidding the bias of the early warning model from affecting misjudgement strategy.

At present, there is no research that classifies the epidemic intensity of acute gastroenteritis (AGE) caused by Norovirus (NoV) GII combined cases and environmental surveillance data at the same time. With reference to the experience of the epidemiological-level classification of infectious disease and the actual epidemiological status of NoV AGE in Chaoyang District, Beijing, China, the epidemic intensity of NoV GII was divided into five grades with increasing intensity from grade 1 to grade 5, which corresponds to non-epidemic risk, general risk, moderate risk, high risk, and ultra-high risk, respectively. If the synthetic index of two consecutive monitoring weeks in the epidemic season of 2023–2024 exceeds a certain threshold, an early warning for the corresponding epidemic intensity will be issued and recommendations on the corresponding control measures will be given. This study established and quantified the criteria for the epidemic intensity of AGE caused by NoV GII based on case surveillance data and environmental surveillance data. It provides a reference for other methods to carry out relevant studies in the future. However, mathematical models cannot completely replace skilled experience. Therefore, when making decisions with early warning models in practice, it is necessary to refer to the opinions of professional and experienced experts to avoid the bias of the early warning model from affecting strategy judgment.