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Objective: This study aimed to enhance the current understanding of the epidemiologic characteristics, laboratory diagnostic levels, and changes in pathogenic populations of rabies in China by studying the status of the human rabies epidemic in China from 2015-2021 and provide useful information for guiding rabies disease prevention and control strategies. Methods: We analyzed the incidence, distribution, and laboratory testing of human rabies in mainland China using statutory surveillance data from 2015-2021. Based on a literature review, the study summarizes the recent updates of the rabies virus population in each province based on previous monitoring. Results: A total of 3032 rabies cases were reported in China from 2015-2021, with a year-after-year decrease in the total number of cases. Most of the cases (75.19%) were distributed in Hunan, Henan, Guangxi, Guizhou, Hubei, Yunnan, Jiangsu, Anhui, Guangdong, and Sichuan, with 13 counties (districts) reporting > 50 cases in 7 years. The number of reported counties (districts) decreased from 512 in 2015 to 116 in 2021. Farmers accounted for most of the cases (73%), and the highest proportion of cases (54.62%) occurred in individuals 50-75 years of age. No changes in endemic populations were detected in China. The laboratory diagnosis rate of cases increased from 4.74% in 2015 to 22.93% in 2021. Conclusions: The rabies epidemic in China decreased steadily from 2015-2021, with a marked contraction in the geographic scope. In the future it will be necessary to continue to carry out large-scale dog immunization and strengthen the surveillance and laboratory diagnosis of rabies.

Scattering kernel models for gas–solid interaction are crucial for rarefied gas flows and microscale flows. However, most existing models depend on certain accommodation coefficients (ACs). We propose here to construct a data-based model using molecular dynamics (MD) simulation and machine learning. The gas–solid interaction is first modelled by 100 000 MD simulations of a single gas molecule reflecting on the wall surface, which is fulfilled by GPU parallel technology. The results showed a correlation of the reflection velocity with the incidence velocity in the same direction, and also revealed correlations that may exist in different directions, which are neglected by the traditional gas–solid interaction model. Inspired by the sophisticated Cercignani–Lampis–Lord (CLL) model, two improved scattering kernels were constructed to better reproduce the probability density of velocity determined from MD simulation. The first one adopts variable ACs which depend on the incidence velocity and the second one combines three CLL-like kernels. All the parameters in the improved kernels are automatically chosen by the machine learning method. Compared with the numerical experiments of a molecular beam, the reconstructed scattering kernels are basically consistent with the MD results.