This study presents a systematic review and meta-analysis of studies on tick-borne encephalitis virus (TBEV) prevalence in ticks and human hosts in China, published between 2000 and 2023. Extensive searches were conducted on four databases—PubMed, CNKI, VIP, and Wan Fang. The findings indicated overall pooled prevalence estimates of TBEV infection in ticks and humans of 5.8% and 9.0%, respectively. The prevalence of TBEV in ticks was 4.8% (95%CI, 4.5–7.1%) during 2000–2010 and increased to 6.3% (95%CI, 4.7–7.8%) during 2011–2023, and was mainly distributed in Jilin (13.4%; 95%CI, 8.3–18.4%) and Inner Mongolia (4.5%; 95%CI, 1.8–7.1%). The prevalence of TBEV was higher in Dermacentor silvarum than in other tick species (8.1%; 95%CI, 3.2–12.9%). The seroprevalence of TBEV in humans increased from 4.7% (95%CI, 2.9–6.4%) during 2000–2010 to 17.6% (95%CI, 11.3–23.8%) during 2011–2023. Simultaneously, the analysis results of the population characteristics showed that females, forestry workers, military personnel, and farmers were found to be susceptible, and the highest seroprevalence was noted among homemakers and unemployed individuals. There were certain differences in the seroprevalence among populations in different provinces, especially in Xinjiang and Heilongjiang. This study can provide a reference for a more comprehensive and in-depth investigation of ticks and humans infected with TBEV in China.

Swine erysipelas (SE) is one of the best-known and most serious diseases that affect domestic pigs, which is caused by Erysipelothrix rhusiopathiae . It is endemic in Nanning and has been circulating for decades, causing considerable economic losses. The aim of this study was to investigate the effect of meteorological-related variations on the epidemiology of swine erysipelas in Nanning City, a subtropical city of China. Data on monthly counts of reported swine erysipelas and climate data in Nanning are provided by the authorities over the period from 2006 to 2015. Cross-correlation analysis was applied to identify the lag effects of meteorological variables. A zero-inflated negative binomial (ZINB) regression model was used to evaluate the independent contribution of meteorological factors to SE transmission. After controlling seasonality, autocorrelation and lag effects, the results of the model indicated that Southern Oscillation Index (SOI) has a positive effect on SE transmission. Moreover, there is a positive correlation between monthly mean maximum temperature and relative humidity at 0-1 month lag and the number of cases. Furthermore, there is a positive association between the number of SE incidences and precipitation, with a lagged effect of 2 months. In contrast, monthly mean wind velocity negatively correlated with SE of the current month. These findings indicate that meteorological variables may play a significant role in SE transmission in southern China. Finally, more public health actions should be taken to prevent and control the increase of SE disease with consideration of local weather variations.