Background Brucellosis is a neglected zoonotic disease and remains a global public health priority for the World Health Organization. Despite its significant global health burden, the disease remains substantially underreported in many regions. This study aimed to elucidate the associations between human brucellosis and its environmental, livestock-related, and socioeconomic determinants in Inner Mongolia from 2014 to 2023, utilizing a robust Bayesian spatiotemporal framework. Methods Descriptive epidemiological methods were employed to characterize the overall epidemic trends of brucellosis in Inner Mongolia. Subsequently, time series decomposition was utilized to analyze the temporal dynamics and seasonal patterns of brucellosis incidence. To identify key drivers of the disease, Spearman correlation analysis was performed to screen potential predictors and mitigate multicollinearity among meteorological, socioeconomic, and livestock-related factors. Finally, a Bayesian spatiotemporal model was developed to quantify the specific associations between these factors and brucellosis risk across the region. Results The incidence of brucellosis exhibited an initial upward trend followed by a decline, demonstrating pronounced seasonality in Inner Mongolia. Among environmental factors, atmospheric pressure (Relative Risk [RR] = 1.22, 95% Confidence Interval [CI]: 1.18–1.27) and wind speed (RR = 1.21, 95% CI: 1.16–1.26) were identified as major risk factors, while the Normalized Difference Vegetation Index (NDVI) exerted a protective effect (RR = 0.89, 95% CI: 0.87–0.91). Livestock density, particularly of sheep and cattle, was a key zoonotic driver. Notably, an inverted U-shaped relationship was observed between GDP and disease risk, with the highest risk (RR = 1.65, 95% CI:1.26–2.17) identified at intermediate economic levels (40–100 million yuan). This suggests an economic development paradox, where initial economic growth intensifies livestock production, thereby escalating short-term disease risks. Conclusions Human brucellosis in Inner Mongolia exhibits significant seasonality and is driven by a complex interplay of environmental and socioeconomic factors. Prevention and control efforts should be targeted at intermediate-income regions and intensified during the spring and summer months. Furthermore, strengthening animal immunization, enhancing occupational protection, and addressing the economic development paradoxthrough sustainable livestock management are critical to reducing the regional disease burden.
Brucellosis is a highly infectious anthropozoonotic disease caused by the genus Brucella, which is the pathogenic bacteria in public health risk and occupation exposure. Therefore, rapid detection and identification of Brucella is crucial for controlling and managing the prevalence of brucellosis. In this work, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) algorithms was developed to identify Brucella isolates at the species level rapidly. A total of 1440 MALDI spectral data from 59 strains of B. melitensis (23 strains of B. melitensis bv. 1 and 36 strains of B. melitensis bv. 3) were obtained. Four ML algorithms including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR) were compared for typing Brucella species. The area under receiver operating characteristic (ROC) curve, precision-recall ratio (PR), accuracy, specificity, and sensitivity were used to evaluate the performances of different ML algorithms. These results showed that MALDI-TOF MS combined with the XGBoost algorithm is the best method for typing Brucella species with good performances, demonstrating the significant potential applications in the diagnosis, control, and epidemiology of brucellosis.
Mixed geographically and temporally weighted regression (GTWR) models, a combination of linear and spatiotemporally varying coefficient models, have been demonstrated as an effective tool for spatiotemporal data analysis under global homogeneity and spatiotemporal heterogeneity. Simultaneously, multiscale estimation for GTWR models has also attracted wide attention due to its scale flexibility. However, most of the existing estimation methods for the mixed GTWR models still have the limitation that either all of regression relationships operate at the same spatiotemporal scale, or each of coefficients is estimated using back-fitting procedures that are very time-consuming. In order to improve the estimation accuracy and alleviate the computation burden, we propose a multiscale method with the adaptive bandwidth (short for scale-adaptive) for calibrating mixed GTWR (say mixed MGTWR) models. In the proposed multiscale estimation approach, a two-step method is used to estimate the constant coefficients and varying coefficients, and then each of the varying coefficients is again estimated by back-fitting procedures with different bandwidth sizes. In addition, we also address the calculation of “hat matrix” in the multiscale estimation for GTWR model and then derive the hat matrix of the complete MGTWR model. Simulation experiments assess the performance of the proposed scale-adaptive calibration method. The results show that the proposed method is much more efficient than existing estimation methods with regard to estimation accuracy and computation efficiency. Moreover, the proposed scale-adaptive method can also correctly reflect the inherent spatiotemporal operating scales of the explanatory variables. Finally, a real-world example demonstrates the applicability of the proposed scale-adaptive method.
Alveolar echinococcosis (AE), caused by the larval stage of Echinococcus multilocularis, poses a substantial global health challenge due to its high mortality profile. This study reports the inaugural human infection of echinococcosis caused by the Mongolian genotype of E. multilocularis in China, also the first reported indigenous AE case in Inner Mongolia. A 58-year-old female pastoralist from Inner Mongolia, who had no endemic region exposure history but prolonged occupational contact with dogs, presented with severe AE. Clinical examinations revealed a massive hepatic lesion exceeding 10 cm in diameter, accompanied by elevated eosinophils (0.90 × 109/L) and basophils (0.08 × 109/L). Despite undergoing liver transplantation, the patient succumbed postoperatively. Histopathological confirmation and molecular phylogenetics identified the Mongolian genotype of E. multilocularis infection, distinct from the predominant Asian genotype in China. Potential evidence of zoonotic transmission was discovered through genotype-matched E. multilocularis detection in corsac fox (Vulpes corsac) feces from the grasslands along the shores of Hulun Lake (Hulun Buir City, northeastern Inner Mongolia, China). This report provides the primary evidence of a locally acquired human AE infection in China caused by the Mongolian genotype of Echinococcus multilocularis. The discovery of this case challenges historical classifications of echinococcosis endemic areas. The findings call for revised AE-endemic identification criteria, improved AE diagnostic protocols, and enhanced AE surveillance in the Inner Mongolia region to generate further epidemiological evidence and information on disease progression.
