Influencing Factors of Human Brucellosis in Inner Mongolia, China, 2014– 2023: A Bayesian Spatio-Temporal Analysis

Influencing Factors of Human Brucellosis in Inner Mongolia, China, 2014– 2023: A Bayesian Spatio-Temporal Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influencing Factors of Human Brucellosis in Inner Mongolia, China, 2014– 2023: A Bayesian Spatio-Temporal Analysis Qi Zhang, Na Ta, Ruidong Qu, Zitong Zheng, Zhiruo Zhu, Benjian Lan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9119326/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract 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 Spatiotemporal distribution Bayesian modeling Environmental factors Inner Mongolia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Brucellosis is a zoonotic infectious disease caused by genus Brucella . The World Health Organization (WHO) has classified it as a “neglected zoonotic disease” that requires priority control, posing a continuous threat to public health and livestock production worldwide[ 1 ]. The disease is transmitted to humans mainly through direct contact with infected animals and their tissues, consumption of unpasteurized dairy products, or inhalation of contaminated aerosols. Clinical manifestations include undulant fever, excessive sweating, fatigue, and muscle and joint pain. Some patients develop chronic infection, which can lead to damage to multiple organs including bone joints and the nervous system, seriously affecting patients’ work capacity and quality of life[ 2 ]. According to WHO statistics, more than 500,000 new cases of brucellosis are reported globally each year. However, due to widespread underdiagnosis and underreporting, the actual number of cases may be much higher than official reports. Brucellosis not only causes physical suffering to patients but also creates substantial economic burdens, including medical expenses, loss of labor productivity, and livestock production losses[ 3 ]. Brucellosis outbreaks have been reported in over 170 countries and regions to date, ranking it among the most significant global zoonoses[ 4 ]. China is a high-incidence country for brucellosis, with a complex epidemic history. China is a high‑incidence country for brucellosis with a complex epidemic history. From the 1950s to the 1980s, comprehensive control measures—including large‑scale animal immunization and culling of infected animals—effectively brought the epidemic under control, and the incidence rate fell to a historic low of 0.028 per 100,000 population in 1993[ 5 ]. However, since the 1990s, rapid development of the livestock industry coupled with the relaxation of disease prevention and control efforts has led to an epidemic resurgence. The incidence rate has increased continuously, reaching 5.06 per 100,000 in 2021, and the geographic range of the disease has expanded to all provinces in China[ 6 ]. Inner Mongolia Autonomous Region is the most severely affected area in China, reporting 88,000 cases between 2019 and 2023, accounting for 28.87% of all cases nationwide. The average incidence rate is as high as 71.88 per 100,000, far exceeding the national average. In some high-incidence years, the region’s cases represent 40%-50% of the national total, ranking first nationwide[ 7 , 8 ]. This region is located in the northern border of China with vast grasslands and abundant livestock resources. Livestock production is the main income source for the local residents, providing favorable natural and socioeconomic conditions for brucellosis transmission. With economic development and modernization of livestock production, large-scale farming has expanded rapidly, increasing herd density and livestock movement frequency[ 9 ]. Therefore, Inner Mongolia represents a high-priority area for studying the epidemiology of brucellosis to better inform targeted prevention and control strategies. Growing evidence indicates that brucellosis transmission is influenced by multiple factors including natural environmental conditions, socioeconomic development level, livestock production models, and disease control capacity, showing strong spatial and temporal characteristics[ 10 , 11 ]. Meteorological factors such as temperature and precipitation affect disease transmission by influencing the survival time of Brucella in the environment, the reproductive cycle of animal hosts, and human activity patterns[ 12 , 13 ]. However, the factors that affect brucellosis incidence vary greatly across different regions. In other words, the effects of environmental factors on brucellosis incidence remain inconsistent across areas. Therefore, it is necessary to study the potential driving effects and interactions between environmental risk factors and brucellosis transmission in regions with different incidence rates, geographic features, and climate conditions, in order to improve risk assessment and management. Additionally, brucellosis shows significant clustering in both time and space. High-incidence areas remain relatively stable but also undergo dynamic changes, and seasonal fluctuations are obvious. These characteristics suggest complex spatial and temporal dependencies in disease transmission[ 13 – 15 ]. However, most previous studies have adopted traditional epidemiological methods that analyze temporal and spatial dimensions separately, making it difficult to capture the dynamic characteristics of spatial-temporal interactions[ 16 – 18 ]. When analyzing environmental risk factors, simple correlation analysis or linear regression methods are often used, which fail to account for spatial autocorrelation in the data, potentially leading to biased statistical inference[ 19 ]. Bayesian spatio-temporal models effectively address these methodological limitations[ 20 ]. By employing a hierarchical structure, these models simultaneously integrate temporal trends, spatial patterns, and environmental variables. This approach allows for the flexible inclusion of multiple covariates to quantify their specific effects on disease risk. Furthermore, the models provide robust uncertainty estimates through posterior inference[ 21 – 23 ]. In recent years, Bayesian spatio-temporal models have been widely applied to spatial-temporal epidemiological studies of various infectious diseases such as malaria and dengue fever, showing good prospects[ 19 , 20 ]. However, their application in brucellosis research in China remains limited, especially with the lack of systematic spatial-temporal analysis and quantitative assessment of environmental risk factors in Inner Mongolia, one of the most severely affected regions. Recent spatio-temporal analytical studies in Inner Mongolia have revealed that brucellosis cases in this region not only exhibit distinct temporal patterns and spatial clustering, but also experience changing epidemic trends in response to economic development, climate change, and shifts in livestock industry structure[ 25 , 26 ]. Therefore, the objective of this study is to systematically analyze the epidemiological characteristics of brucellosis and quantify the effects of meteorological, ecological, and socioeconomic factors on disease risk using a Bayesian spatiotemporal interaction model, based on 2014–2023 brucellosis surveillance data from Inner Mongolia. The findings will provide new evidence for understanding the epidemiology and underlying drivers of brucellosis in the context of rapid socioeconomic transition, and offer a scientific basis for developing locally tailored, adaptive prevention and control strategies in Inner Mongolia and across China. 2. Methods 2.1 Study Area Inner Mongolia Autonomous Region is located in northern China. The total area of the region is 1.183 million square kilometers, accounting for 12.3% of China's land area, ranking third among all provinces, municipalities, and autonomous regions in the country[ 21 ]. As of the end of 2024, the region had a permanent population of 23.88 million. The region is divided into 9 prefecture-level cities and 3 leagues, comprising a total of 103 counties and districts (Fig. 1). In 2024, Inner Mongolia had the highest cattle population (9.501 million head) and sheep population (69.477 million head) in the nation[ 22 ]. 2.2. Data Sources and Processing 2.2.1 Brucellosis Case Data Human brucellosis is a Category B notifiable infectious disease in China and must be reported through the China Information System for Disease Control and Prevention(CISDCP)[ 23 ]. Monthly brucellosis cases at the county and district levels in Inner Mongolia Autonomous Region from 2014 to 2023 were obtained from the Inner Mongolia Disease Prevention and Control Center. Brucellosis cases were confirmed strictly according to the diagnostic criteria and principles specified in the Industry Standard for the Diagnosis of Brucellosis of the People's Republic of China (WS 269–2007/WS 269–2019). This protocol integrated epidemiological exposure history, clinical manifestations, and laboratory test results[ 24 ]. 2.2.2 Meteorological Data Monthly meteorological data for Inner Mongolia during the study period were obtained from the China Meteorological Data Sharing Service System ( http://data.cma.cn/ ). The extracted meteorological variables included precipitation (Prec, mm), mean temperature (Temperature, °C), specific humidity (Specific Humidity, g/kg), and wind speed (Wind Speed, m/s). The data processing method was as follows: Kriging interpolation was first applied to the meteorological station data to perform spatial interpolation, generating county-level monthly meteorological indicators. 2.2.3 Remote Sensing Data Surface elevation were extracted from the Digital Elevation Model (DEM) provided by the Shuttle Radar Topography Mission (SRTM) with a resolution of 90 m. The processing method was as follows: the mean value of DEM grids was calculated at the county level to obtain the county-level average elevation (Elevation, m) indicator. Normalized Difference Vegetation Index (NDVI) data were obtained from the Land Atmosphere Archive and Distribution System (LAADS DAAC, https://ladsweb.modaps.eosdis.nasa.gov/ ), using global monthly composite products at 1 km resolution. Data processing included three steps. First, negative values and outliers were removed. Second, the maximum value composite (MVC) method was applied for each month to mitigate the effects of cloud cover and atmospheric interference. Finally, the processed NDVI values were averaged at the county level to generate monthly NDVI indicators for each county. 2.2.4 Socioeconomic and Livestock Data County-level socioeconomic data were obtained from the Statistical Yearbook of Inner Mongolia Autonomous Region compiled by the Statistics Bureau of Inner Mongolia Autonomous Region, including: year-end population, annual gross domestic product (GDP), and year-end total livestock numbers and cattle, sheep, and pig inventory( http://www.tjnjw.com/ ). These data reflect county-level population scale, economic development level, and major livestock population size, which are closely associated with brucellosis epidemic risk. 2.3.Statistical Analysis 2.3.1 Time Series Decomposition Model Time series decomposition model was applied to analyze the temporal dynamics of brucellosis incidence in Inner Mongolia from 2014 to 2023. The original series was decomposed into three components: trend, seasonal, and residual. The trend component captures the long-term underlying change in incidence, the seasonal component reflects recurring intra-annual fluctuations, and the residual represents the unexplained random variation after accounting for trend and seasonality. This decomposition enabled a systematic identification of the long-term evolution and seasonal transmission patterns of brucellosis in the region[ 25 ]. 2.3.2 Bayesian Spatiotemporal Model To avoid multicollinearity, Spearman rank correlation analysis was performed to assess correlations among candidate covariates, and covariates with absolute Spearman correlation coefficients less than 0.7 were retained for inclusion in the Bayesian multivariate model. All continuous variables were standardized using z-score normalization to have a mean of 0 and standard deviation of 1, facilitating uniform interpretation of model parameters. GDP was categorized into 20 groups to capture potential nonlinear relationships between economic development and disease risk in the model, avoiding assumptions of strict linear associations. For variable pairs exhibiting multicollinearity, the indicator more strongly associated with the outcome or with greater clinical or prevention significance was preferentially retained to avoid over-adjustment[ 27 ]. The reported human brucellosis cases were followed a negative binomial distribution, which is appropriate for describing the occurrence of rare events within a defined spatio-temporal range and is a standard statistical model for disease surveillance data, effectively handling overdispersion in count data. To fully capture the spatio-temporal distribution characteristics of disease data, four nested Bayesian spatio-temporal negative binomial models were constructed: Model 1 was a spatial-only model, Model 2 was a temporal-only model, Model 3 was a spatio-temporal independent model, and Model 4 was a spatio-temporal interaction model. The expected number of cases was estimated based on monthly observational data from 103 counties and districts in Inner Mongolia Autonomous Region from 2014 to 2023. To account for population size differences across regions affecting incidence rates, the natural logarithm of population log(population) was used as an offset in the model, enabling the model to directly estimate relative changes in age-standardized incidence rates rather than absolute case numbers[ 28 ]. Given the marked geographic clustering of brucellosis, this study constructed a Queen's adjacency matrix covering 103 county-level units based on the standard administrative boundaries and geographic location of Inner Mongolia. Adjacency was defined as two county-level units sharing a common administrative boundary line. The matrix encompassed county-level administrative units from 12 leagues and cities in Inner Mongolia: Hohhot (9 units), Baotou (9 units), Wuhai (3 units), Chifeng (12 units), Tongliao (8 units), Ordos (9 units), Hulunbuir (15 units), Bayannur (7 units), Ulanqab (11 units), Hinggan League (6 units), Xilingol League (11 units), and Alxa League (3 units). To ensure the validity of the adjacency matrix, we verified that each county-level unit had at least one adjacent county and converted the matrix to the GRAPH format required by the INLA (Integrated Nested Laplace Approximation) software package for fitting the Besag conditional autoregressive (CAR) model. In model construction, the spatial effect was modeled using the Besag conditional autoregressive (CAR) model with an intrinsic CAR (iCAR) prior to capture spatial dependence among neighboring regions and smooth risk differences between adjacent geographic units. The temporal effect was specified through a first-order random walk (RW1) model combined with unstructured temporal effects to ensure smooth evolution of disease risk over time. The spatio-temporal interaction effect used a Type I interaction structure to simulate the joint action of unstructured spatial and temporal effects. All random effects components were assigned penalized complexity (PC) priors or Gamma (1, 0.01) hyperpriors, while fixed effects used weakly informative priors with precision 0.001. To further explore the nonlinear association between GDP and brucellosis incidence risk, we employed a semiparametric approach: first, the inla.group() function was used to group the original GDP data into 20 quantiles to reduce noise and improve curve smoothness; subsequently, the grouped GDP was included as a random effect in the Bayesian hierarchical model and smoothed using a second-order random walk (RW2) model with scaling enabled (scale.model=TRUE) to optimize model convergence. This approach retained nonparametric effect estimates of GDP on disease risk while avoiding overfitting and irregular fluctuations through smoothing. Model inference was based on the INLA algorithm, with integration calculations performed using the simplified Laplace strategy[ 29 ]. The deviance information criterion (DIC) and widely applicable information criterion (WAIC) were used to compare the goodness-of-fit among the four baseline models, and the model with the smallest DIC and WAIC values was selected as the optimal model. Posterior estimates of fixed effect regression coefficients were extracted from the optimal model to calculate relative risk (RR) and its 95% credible interval. A factor was considered to have a statistically significant association with disease risk if its credible interval did not include 1. Risk factors were defined as those with RR > 1, and protective factors as those with RR < 1. 2.3.4 Sensitivity Analysis To assess the robustness of model results, sensitivity analyses were conducted in three aspects. First, regarding prior distribution specification, we tested three different prior strengths for the precision parameter of fixed effects (0.001, 0.01, and 0.1) to examine the sensitivity of model inference to prior settings[ 30 ]. Second, concerning the treatment of the GDP variable, in addition to the equally spaced 20-group classification used in the baseline model, we further tested grouping numbers of 15 and 25 groups. By comparing the estimated relative risk values of various factors under different groupings, we examined the stability of the nonlinear representation. Third, regarding spatial structure definition, we systematically assessed the model's robustness to spatial dependence specification by comparing results from both the complete adjacency matrix and a partially trimmed adjacency matrix[ 31 ]. 2.3.4 Statistical Analysis This study employed ArcGIS 10.8 software for spatial data visualization. SATScan (version 10.0.2) was used to perform spatial scan statistics. Data preprocessing and statistical analysis were conducted using R 4.5.1 software, with Spearman correlation analysis performed through the Hmisc package and multicollinearity diagnostics through the car package. Construction and parameter estimation of the Bayesian spatio-temporal model were completed using the INLA package. To define spatial relationships, Queen's adjacency was used to construct the spatial weight matrix. The adjacency matrix was constructed based on Inner Mongolia's standard administrative boundaries using the actual adjacency relationships rather than ID ordering, and was verified using the spdep package. A 95% credible interval (CI) was used, representing the 2.5th to 97.5th percentile of the posterior distribution of estimated brucellosis incidence rates. A p-value < 0.05 was considered statistically significant, and all tests were two-tailed. 3. Results A total of 130,855 brucellosis cases were reported during 2014–2023. The overall incidence of brucellosis in Inner Mongolia Autonomous Region showed a trend of initial decline followed by increase and then decline again, reaching a peak of 21,838 cases in 2021 (Fig. 2 ). Furthermore, 72.61% of cases occurred between March and July, with 65.99% of cases concentrated in spring and summer. During the study period, 101 of the 103 counties in Inner Mongolia Autonomous Region reported brucellosis cases. The epidemic was predominantly concentrated in the central and eastern regions of Inner Mongolia. During 2014–2023, the three counties with the highest average annual incidence rates were Zhuozizhou County, Ulanqab City (4,310.11 per 100,000 population), Naiman Banner, Tongliao City (3,584.12 per 100,000 population), and Korqin Left Middle Banner, Tongliao City (3,338.81 per 100,000 population). Time series decomposition results (Fig. 3 ) showed that the original incidence (black curve) remained relatively stable with fluctuations during 2014–2018, followed by continuous increase after 2018 and reaching a peak during 2021–2022. The trend component (blue curve) further confirmed this long-term upward trend, entering a phase of significant increase after 2017, indicating the existence of persistent driving factors for brucellosis incidence. The seasonal component (green curve) displayed a clear 12-month periodic fluctuation, reflecting obvious seasonal characteristics of brucellosis incidence that were highly consistent with the annual cycle of livestock reproduction and pastoral activities. The residual component (red curve) fluctuated randomly around zero without apparent trends or periodicity, suggesting the model effectively extracted the main signals with only minor unexplained short-term random variations. Spearman correlation analysis was performed to examine environmental and socioeconomic factors associated with zoonotic disease transmission(Fig. 4 ). Multicollinearity diagnostics were first conducted, including precipitation, NDVI, cattle inventory, sheep inventory, population, and GDP variables in the analysis. Due to high correlation between cattle inventory and sheep inventory (r = 0.72), and stronger correlation between total livestock number and cattle inventory (r = 0.93), the livestock variable was ultimately removed to avoid multicollinearity, retaining individual livestock species for modeling. Additionally, correlations between other variables and the zoonotic disease were all relatively low (|r|<0.5). Specifically, GDP had correlations with most variables less than 0.11; wind speed showed weak negative correlations with meteorological composite variables (r=-0.35 to 0.49) but was essentially uncorrelated with other variables; atmospheric pressure (PS) showed weak positive correlation with NDVI (r = 0.17) but was essentially uncorrelated with livestock; population had correlations of 0.30 with GDP and 0.08 with precipitation, with very low correlations with other variables. These low-correlation variables could be safely included in multivariate regression models for analysis. Table 1 Bayesian spatiotemporal model comparison Model DIC pDIC ΔDIC WAIC pWAIC ΔWAIC Model 1: Spatial 52658.13 109.68 2838.21 52663.03 112.31 2801.92 Model 2: Temporal 56397.552 93.25 6577.63 56409.80 102.59 6548.69 Model 3: ST Independent 50041.67 187.25 221.75 50057.44 196.89 196.33 Model 4: ST Interaction ᵃ 49819.92 1519.51 0 49861.11 1342.28 0 Note:ᵃ Optimal model. DIC = Deviance Information Criterion; WAIC = Watanabe–Akaike Information Criterion; pDIC/pWAIC = effective number of parameters; ΔDIC/ΔWAIC = difference relative to the optimal model. Table 1 presents the results of variable selection and Bayesian spatio-temporal model comparison. The optimal model was Model 4, with cattle inventory, sheep inventory, mean temperature, precipitation, vegetation index, atmospheric pressure, wind speed, and GDP (nonlinear) as covariates. Figure 5 (A) shows that atmospheric pressure is the most important environmental factor influencing brucellosis incidence risk. Atmospheric pressure demonstrated a significant positive association with brucellosis risk (RR = 1.22, 95%,CI: 1.18–1.27). For every one standard deviation increase in atmospheric pressure (approximately 0.6 hPa), brucellosis risk increased by 22%. Wind speed was likewise an important risk factor. Wind speed was the strongest risk factor for brucellosis (RR = 1.21, 95%CI: 1.16–1.26). For every one standard deviation increase in wind speed (approximately 2.5 m/s), brucellosis risk increased by 21%. Sheep inventory demonstrated a significant positive association with brucellosis risk (RR = 1.15, 95%CI: 1.06–1.25). For every one standard deviation increase in sheep inventory (approximately 150,000 head), brucellosis risk increased by 15%. Cattle inventory also showed a significant positive association with brucellosis risk (RR = 1.11, 95%CI: 1.04–1.18). NDVI was the only significant protective factor (RR = 0.89, 95%CI: 0.87–0.91). For every one standard deviation increase in NDVI, brucellosis risk decreased by 11%. Temperature showed no significant statistical association with brucellosis incidence risk (95% CI included 1.0), suggesting that this factor has limited epidemiological importance in the current study region, or its effects may be indirect through other mediating factors. Figure 5 (B) further revealed the nonlinear association between GDP and brucellosis incidence risk, displaying the nonlinear GDP effect based on the actual adjacency matrix, revealing a significant inverted U-shaped relationship between GDP and brucellosis incidence risk. Specifically, at low GDP levels (0–40 million yuan), brucellosis relative risk remained between 0.85–0.95, reaching the lowest risk point in the 20–40 million yuan range (RR ≈ 0.80–0.90). As GDP entered the rapid growth phase of 40–100 million yuan, RR gradually increased to 1.35, with an accelerating trend during the increase. Brucellosis risk peaked at the 100–110 million yuan GDP level, with RR approximately 1.35–1.40, representing a 35%-40% increase in incidence risk relative to baseline. The incidence risk peaked at a GDP level of approximately 101.67 million RMB, reaching a maximum RR of 1.65 (95% CI: 1.26–2.17). As GDP further increased to the 110–120 million yuan phase, relative risk showed a declining trend, with RR decreasing to 1.25–1.30. The credible interval (shown in pink shading) was wider in high GDP regions, reflecting increased heterogeneity in environmental and social factors at the highly developed economic stage. Results of three sensitivity analyses are presented in Fig. 6 . Prior distribution sensitivity: Models were refitted using three different prior precision strengths (prec = 0.001/0.01/0.1), with RR estimates for the six risk factors varying by no more than 2%, and 95% credible intervals remaining essentially consistent, indicating that the model is insensitive to prior distribution specification. GDP grouping sensitivity: Three grouping strategies with n = 15, 20, and 25 were applied, with RR estimate fluctuations for each risk factor less than 5%, and the protective or risk effects of each variable remaining unchanged, indicating good stability of results to GDP grouping strategy. Adjacency matrix robustness: When comparing standard adjacency definition with the trimmed version, RR estimates for each risk factor were highly consistent, with DIC value differences between the two models not exceeding 5%, indicating that model conclusions are not affected by minor adjustments to the adjacency matrix. Comprehensive sensitivity analysis results demonstrate that the main findings of this study have good robustness and credibility. 4. Discussion This study conducted a systematic analysis of brucellosis cases in Inner Mongolia Autonomous Region during 2014–2023, revealing the epidemiological characteristics and associated factors of brucellosis in the region. Human brucellosis in the Inner Mongolia Autonomous Region exhibited a pronounced spring and summer seasonality, a temporal pattern consistent with the findings of Zhang et al. regarding the epidemic in Jiangsu Province. Spring and summer represent peak periods for livestock reproduction and breeding, during which large quantities of highly infectious materials including aborted fetuses, placentas, and amniotic fluid contaminate the environment, and frequent contact with these contaminated sources by herders significantly increases infection risk[ 32 ]. Notably, disease incidence experienced rapid increase during 2017–2021, reaching a peak in 2021 before declining. This fluctuation trend coincided with the accelerated period of intensive transformation of Inner Mongolia's livestock industry, revealing new challenges in disease control amid rapid economic development. Traditional epidemiological analysis methods often examine temporal trends or spatial distribution independently, making it difficult to fully reveal the spatio-temporal dynamics of disease risk and driving mechanisms[ 33 ]. Bayesian spatio-temporal models integrate spatial adjacency and temporal trends, utilizing prior information and hierarchical structures to address spatial autocorrelation, small-area estimation, and data sparsity problems, enabling simultaneous capture of spatial heterogeneity and temporal dynamics in disease risk. These methods have been widely applied in infectious disease epidemiology[ 34 ].This approach not only identifies high-risk areas and time periods but also quantifies the effects of environmental and socioeconomic factors on disease risk, providing scientific evidence for formulating targeted prevention and control strategies. Spatio-temporal interaction models incorporate interaction terms to simultaneously capture spatio-temporal change patterns in disease risk, more effectively integrating the effects of temporal continuity and spatial adjacency[ 35 ]. The spatio-temporal interaction model employed in this study demonstrated optimal performance in goodness-of-fit assessment indices (DIC and WAIC), confirming the importance of considering spatio-temporal interaction effects in analyzing brucellosis data. Based on this model, the analysis revealed that atmospheric pressure is an important environmental factor influencing brucellosis incidence risk, demonstrating significant positive association with brucellosis risk (RR = 1.22, 95%CI: 1.18–1.27), a finding with important epidemiological significance. Within certain ranges, high atmospheric pressure is usually accompanied by specific meteorological conditions that may affect brucellosis transmission through multiple mechanisms. For instance, studies using distributed lag nonlinear models (DLNM) have identified atmospheric pressure ranges of 789-793.5 hPa and lag periods of 0–18 days as intervals associated with increased brucellosis incidence[ 14 ]. High atmospheric pressure is typically associated with clear, dry weather, and this stable climate condition may favor pathogen persistence in the environment; meanwhile, elevated atmospheric pressure indicates more stable atmospheric conditions that, combined with suitable temperatures, may further promote disease development and transmission[ 36 ]. Wind speed was identified as one of the risk factors for brucellosis in this study (RR = 1.21, 95%CI: 1.16–1.26). This finding is consistent with previous research, reflecting the important impact of meteorological conditions on infectious disease transmission[ 37 ]. Strong wind conditions can disseminate Brucella -containing excreta and dust into aerosols, accelerating horizontal transmission within animal populations and increasing human inhalation infection risk; simultaneously, the wider dispersal range promotes contaminant spread among livestock, further expanding human exposure risk. Additionally, in strong wind weather, herders tend to confine animals indoors, increasing contact opportunities between humans and infected animals in enclosed spaces, thereby increasing the likelihood of human brucellosis infection [ 38 ]. Sheep inventory (RR = 1.15, 95%CI: 1.06–1.25) and cattle inventory (RR = 1.11, 95%CI: 1.04–1.18) were also important risk factors. This underscores the critical importance of sheep and cattle as primary sources of Brucella transmission. When abortion occurs in sheep and cattle herds, abortion products containing large quantities of pathogens contaminate the environment, and occupational populations become infected through skin wounds and mucosal contact during handling of aborted material, cleaning livestock housing, and disinfection procedures[ 39 ]. Brucella concentrations are particularly elevated in the uterus of infected pregnant animals, making aborted fetuses, placental membranes, and uterine secretions key sources of contamination. Furthermore, infected animals shed the pathogen continuously in milk throughout lactation, with many remaining lifelong shedders, sustaining environmental and foodborne transmission risks[ 40 , 41 ]. In Inner Mongolia, traditional pastoral farming systems result in high sheep and cattle inventory with frequent human-animal contact, increasing exposure risk. Additionally, surveillance for diseased animals in pastoral systems is relatively limited, with potentially infected lactating animals maintaining close contact with susceptible populations over prolonged periods, creating persistent transmission pressure[ 42 ]. By contrast, while modern intensive farming systems offer significant advantages in production efficiency and management standardization, inadequate control measures may actually increase disease clustering risk. In intensive farming systems, high-density husbandry, relatively enclosed environments, and complex animal movement networks mean that once pathogens enter a herd, rapid spread occurs within a short period, leading to group abortion events termed “abortion storms”[ 43 ]. Such clustered infections can produce environmental contamination far exceeding that from traditional farming systems, as a single abortion releases approximately 10^13 bacteria into the environment, sufficient to infect 60,000 to 600,000 susceptible females[ 44 ]. Furthermore, crowded conditions in intensive facilities further accelerate pathogen transmission risk. In some intensive operations, staff lack adequate biosafety awareness, control measures are poorly implemented, and stress associated with high-density conditions increases infection susceptibility, all substantially elevating occupational population infection risk[ 40 , 45 ]. Therefore, strengthening vaccination, regular screening, and isolation of diseased animals in sheep and cattle herds are important measures to reduce human disease incidence. Systematic brucellosis control strategies should include animal vaccination, environmental sanitation and disinfection, and elimination or isolation of infected animals. Vaccination programs should be designed scientifically based on local epidemiological conditions to provide sustained protection to susceptible livestock populations. Vaccination as a standalone measure has proven effective, with decreased disease incidence in herds directly correlating with the proportion of vaccinated animals[ 41 , 46 ]. Simultaneously, areas potentially contaminated by pathogens should undergo regular thorough cleaning and disinfection. For high-risk occupational groups such as farm workers and veterinarians, enhanced protective training, provision of personal protective equipment (such as rubber gloves and eye protection), and standardized operational procedures are essential. Additionally, establishing animal traceability systems, improving livestock health records, and strengthening coordinated reporting with health departments to form collaborative mechanisms between veterinary and human disease control are necessary to effectively prevent human-to-human transmission of brucellosis[ 47 ]. NDVI demonstrated a protective effect, with brucellosis incidence risk decreasing by 11% for every one standard deviation increase in vegetation coverage (RR = 0.89, 95%CI: 0.87–0.91). This finding requires interpretation in the context of the nonlinear relationship between NDVI and brucellosis risk. Similar studies have confirmed significant negative correlation between NDVI and brucellosis risk, contrasting with positive correlations reported in Xinjiang and Inner Mongolia studies, which indicated that dense vegetation can protect Brucella from ultraviolet radiation and dry environmental conditions[ 10 ]. Research in Xinjiang found an inverted U-shaped relationship between NDVI and incidence, with risk reaching a peak at NDVI value of 0.4, after which risk declined as vegetation coverage increased[ 48 ]. In this study, NDVI demonstrated a protective effect, possibly suggesting that the ongoing desert prevention and ecological restoration projects in Inner Mongolia have not only improved environmental quality but may also indirectly reduced the disease burden of brucellosis. The Inner Mongolia region may be at the declining phase of the inverted U-shaped curve, where the dilution effect of the ecosystem begins to function, with high vegetation coverage limiting human-animal contact frequency. The study found no significant association between temperature and brucellosis incidence. Although temperature may indirectly affect pathogen survival through changes in environmental humidity, this effect may be masked by other stronger factors, or its effects may be indirectly manifested through variables such as vegetation index. One of the most important findings of this study is that GDP demonstrates a significant nonlinear inverted U-shaped relationship with brucellosis incidence risk, rather than the traditional assumption of monotonic decline, challenging the conventional hypothesis that “economic development necessarily leads to monotonic reduction in disease risk”[ 49 ]. At the low GDP stage (0–40 million yuan), regions experience underdeveloped economies with economic structures dominated by traditional agriculture and livestock farming. Although limited disease control investment and weak diagnostic capacity may lead to underestimation of disease cases, the low population density and low human-animal contact intensity result in relatively low natural infection risk, thus maintaining overall low disease incidence[ 50 , 51 ]. Entering the middle GDP stage (40–100 million yuan), regions experience rapid economic development, with traditional livestock farming remaining important but at substantially increased intensive levels[ 52 ]。Livestock herd size and numbers increase dramatically, and economic development-driven population movement and increased market linkages create favorable conditions for disease transmission; more importantly, diagnostic capacity improves, and with higher GDP, sanitation conditions improve, medical accessibility increases, thus enhancing disease detection rates[ 53 ]. Intensive farming may actually exacerbate disease clustering transmission risk, making this stage a high-risk period[ 54 ]. Upon entering the high GDP stage (≥ 110 million yuan), regions are typically more developed cities and prefecture-level municipalities within the autonomous region, with economic structures undergoing complete optimization and upgrading. Although livestock farming persists, it has achieved standardized and regulated husbandry practices with enhanced biosafety awareness and high vaccination coverage rates; quarantine systems are well-established, and medical and health resources are abundant, with significantly improved public health awareness and occupational protection measures, ultimately resulting in substantial reduction and stability of disease risk[ 55 ]. These findings are consistent with perspectives from international literature. Agricultural intensification and environmental change are closely associated with the emergence of zoonotic diseases[ 49 ]. A research indicates that since 1940, agricultural drivers have been associated with more than 25% of emerging infectious diseases and more than 50% of zoonotic diseases globally, with this proportion further increasing during periods of agricultural expansion and intensification[ 56 ]. Therefore, future brucellosis control efforts in Inner Mongolia should focus on regions with moderate economic development, establishing prevention and control systems aligned with industrial transformation at this critical stage. Our study has several limitations. The use of an ecological study design prevents inference of individual-level causal relationships and did not include potential confounding factors such as vaccination rates and occupational protection measures. Future research should incorporate individual-level data to further verify the mechanisms through which socioeconomic factors influence disease risk, and explore the effects of climate change on long-term epidemic trends of brucellosis, providing evidence for formulating more scientifically sound and effective prevention and control strategies. 5. Conclusion This study revealed an inverted U-shaped nonlinear relationship between GDP and disease incidence risk. Atmospheric pressure, wind speed, and sheep and cattle inventory were identified as major risk factors, while vegetation coverage provided certain inhibitory effects on disease transmission. These findings indicate that brucellosis control requires full recognition of factor differences across regions and cannot apply a uniform model; rather, prevention and control measures should be tailored based on each region’s economic development stage, industrial structure characteristics, and ecological environmental conditions. Effective control of brucellosis is a systematic undertaking requiring the establishment of comprehensive prevention and control systems encompassing source control, transmission blocking, and surveillance and early warning. Future research should integrate individual-level data to further verify the mechanisms through which socioeconomic factors influence disease risk, explore the effects of climate change on long-term epidemic trends, and provide a more solid scientific foundation for achieving staged control and eventual elimination of brucellosis. Declarations Clinical trial number Not applicable. Ethics approval and consent to participate This study did not involve any human intervention or clinical trials. The use of human brucellosis case data was based on routine public health surveillance activities in China. According to national regulations, such surveillance data are exempt from institutional ethics review. Therefore, ethics approval was not required for this study. As all data were anonymized and contained no personal identifiers or private health information, the requirement for informed consent was also deemed unnecessary. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests All authors have no competing interests to declare. Funding No funding was received for this study. Author Contribution QZ: Writing-review & editing, Writing-original draft, Software, Data curation, Methodology, Visualization, Formal analysis, Conceptualization. NT: Data curation, Validation, Methodology, Supervision, Conceptualization, Resources. HS: Validation, Methodology, Supervision. RQ: Data curation. ZZ: Data curation. BL: Data curation. DZ: Data curation. YX: Validation, Methodology, Supervision, Conceptualization. YW: Validation, Methodology, Supervision. ZZ: Validation, Methodology, Supervision. 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Prevention and Control Center","correspondingAuthor":false,"prefix":"","firstName":"Zhongbing","middleName":"","lastName":"Zhang","suffix":""},{"id":618951364,"identity":"c7677dbc-e3ab-4047-940d-7b8aa60cc5d6","order_by":10,"name":"Wenyi Zhang","email":"","orcid":"","institution":"Chinese Center For Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-14 03:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9119326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9119326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106418526,"identity":"337244d0-78f1-404c-85dc-a21a1891dda7","added_by":"auto","created_at":"2026-04-08 10:54:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232705,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Regional Location and County-Level Administrative Divisions of Inner Mongolia Autonomous Region\u003c/p\u003e\n\u003cp\u003eNote: Drawing Review Number: GS(2024)0650\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/78109c70b171397872adb841.png"},{"id":106418311,"identity":"6547c1f4-5214-4738-a1f4-0be3c7a86760","added_by":"auto","created_at":"2026-04-08 10:53:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17378,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Reported Cases and Incidence Rate of Human Brucellosis in Inner Mongolia Autonomous Region, 2014-2023 (N=130,855)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/5930b788da56c350200e9e69.png"},{"id":106418336,"identity":"e1cc992c-6339-4430-af6d-3e66f92ae12c","added_by":"auto","created_at":"2026-04-08 10:53:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21582,"visible":true,"origin":"","legend":"\u003cp\u003eTime Series Decomposition of Brucellosis Incidence in Inner Mongolia, 2014-2024\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/843c14eef9c8b366fd2045f3.png"},{"id":106418359,"identity":"c1db56de-e35a-4565-a0d8-36e526d278da","added_by":"auto","created_at":"2026-04-08 10:53:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125425,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman Rank Correlation Matrix of Key Variables\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/bd162c4a97ac74f1ad59547b.png"},{"id":106418211,"identity":"1cd3add3-7d1a-4589-a72b-be626f5e1896","added_by":"auto","created_at":"2026-04-08 10:53:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108456,"visible":true,"origin":"","legend":"\u003cp\u003eFactors influencing brucellosis based on the Bayesian spatiotemporal interaction model in Inner Mongolia，2014-2023. (A) Relative risks and 95% BCI for covariates with fixed effects in the model. (B) Nonlinear relationship between GDP and brucellosis in the model\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/3fd7e4c12e45a60874940644.png"},{"id":106418334,"identity":"4352386f-e1d8-45b7-949c-7d90697c4f9f","added_by":"auto","created_at":"2026-04-08 10:53:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":205553,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity Analysis Results of the Bayesian Spatiotemporal Model\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/5f292854b459aaedafadab7d.png"},{"id":106419466,"identity":"966487f0-bbd2-49a3-b550-24d9edd5319c","added_by":"auto","created_at":"2026-04-08 10:58:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1356667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9119326/v1/c4c4b63a-3ff6-46c4-97fb-b5972a4a266b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing Factors of Human Brucellosis in Inner Mongolia, China, 2014– 2023: A Bayesian Spatio-Temporal Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBrucellosis is a zoonotic infectious disease caused by genus \u003cem\u003eBrucella\u003c/em\u003e. The World Health Organization (WHO) has classified it as a \u0026ldquo;neglected zoonotic disease\u0026rdquo; that requires priority control, posing a continuous threat to public health and livestock production worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease is transmitted to humans mainly through direct contact with infected animals and their tissues, consumption of unpasteurized dairy products, or inhalation of contaminated aerosols. Clinical manifestations include undulant fever, excessive sweating, fatigue, and muscle and joint pain. Some patients develop chronic infection, which can lead to damage to multiple organs including bone joints and the nervous system, seriously affecting patients\u0026rsquo; work capacity and quality of life[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to WHO statistics, more than 500,000 new cases of brucellosis are reported globally each year. However, due to widespread underdiagnosis and underreporting, the actual number of cases may be much higher than official reports. Brucellosis not only causes physical suffering to patients but also creates substantial economic burdens, including medical expenses, loss of labor productivity, and livestock production losses[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBrucellosis outbreaks have been reported in over 170 countries and regions to date, ranking it among the most significant global zoonoses[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. China is a high-incidence country for brucellosis, with a complex epidemic history. China is a high‑incidence country for brucellosis with a complex epidemic history. From the 1950s to the 1980s, comprehensive control measures\u0026mdash;including large‑scale animal immunization and culling of infected animals\u0026mdash;effectively brought the epidemic under control, and the incidence rate fell to a historic low of 0.028 per 100,000 population in 1993[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, since the 1990s, rapid development of the livestock industry coupled with the relaxation of disease prevention and control efforts has led to an epidemic resurgence. The incidence rate has increased continuously, reaching 5.06 per 100,000 in 2021, and the geographic range of the disease has expanded to all provinces in China[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Inner Mongolia Autonomous Region is the most severely affected area in China, reporting 88,000 cases between 2019 and 2023, accounting for 28.87% of all cases nationwide. The average incidence rate is as high as 71.88 per 100,000, far exceeding the national average. In some high-incidence years, the region\u0026rsquo;s cases represent 40%-50% of the national total, ranking first nationwide[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This region is located in the northern border of China with vast grasslands and abundant livestock resources. Livestock production is the main income source for the local residents, providing favorable natural and socioeconomic conditions for brucellosis transmission. With economic development and modernization of livestock production, large-scale farming has expanded rapidly, increasing herd density and livestock movement frequency[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, Inner Mongolia represents a high-priority area for studying the epidemiology of brucellosis to better inform targeted prevention and control strategies.\u003c/p\u003e \u003cp\u003eGrowing evidence indicates that brucellosis transmission is influenced by multiple factors including natural environmental conditions, socioeconomic development level, livestock production models, and disease control capacity, showing strong spatial and temporal characteristics[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meteorological factors such as temperature and precipitation affect disease transmission by influencing the survival time of \u003cem\u003eBrucella\u003c/em\u003e in the environment, the reproductive cycle of animal hosts, and human activity patterns[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the factors that affect brucellosis incidence vary greatly across different regions. In other words, the effects of environmental factors on brucellosis incidence remain inconsistent across areas. Therefore, it is necessary to study the potential driving effects and interactions between environmental risk factors and brucellosis transmission in regions with different incidence rates, geographic features, and climate conditions, in order to improve risk assessment and management. Additionally, brucellosis shows significant clustering in both time and space. High-incidence areas remain relatively stable but also undergo dynamic changes, and seasonal fluctuations are obvious. These characteristics suggest complex spatial and temporal dependencies in disease transmission[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, most previous studies have adopted traditional epidemiological methods that analyze temporal and spatial dimensions separately, making it difficult to capture the dynamic characteristics of spatial-temporal interactions[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. When analyzing environmental risk factors, simple correlation analysis or linear regression methods are often used, which fail to account for spatial autocorrelation in the data, potentially leading to biased statistical inference[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Bayesian spatio-temporal models effectively address these methodological limitations[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By employing a hierarchical structure, these models simultaneously integrate temporal trends, spatial patterns, and environmental variables. This approach allows for the flexible inclusion of multiple covariates to quantify their specific effects on disease risk. Furthermore, the models provide robust uncertainty estimates through posterior inference[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In recent years, Bayesian spatio-temporal models have been widely applied to spatial-temporal epidemiological studies of various infectious diseases such as malaria and dengue fever, showing good prospects[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, their application in brucellosis research in China remains limited, especially with the lack of systematic spatial-temporal analysis and quantitative assessment of environmental risk factors in Inner Mongolia, one of the most severely affected regions.\u003c/p\u003e \u003cp\u003eRecent spatio-temporal analytical studies in Inner Mongolia have revealed that brucellosis cases in this region not only exhibit distinct temporal patterns and spatial clustering, but also experience changing epidemic trends in response to economic development, climate change, and shifts in livestock industry structure[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, the objective of this study is to systematically analyze the epidemiological characteristics of brucellosis and quantify the effects of meteorological, ecological, and socioeconomic factors on disease risk using a Bayesian spatiotemporal interaction model, based on 2014\u0026ndash;2023 brucellosis surveillance data from Inner Mongolia. The findings will provide new evidence for understanding the epidemiology and underlying drivers of brucellosis in the context of rapid socioeconomic transition, and offer a scientific basis for developing locally tailored, adaptive prevention and control strategies in Inner Mongolia and across China.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eInner Mongolia Autonomous Region is located in northern China. The total area of the region is 1.183\u0026nbsp;million square kilometers, accounting for 12.3% of China's land area, ranking third among all provinces, municipalities, and autonomous regions in the country[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As of the end of 2024, the region had a permanent population of 23.88\u0026nbsp;million. The region is divided into 9 prefecture-level cities and 3 leagues, comprising a total of 103 counties and districts (Fig.\u0026nbsp;1). In 2024, Inner Mongolia had the highest cattle population (9.501\u0026nbsp;million head) and sheep population (69.477\u0026nbsp;million head) in the nation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Sources and Processing\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Brucellosis Case Data\u003c/h2\u003e \u003cp\u003eHuman brucellosis is a Category B notifiable infectious disease in China and must be reported through the China Information System for Disease Control and Prevention(CISDCP)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Monthly brucellosis cases at the county and district levels in Inner Mongolia Autonomous Region from 2014 to 2023 were obtained from the Inner Mongolia Disease Prevention and Control Center. Brucellosis cases were confirmed strictly according to the diagnostic criteria and principles specified in the Industry Standard for the Diagnosis of Brucellosis of the People's Republic of China (WS 269\u0026ndash;2007/WS 269\u0026ndash;2019). This protocol integrated epidemiological exposure history, clinical manifestations, and laboratory test results[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Meteorological Data\u003c/h2\u003e \u003cp\u003eMonthly meteorological data for Inner Mongolia during the study period were obtained from the China Meteorological Data Sharing Service System (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn/\u003c/span\u003e\u003cspan address=\"http://data.cma.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The extracted meteorological variables included precipitation (Prec, mm), mean temperature (Temperature, \u0026deg;C), specific humidity (Specific Humidity, g/kg), and wind speed (Wind Speed, m/s). The data processing method was as follows: Kriging interpolation was first applied to the meteorological station data to perform spatial interpolation, generating county-level monthly meteorological indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Remote Sensing Data\u003c/h2\u003e \u003cp\u003eSurface elevation were extracted from the Digital Elevation Model (DEM) provided by the Shuttle Radar Topography Mission (SRTM) with a resolution of 90 m. The processing method was as follows: the mean value of DEM grids was calculated at the county level to obtain the county-level average elevation (Elevation, m) indicator. Normalized Difference Vegetation Index (NDVI) data were obtained from the Land Atmosphere Archive and Distribution System (LAADS DAAC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ladsweb.modaps.eosdis.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://ladsweb.modaps.eosdis.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using global monthly composite products at 1 km resolution. Data processing included three steps. First, negative values and outliers were removed. Second, the maximum value composite (MVC) method was applied for each month to mitigate the effects of cloud cover and atmospheric interference. Finally, the processed NDVI values were averaged at the county level to generate monthly NDVI indicators for each county.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Socioeconomic and Livestock Data\u003c/h2\u003e \u003cp\u003eCounty-level socioeconomic data were obtained from the Statistical Yearbook of Inner Mongolia Autonomous Region compiled by the Statistics Bureau of Inner Mongolia Autonomous Region, including: year-end population, annual gross domestic product (GDP), and year-end total livestock numbers and cattle, sheep, and pig inventory(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tjnjw.com/\u003c/span\u003e\u003cspan address=\"http://www.tjnjw.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These data reflect county-level population scale, economic development level, and major livestock population size, which are closely associated with brucellosis epidemic risk.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Time Series Decomposition Model\u003c/h2\u003e \u003cp\u003eTime series decomposition model was applied to analyze the temporal dynamics of brucellosis incidence in Inner Mongolia from 2014 to 2023. The original series was decomposed into three components: trend, seasonal, and residual. The trend component captures the long-term underlying change in incidence, the seasonal component reflects recurring intra-annual fluctuations, and the residual represents the unexplained random variation after accounting for trend and seasonality. This decomposition enabled a systematic identification of the long-term evolution and seasonal transmission patterns of brucellosis in the region[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Bayesian Spatiotemporal Model\u003c/h2\u003e \u003cp\u003eTo avoid multicollinearity, Spearman rank correlation analysis was performed to assess correlations among candidate covariates, and covariates with absolute Spearman correlation coefficients less than 0.7 were retained for inclusion in the Bayesian multivariate model. All continuous variables were standardized using z-score normalization to have a mean of 0 and standard deviation of 1, facilitating uniform interpretation of model parameters. GDP was categorized into 20 groups to capture potential nonlinear relationships between economic development and disease risk in the model, avoiding assumptions of strict linear associations. For variable pairs exhibiting multicollinearity, the indicator more strongly associated with the outcome or with greater clinical or prevention significance was preferentially retained to avoid over-adjustment[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe reported human brucellosis cases were followed a negative binomial distribution, which is appropriate for describing the occurrence of rare events within a defined spatio-temporal range and is a standard statistical model for disease surveillance data, effectively handling overdispersion in count data. To fully capture the spatio-temporal distribution characteristics of disease data, four nested Bayesian spatio-temporal negative binomial models were constructed: Model 1 was a spatial-only model, Model 2 was a temporal-only model, Model 3 was a spatio-temporal independent model, and Model 4 was a spatio-temporal interaction model. The expected number of cases was estimated based on monthly observational data from 103 counties and districts in Inner Mongolia Autonomous Region from 2014 to 2023. To account for population size differences across regions affecting incidence rates, the natural logarithm of population log(population) was used as an offset in the model, enabling the model to directly estimate relative changes in age-standardized incidence rates rather than absolute case numbers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the marked geographic clustering of brucellosis, this study constructed a Queen's adjacency matrix covering 103 county-level units based on the standard administrative boundaries and geographic location of Inner Mongolia. Adjacency was defined as two county-level units sharing a common administrative boundary line. The matrix encompassed county-level administrative units from 12 leagues and cities in Inner Mongolia: Hohhot (9 units), Baotou (9 units), Wuhai (3 units), Chifeng (12 units), Tongliao (8 units), Ordos (9 units), Hulunbuir (15 units), Bayannur (7 units), Ulanqab (11 units), Hinggan League (6 units), Xilingol League (11 units), and Alxa League (3 units). To ensure the validity of the adjacency matrix, we verified that each county-level unit had at least one adjacent county and converted the matrix to the GRAPH format required by the INLA (Integrated Nested Laplace Approximation) software package for fitting the Besag conditional autoregressive (CAR) model.\u003c/p\u003e \u003cp\u003eIn model construction, the spatial effect was modeled using the Besag conditional autoregressive (CAR) model with an intrinsic CAR (iCAR) prior to capture spatial dependence among neighboring regions and smooth risk differences between adjacent geographic units. The temporal effect was specified through a first-order random walk (RW1) model combined with unstructured temporal effects to ensure smooth evolution of disease risk over time. The spatio-temporal interaction effect used a Type I interaction structure to simulate the joint action of unstructured spatial and temporal effects. All random effects components were assigned penalized complexity (PC) priors or Gamma (1, 0.01) hyperpriors, while fixed effects used weakly informative priors with precision 0.001. To further explore the nonlinear association between GDP and brucellosis incidence risk, we employed a semiparametric approach: first, the inla.group() function was used to group the original GDP data into 20 quantiles to reduce noise and improve curve smoothness; subsequently, the grouped GDP was included as a random effect in the Bayesian hierarchical model and smoothed using a second-order random walk (RW2) model with scaling enabled (scale.model=TRUE) to optimize model convergence. This approach retained nonparametric effect estimates of GDP on disease risk while avoiding overfitting and irregular fluctuations through smoothing.\u003c/p\u003e \u003cp\u003eModel inference was based on the INLA algorithm, with integration calculations performed using the simplified Laplace strategy[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The deviance information criterion (DIC) and widely applicable information criterion (WAIC) were used to compare the goodness-of-fit among the four baseline models, and the model with the smallest DIC and WAIC values was selected as the optimal model. Posterior estimates of fixed effect regression coefficients were extracted from the optimal model to calculate relative risk (RR) and its 95% credible interval. A factor was considered to have a statistically significant association with disease risk if its credible interval did not include 1. Risk factors were defined as those with RR\u0026thinsp;\u0026gt;\u0026thinsp;1, and protective factors as those with RR\u0026thinsp;\u0026lt;\u0026thinsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of model results, sensitivity analyses were conducted in three aspects. First, regarding prior distribution specification, we tested three different prior strengths for the precision parameter of fixed effects (0.001, 0.01, and 0.1) to examine the sensitivity of model inference to prior settings[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Second, concerning the treatment of the GDP variable, in addition to the equally spaced 20-group classification used in the baseline model, we further tested grouping numbers of 15 and 25 groups. By comparing the estimated relative risk values of various factors under different groupings, we examined the stability of the nonlinear representation. Third, regarding spatial structure definition, we systematically assessed the model's robustness to spatial dependence specification by comparing results from both the complete adjacency matrix and a partially trimmed adjacency matrix[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThis study employed ArcGIS 10.8 software for spatial data visualization. SATScan (version 10.0.2) was used to perform spatial scan statistics. Data preprocessing and statistical analysis were conducted using R 4.5.1 software, with Spearman correlation analysis performed through the Hmisc package and multicollinearity diagnostics through the car package. Construction and parameter estimation of the Bayesian spatio-temporal model were completed using the INLA package. To define spatial relationships, Queen's adjacency was used to construct the spatial weight matrix. The adjacency matrix was constructed based on Inner Mongolia's standard administrative boundaries using the actual adjacency relationships rather than ID ordering, and was verified using the spdep package. A 95% credible interval (CI) was used, representing the 2.5th to 97.5th percentile of the posterior distribution of estimated brucellosis incidence rates. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and all tests were two-tailed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 130,855 brucellosis cases were reported during 2014\u0026ndash;2023. The overall incidence of brucellosis in Inner Mongolia Autonomous Region showed a trend of initial decline followed by increase and then decline again, reaching a peak of 21,838 cases in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, 72.61% of cases occurred between March and July, with 65.99% of cases concentrated in spring and summer. During the study period, 101 of the 103 counties in Inner Mongolia Autonomous Region reported brucellosis cases. The epidemic was predominantly concentrated in the central and eastern regions of Inner Mongolia. During 2014\u0026ndash;2023, the three counties with the highest average annual incidence rates were Zhuozizhou County, Ulanqab City (4,310.11 per 100,000 population), Naiman Banner, Tongliao City (3,584.12 per 100,000 population), and Korqin Left Middle Banner, Tongliao City (3,338.81 per 100,000 population).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTime series decomposition results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that the original incidence (black curve) remained relatively stable with fluctuations during 2014\u0026ndash;2018, followed by continuous increase after 2018 and reaching a peak during 2021\u0026ndash;2022. The trend component (blue curve) further confirmed this long-term upward trend, entering a phase of significant increase after 2017, indicating the existence of persistent driving factors for brucellosis incidence. The seasonal component (green curve) displayed a clear 12-month periodic fluctuation, reflecting obvious seasonal characteristics of brucellosis incidence that were highly consistent with the annual cycle of livestock reproduction and pastoral activities. The residual component (red curve) fluctuated randomly around zero without apparent trends or periodicity, suggesting the model effectively extracted the main signals with only minor unexplained short-term random variations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman correlation analysis was performed to examine environmental and socioeconomic factors associated with zoonotic disease transmission(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Multicollinearity diagnostics were first conducted, including precipitation, NDVI, cattle inventory, sheep inventory, population, and GDP variables in the analysis. Due to high correlation between cattle inventory and sheep inventory (r\u0026thinsp;=\u0026thinsp;0.72), and stronger correlation between total livestock number and cattle inventory (r\u0026thinsp;=\u0026thinsp;0.93), the livestock variable was ultimately removed to avoid multicollinearity, retaining individual livestock species for modeling. Additionally, correlations between other variables and the zoonotic disease were all relatively low (|r|\u0026lt;0.5). Specifically, GDP had correlations with most variables less than 0.11; wind speed showed weak negative correlations with meteorological composite variables (r=-0.35 to 0.49) but was essentially uncorrelated with other variables; atmospheric pressure (PS) showed weak positive correlation with NDVI (r\u0026thinsp;=\u0026thinsp;0.17) but was essentially uncorrelated with livestock; population had correlations of 0.30 with GDP and 0.08 with precipitation, with very low correlations with other variables. These low-correlation variables could be safely included in multivariate regression models for analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBayesian spatiotemporal model comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epDIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔDIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epWAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔWAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1: Spatial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52658.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2838.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52663.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2801.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2: Temporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56397.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6577.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56409.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6548.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3: ST Independent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50041.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50057.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e196.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e196.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4: ST Interaction ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49819.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1519.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49861.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1342.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote:ᵃ Optimal model. DIC\u0026thinsp;=\u0026thinsp;Deviance Information Criterion; WAIC\u0026thinsp;=\u0026thinsp;Watanabe\u0026ndash;Akaike Information Criterion; pDIC/pWAIC\u0026thinsp;=\u0026thinsp;effective number of parameters; ΔDIC/ΔWAIC\u0026thinsp;=\u0026thinsp;difference relative to the optimal model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of variable selection and Bayesian spatio-temporal model comparison. The optimal model was Model 4, with cattle inventory, sheep inventory, mean temperature, precipitation, vegetation index, atmospheric pressure, wind speed, and GDP (nonlinear) as covariates. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e(A) shows that atmospheric pressure is the most important environmental factor influencing brucellosis incidence risk. Atmospheric pressure demonstrated a significant positive association with brucellosis risk (RR\u0026thinsp;=\u0026thinsp;1.22, 95%,CI: 1.18\u0026ndash;1.27). For every one standard deviation increase in atmospheric pressure (approximately 0.6 hPa), brucellosis risk increased by 22%. Wind speed was likewise an important risk factor. Wind speed was the strongest risk factor for brucellosis (RR\u0026thinsp;=\u0026thinsp;1.21, 95%CI: 1.16\u0026ndash;1.26). For every one standard deviation increase in wind speed (approximately 2.5 m/s), brucellosis risk increased by 21%. Sheep inventory demonstrated a significant positive association with brucellosis risk (RR\u0026thinsp;=\u0026thinsp;1.15, 95%CI: 1.06\u0026ndash;1.25). For every one standard deviation increase in sheep inventory (approximately 150,000 head), brucellosis risk increased by 15%. Cattle inventory also showed a significant positive association with brucellosis risk (RR\u0026thinsp;=\u0026thinsp;1.11, 95%CI: 1.04\u0026ndash;1.18). NDVI was the only significant protective factor (RR\u0026thinsp;=\u0026thinsp;0.89, 95%CI: 0.87\u0026ndash;0.91). For every one standard deviation increase in NDVI, brucellosis risk decreased by 11%. Temperature showed no significant statistical association with brucellosis incidence risk (95% CI included 1.0), suggesting that this factor has limited epidemiological importance in the current study region, or its effects may be indirect through other mediating factors. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e(B) further revealed the nonlinear association between GDP and brucellosis incidence risk, displaying the nonlinear GDP effect based on the actual adjacency matrix, revealing a significant inverted U-shaped relationship between GDP and brucellosis incidence risk. Specifically, at low GDP levels (0\u0026ndash;40\u0026nbsp;million yuan), brucellosis relative risk remained between 0.85\u0026ndash;0.95, reaching the lowest risk point in the 20\u0026ndash;40\u0026nbsp;million yuan range (RR\u0026thinsp;\u0026asymp;\u0026thinsp;0.80\u0026ndash;0.90). As GDP entered the rapid growth phase of 40\u0026ndash;100\u0026nbsp;million yuan, RR gradually increased to 1.35, with an accelerating trend during the increase. Brucellosis risk peaked at the 100\u0026ndash;110\u0026nbsp;million yuan GDP level, with RR approximately 1.35\u0026ndash;1.40, representing a 35%-40% increase in incidence risk relative to baseline. The incidence risk peaked at a GDP level of approximately 101.67\u0026nbsp;million RMB, reaching a maximum RR of 1.65 (95% CI: 1.26\u0026ndash;2.17). As GDP further increased to the 110\u0026ndash;120\u0026nbsp;million yuan phase, relative risk showed a declining trend, with RR decreasing to 1.25\u0026ndash;1.30. The credible interval (shown in pink shading) was wider in high GDP regions, reflecting increased heterogeneity in environmental and social factors at the highly developed economic stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults of three sensitivity analyses are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Prior distribution sensitivity: Models were refitted using three different prior precision strengths (prec\u0026thinsp;=\u0026thinsp;0.001/0.01/0.1), with RR estimates for the six risk factors varying by no more than 2%, and 95% credible intervals remaining essentially consistent, indicating that the model is insensitive to prior distribution specification. GDP grouping sensitivity: Three grouping strategies with n\u0026thinsp;=\u0026thinsp;15, 20, and 25 were applied, with RR estimate fluctuations for each risk factor less than 5%, and the protective or risk effects of each variable remaining unchanged, indicating good stability of results to GDP grouping strategy. Adjacency matrix robustness: When comparing standard adjacency definition with the trimmed version, RR estimates for each risk factor were highly consistent, with DIC value differences between the two models not exceeding 5%, indicating that model conclusions are not affected by minor adjustments to the adjacency matrix. Comprehensive sensitivity analysis results demonstrate that the main findings of this study have good robustness and credibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducted a systematic analysis of brucellosis cases in Inner Mongolia Autonomous Region during 2014\u0026ndash;2023, revealing the epidemiological characteristics and associated factors of brucellosis in the region. Human brucellosis in the Inner Mongolia Autonomous Region exhibited a pronounced spring and summer seasonality, a temporal pattern consistent with the findings of Zhang et al. regarding the epidemic in Jiangsu Province. Spring and summer represent peak periods for livestock reproduction and breeding, during which large quantities of highly infectious materials including aborted fetuses, placentas, and amniotic fluid contaminate the environment, and frequent contact with these contaminated sources by herders significantly increases infection risk[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Notably, disease incidence experienced rapid increase during 2017\u0026ndash;2021, reaching a peak in 2021 before declining. This fluctuation trend coincided with the accelerated period of intensive transformation of Inner Mongolia's livestock industry, revealing new challenges in disease control amid rapid economic development.\u003c/p\u003e \u003cp\u003eTraditional epidemiological analysis methods often examine temporal trends or spatial distribution independently, making it difficult to fully reveal the spatio-temporal dynamics of disease risk and driving mechanisms[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Bayesian spatio-temporal models integrate spatial adjacency and temporal trends, utilizing prior information and hierarchical structures to address spatial autocorrelation, small-area estimation, and data sparsity problems, enabling simultaneous capture of spatial heterogeneity and temporal dynamics in disease risk. These methods have been widely applied in infectious disease epidemiology[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].This approach not only identifies high-risk areas and time periods but also quantifies the effects of environmental and socioeconomic factors on disease risk, providing scientific evidence for formulating targeted prevention and control strategies. Spatio-temporal interaction models incorporate interaction terms to simultaneously capture spatio-temporal change patterns in disease risk, more effectively integrating the effects of temporal continuity and spatial adjacency[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The spatio-temporal interaction model employed in this study demonstrated optimal performance in goodness-of-fit assessment indices (DIC and WAIC), confirming the importance of considering spatio-temporal interaction effects in analyzing brucellosis data. Based on this model, the analysis revealed that atmospheric pressure is an important environmental factor influencing brucellosis incidence risk, demonstrating significant positive association with brucellosis risk (RR\u0026thinsp;=\u0026thinsp;1.22, 95%CI: 1.18\u0026ndash;1.27), a finding with important epidemiological significance. Within certain ranges, high atmospheric pressure is usually accompanied by specific meteorological conditions that may affect brucellosis transmission through multiple mechanisms. For instance, studies using distributed lag nonlinear models (DLNM) have identified atmospheric pressure ranges of 789-793.5 hPa and lag periods of 0\u0026ndash;18 days as intervals associated with increased brucellosis incidence[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. High atmospheric pressure is typically associated with clear, dry weather, and this stable climate condition may favor pathogen persistence in the environment; meanwhile, elevated atmospheric pressure indicates more stable atmospheric conditions that, combined with suitable temperatures, may further promote disease development and transmission[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Wind speed was identified as one of the risk factors for brucellosis in this study (RR\u0026thinsp;=\u0026thinsp;1.21, 95%CI: 1.16\u0026ndash;1.26). This finding is consistent with previous research, reflecting the important impact of meteorological conditions on infectious disease transmission[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Strong wind conditions can disseminate \u003cem\u003eBrucella\u003c/em\u003e-containing excreta and dust into aerosols, accelerating horizontal transmission within animal populations and increasing human inhalation infection risk; simultaneously, the wider dispersal range promotes contaminant spread among livestock, further expanding human exposure risk. Additionally, in strong wind weather, herders tend to confine animals indoors, increasing contact opportunities between humans and infected animals in enclosed spaces, thereby increasing the likelihood of human brucellosis infection [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSheep inventory (RR\u0026thinsp;=\u0026thinsp;1.15, 95%CI: 1.06\u0026ndash;1.25) and cattle inventory (RR\u0026thinsp;=\u0026thinsp;1.11, 95%CI: 1.04\u0026ndash;1.18) were also important risk factors. This underscores the critical importance of sheep and cattle as primary sources of \u003cem\u003eBrucella\u003c/em\u003e transmission. When abortion occurs in sheep and cattle herds, abortion products containing large quantities of pathogens contaminate the environment, and occupational populations become infected through skin wounds and mucosal contact during handling of aborted material, cleaning livestock housing, and disinfection procedures[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. \u003cem\u003eBrucella\u003c/em\u003e concentrations are particularly elevated in the uterus of infected pregnant animals, making aborted fetuses, placental membranes, and uterine secretions key sources of contamination. Furthermore, infected animals shed the pathogen continuously in milk throughout lactation, with many remaining lifelong shedders, sustaining environmental and foodborne transmission risks[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In Inner Mongolia, traditional pastoral farming systems result in high sheep and cattle inventory with frequent human-animal contact, increasing exposure risk. Additionally, surveillance for diseased animals in pastoral systems is relatively limited, with potentially infected lactating animals maintaining close contact with susceptible populations over prolonged periods, creating persistent transmission pressure[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. By contrast, while modern intensive farming systems offer significant advantages in production efficiency and management standardization, inadequate control measures may actually increase disease clustering risk. In intensive farming systems, high-density husbandry, relatively enclosed environments, and complex animal movement networks mean that once pathogens enter a herd, rapid spread occurs within a short period, leading to group abortion events termed \u0026ldquo;abortion storms\u0026rdquo;[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Such clustered infections can produce environmental contamination far exceeding that from traditional farming systems, as a single abortion releases approximately 10^13 bacteria into the environment, sufficient to infect 60,000 to 600,000 susceptible females[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, crowded conditions in intensive facilities further accelerate pathogen transmission risk. In some intensive operations, staff lack adequate biosafety awareness, control measures are poorly implemented, and stress associated with high-density conditions increases infection susceptibility, all substantially elevating occupational population infection risk[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, strengthening vaccination, regular screening, and isolation of diseased animals in sheep and cattle herds are important measures to reduce human disease incidence. Systematic brucellosis control strategies should include animal vaccination, environmental sanitation and disinfection, and elimination or isolation of infected animals. Vaccination programs should be designed scientifically based on local epidemiological conditions to provide sustained protection to susceptible livestock populations. Vaccination as a standalone measure has proven effective, with decreased disease incidence in herds directly correlating with the proportion of vaccinated animals[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Simultaneously, areas potentially contaminated by pathogens should undergo regular thorough cleaning and disinfection. For high-risk occupational groups such as farm workers and veterinarians, enhanced protective training, provision of personal protective equipment (such as rubber gloves and eye protection), and standardized operational procedures are essential. Additionally, establishing animal traceability systems, improving livestock health records, and strengthening coordinated reporting with health departments to form collaborative mechanisms between veterinary and human disease control are necessary to effectively prevent human-to-human transmission of brucellosis[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNDVI demonstrated a protective effect, with brucellosis incidence risk decreasing by 11% for every one standard deviation increase in vegetation coverage (RR\u0026thinsp;=\u0026thinsp;0.89, 95%CI: 0.87\u0026ndash;0.91). This finding requires interpretation in the context of the nonlinear relationship between NDVI and brucellosis risk. Similar studies have confirmed significant negative correlation between NDVI and brucellosis risk, contrasting with positive correlations reported in Xinjiang and Inner Mongolia studies, which indicated that dense vegetation can protect Brucella from ultraviolet radiation and dry environmental conditions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research in Xinjiang found an inverted U-shaped relationship between NDVI and incidence, with risk reaching a peak at NDVI value of 0.4, after which risk declined as vegetation coverage increased[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, NDVI demonstrated a protective effect, possibly suggesting that the ongoing desert prevention and ecological restoration projects in Inner Mongolia have not only improved environmental quality but may also indirectly reduced the disease burden of brucellosis. The Inner Mongolia region may be at the declining phase of the inverted U-shaped curve, where the dilution effect of the ecosystem begins to function, with high vegetation coverage limiting human-animal contact frequency. The study found no significant association between temperature and brucellosis incidence. Although temperature may indirectly affect pathogen survival through changes in environmental humidity, this effect may be masked by other stronger factors, or its effects may be indirectly manifested through variables such as vegetation index.\u003c/p\u003e \u003cp\u003eOne of the most important findings of this study is that GDP demonstrates a significant nonlinear inverted U-shaped relationship with brucellosis incidence risk, rather than the traditional assumption of monotonic decline, challenging the conventional hypothesis that \u0026ldquo;economic development necessarily leads to monotonic reduction in disease risk\u0026rdquo;[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. At the low GDP stage (0\u0026ndash;40\u0026nbsp;million yuan), regions experience underdeveloped economies with economic structures dominated by traditional agriculture and livestock farming. Although limited disease control investment and weak diagnostic capacity may lead to underestimation of disease cases, the low population density and low human-animal contact intensity result in relatively low natural infection risk, thus maintaining overall low disease incidence[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Entering the middle GDP stage (40\u0026ndash;100\u0026nbsp;million yuan), regions experience rapid economic development, with traditional livestock farming remaining important but at substantially increased intensive levels[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]。Livestock herd size and numbers increase dramatically, and economic development-driven population movement and increased market linkages create favorable conditions for disease transmission; more importantly, diagnostic capacity improves, and with higher GDP, sanitation conditions improve, medical accessibility increases, thus enhancing disease detection rates[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Intensive farming may actually exacerbate disease clustering transmission risk, making this stage a high-risk period[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Upon entering the high GDP stage (\u0026ge;\u0026thinsp;110\u0026nbsp;million yuan), regions are typically more developed cities and prefecture-level municipalities within the autonomous region, with economic structures undergoing complete optimization and upgrading. Although livestock farming persists, it has achieved standardized and regulated husbandry practices with enhanced biosafety awareness and high vaccination coverage rates; quarantine systems are well-established, and medical and health resources are abundant, with significantly improved public health awareness and occupational protection measures, ultimately resulting in substantial reduction and stability of disease risk[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. These findings are consistent with perspectives from international literature. Agricultural intensification and environmental change are closely associated with the emergence of zoonotic diseases[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A research indicates that since 1940, agricultural drivers have been associated with more than 25% of emerging infectious diseases and more than 50% of zoonotic diseases globally, with this proportion further increasing during periods of agricultural expansion and intensification[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, future brucellosis control efforts in Inner Mongolia should focus on regions with moderate economic development, establishing prevention and control systems aligned with industrial transformation at this critical stage.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The use of an ecological study design prevents inference of individual-level causal relationships and did not include potential confounding factors such as vaccination rates and occupational protection measures. Future research should incorporate individual-level data to further verify the mechanisms through which socioeconomic factors influence disease risk, and explore the effects of climate change on long-term epidemic trends of brucellosis, providing evidence for formulating more scientifically sound and effective prevention and control strategies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revealed an inverted U-shaped nonlinear relationship between GDP and disease incidence risk. Atmospheric pressure, wind speed, and sheep and cattle inventory were identified as major risk factors, while vegetation coverage provided certain inhibitory effects on disease transmission. These findings indicate that brucellosis control requires full recognition of factor differences across regions and cannot apply a uniform model; rather, prevention and control measures should be tailored based on each region\u0026rsquo;s economic development stage, industrial structure characteristics, and ecological environmental conditions. Effective control of brucellosis is a systematic undertaking requiring the establishment of comprehensive prevention and control systems encompassing source control, transmission blocking, and surveillance and early warning. Future research should integrate individual-level data to further verify the mechanisms through which socioeconomic factors influence disease risk, explore the effects of climate change on long-term epidemic trends, and provide a more solid scientific foundation for achieving staged control and eventual elimination of brucellosis.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eClinical trial number\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study did not involve any human intervention or clinical trials. The use of human brucellosis case data was based on routine public health surveillance activities in China. According to national regulations, such surveillance data are exempt from institutional ethics review. Therefore, ethics approval was not required for this study. As all data were anonymized and contained no personal identifiers or private health information, the requirement for informed consent was also deemed unnecessary. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAll authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQZ: Writing-review \u0026amp; editing, Writing-original draft, Software, Data curation, Methodology, Visualization, Formal analysis, Conceptualization. NT: Data curation, Validation, Methodology, Supervision, Conceptualization, Resources. HS: Validation, Methodology, Supervision. RQ: Data curation. ZZ: Data curation. BL: Data curation. DZ: Data curation. YX: Validation, Methodology, Supervision, Conceptualization. YW: Validation, Methodology, Supervision. ZZ: Validation, Methodology, Supervision. WZ: Writing-review \u0026amp; editing, Validation, Methodology, Supervision, Conceptualization, Resources.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank the local Centers for Disease Control and Prevention in for their valuable assistance in the data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data is not publicly available due to restrictions (e.g. their containing information that could compromise the privacy of research participants).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShakir R, Brucellosis. 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PubMed PMID: 36279792.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Brucellosis, Spatiotemporal distribution, Bayesian modeling, Environmental factors, Inner Mongolia","lastPublishedDoi":"10.21203/rs.3.rs-9119326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9119326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBrucellosis 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDescriptive 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 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]\u0026thinsp;=\u0026thinsp;1.22, 95% Confidence Interval [CI]: 1.18\u0026ndash;1.27) and wind speed (RR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.16\u0026ndash;1.26) were identified as major risk factors, while the Normalized Difference Vegetation Index (NDVI) exerted a protective effect (RR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.87\u0026ndash;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\u0026thinsp;=\u0026thinsp;1.65, 95% CI:1.26\u0026ndash;2.17) identified at intermediate economic levels (40\u0026ndash;100\u0026nbsp;million yuan). This suggests an economic development paradox, where initial economic growth intensifies livestock production, thereby escalating short-term disease risks.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHuman 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.\u003c/p\u003e","manuscriptTitle":"Influencing Factors of Human Brucellosis in Inner Mongolia, China, 2014– 2023: A Bayesian Spatio-Temporal Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 10:43:03","doi":"10.21203/rs.3.rs-9119326/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-13T09:36:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208222192614925936884985159266803704055","date":"2026-04-07T12:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106030147426215768838648962080230850921","date":"2026-04-06T03:56:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T11:45:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T05:57:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T08:10:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T08:09:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-03-14T03:43:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b948100-006f-4447-a1cd-66a300abd610","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T10:43:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 10:43:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9119326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9119326","identity":"rs-9119326","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}