Spatial pattern of foot-and-mouth disease in animals in China, 2010-2016

Preliminary evaluation of a novel serotype O foot-and-mouth disease mRNA vaccine

Foot-and-mouth disease virus (FMDV) is one of the most significant animal pathogens worldwide, severely impacting the health and productivity of pigs, cattle, sheep, and other ungulates. Although the traditional vaccines have played a crucial role in epidemic control, inactivated vaccines face persistent challenges concerning the potential for virus dissemination and pressures from serotype and subtype matching. However, the manufacture of attenuated vaccines is forbidden, and the efficiency of alternative vaccines for immune protection is still inadequate. Consequently, there exists an urgent need for safer and more effective innovative vaccines in animal husbandry. In this study, we aimed to develop a lipid nanoparticle mRNA vaccine based on VP1-3A-3D epitopes from serotype O FMD and to verify its specific expression within cytoplasmic and injection sites. Our findings demonstrated that mRNA transfected into primary spleen cells derived from guinea pigs induced cytokine release, promoted differentiation of both CD4+ T and CD8+ T lymphocytes, and enhanced lymphocyte proliferation rates. Following immunization of mRNA vaccine in guinea pigs, we observed increased differentiation of both CD4+ T and CD8+ T cells, alongside elevated levels of cytokine secretion. Additionally, this vaccination induced the production of specific IgG antibodies as well as neutralizing antibodies. Importantly, our vaccine provided complete protection for all six guinea pigs against a lethal challenge of 100 GPID50, with histopathological scores indicating protection equivalent to that conferred by the inactivated vaccine. The viral load results demonstrated that the vaccine group significantly reduced viral copy numbers in serum and effectively decreased the concentration of the inflammatory cytokine IL-1β. Furthermore, during the pre-immune phase following vaccination with the mRNA vaccine in pigs, heightened cytokine secretion was observed, along with the inhibition of viral replication. Simultaneously, the neutralizing antibody titer in the serum remained stable over 4 months. Immunofluorescence analysis of spleen tissues from both guinea pigs and pigs demonstrated marked activation and increased expression of CD4+ and CD8+ T lymphocytes, as well as macrophages, in the mRNA vaccine group. In summary, this study suggests that the serotype O FMD mRNA vaccine is a promising candidate for further development in the fight against FMDV.

Unveiling the spatial distribution and transboundary pathways of FMD serotype O in Western China and its bordering countries

Foot-and-mouth disease (FMD) is a severe, highly contagious viral disease of livestock that has a significant economic impact on domestic animals and threatens wildlife survival in China and border countries. However, effective surveillance and prevention of this disease is often incomplete and unattainable due to the cost, the great diversity of wildlife hosts, the changing range and dynamics, and the diversity of FMDV. In this study, we used predictive models to reveal the spread and risk of FMD in anticipation of identifying key nodes to control its spread. For the first time, the spatial distribution of FMD serotype O was predicted in western China and border countries using a niche model, which is a combination of eco-geographic, human, topographic, and vegetation variables. The transboundary least-cost pathways (LCPs) model for ungulates in the study area were also calculated. Our study indicates that FMD serotype O survival is seasonal at low altitudes (March and June) and more sensitive to temperature differences at high altitudes. FMD serotype O risk was higher in Central Asian countries and both were highly correlated with the population variables. Ten LCPs were obtained representing Pakistan, Kazakhstan, Kyrgyzstan, and China.

Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987-2016)

Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987-2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.

Global Risk Assessment of the Occurrence of Bovine Lumpy Skin Disease: Based on an Ecological Niche Model

Lumpy skin disease (LSD) is a highly contagious disease in bovine animals. An outbreak of LSD can cause devastating economic losses to the cattle industry. To investigate the distribution characteristics of historical LSD epidemics, LSD was divided into four phases for directional distribution analysis based on trends in epidemic prevalence. Ecological niche models were developed for LSD as well as for two vectors (Stomoxys calcitrans and Aedes aegypti), and global predictive maps were generated for the probability of LSD occurrence and the potential distribution of the two LSD vectors. The models had good predictive performance (the AUC values were 0.894 for the LSD model, 0.911 for the S. calcitrans model, and 0.950 for the A. aegypti model). The LSD combined vector prediction map was generated by combining the distribution maps of Stomoxys calcitrans and Aedes aegyptiwith fuzzy overlay tool in ArcGIS. The LSD combined vector prediction map was combined with the LSD prediction map to generate the LSD vector transmission risk map. The eastern and northwestern regions of North America, the eastern and northern regions of South America, the central and southern regions of Africa, the southern region of Europe, the northwestern and southeastern regions of Asia, and the eastern region of Australia were predicted to provide suitable environmental conditions for the occurrence of LSD. Cattle density, buffalo density, and bio2 (mean diurnal range) were identified as key variables for the occurrence of LSD. The findings of this study can be useful to policymakers in developing and implementing preventive measures of LSD for the health of cattle and the cattle industry.

A scoping review of foot-and-mouth disease risk, based on spatial and spatio-temporal analysis of outbreaks in endemic settings

Foot-and-mouth disease (FMD) is one of the most important transboundary animal diseases affecting livestock and wildlife species worldwide. Sustained viral circulation, as evidenced by serological surveys and the recurrence of outbreaks, suggests endemic transmission cycles in some parts of Africa, Asia and the Middle East. This is the result of a complex process in which multiple serotypes, multi-host interactions and numerous socio-epidemiological factors converge to facilitate disease introduction, survival and spread. Spatial and spatio-temporal analyses have been increasingly used to explore the burden of the disease by identifying high-risk areas, analysing temporal trends and exploring the factors that contribute to the outbreaks. We systematically retrieved spatial and spatial-temporal studies on FMD outbreaks to summarize variations on their methodological approaches and identify the epidemiological factors associated with the outbreaks in endemic contexts. Fifty-one studies were included in the final review. A high proportion of papers described and visualized the outbreaks (72.5%) and 49.0% used one or more approaches to study their spatial, temporal and spatio-temporal aggregation. The epidemiological aspects commonly linked to FMD risk are broadly categorizable into themes such as (a) animal demographics and interactions, (b) spatial accessibility, (c) trade, (d) socio-economic and (e) environmental factors. The consistency of these themes across studies underlines the different pathways in which the virus is sustained in endemic areas, with the potential to exploit them to design tailored evidence based-control programmes for the local needs. There was limited data linking the socio-economics of communities and modelled FMD outbreaks, leaving a gap in the current knowledge. A thorough analysis of FMD outbreaks requires a systemic view as multiple epidemiological factors contribute to viral circulation and may improve the accuracy of disease mapping. Future studies should explore the links between socio-economic and epidemiological factors as a foundation for translating the identified opportunities into interventions to improve the outcomes of FMD surveillance and control initiatives in endemic contexts.

The spatial and temporal trends of severe periodontitis burden in Asia, 1990-2019: A population-based epidemiological study

BACKGROUND

To investigate the long-term and spatial patterns of incidence, prevalence and disability-adjusted life year (DALY) of severe periodontitis in Asia from 1990 to 2019, and to estimate the associations between disease burden and socioeconomic development using the Socio-Demographic Index (SDI).

METHODS

Data were obtained from the global burden of disease study 2019. The average annual percent change (AAPC) was calculated to reflect temporal trends, spatial autocorrelation analysis was conducted to estimate the spatial characteristics, and spatial panel models were used to investigate the association between SDI and severe periodontitis burden.

RESULTS

For Asia as a whole, the crude rates increased by 1.10% per year for incidence, 1.42% per year for prevalence and 1.41% per year for DALY from 1990 to 2019. The age-standardized incidence, prevalence and DALY rates increased by 0.18%, 0.22% and 0.23% per year, respectively. Spatially, the hot spots of age-standardized incidence, prevalence and DALY rates were located in Southern Asia, besides, these rates all showed increasing trends in most countries, and the increases were clustered in Southeastern Asia. Furthermore, SDI showed a negative association with incidence (coef = -14.44; 95%CI: -24.63, -4.25) and prevalence (coef = -40.09; -51.81, -28.36), and a positive association with DALY rates (coef = 0.31; 0.23, 0.38).

CONCLUSIONS

Severe periodontitis poses a serious public health challenge in Asian countries with increasing temporal trends and substantial spatial inequalities. Effective geographically targeted public health interventions and strategies are needed to address the growing burden associated with severe periodontitis. This article is protected by copyright. All rights reserved.

Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation

Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.

Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis

Background

Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals. As a transboundary animal disease, the prevention and control of FMD are important. This study was based on spatial multi-criteria decision analysis (MCDA) to assess FMD risk areas in mainland China. Ten risk factors were identified for constructing risk maps by scoring, and the analytic hierarchy process (AHP) was used to calculate the criteria weights of all factors. Different risk factors had different units and attributes, and fuzzy membership was used to standardize the risk factors. The weighted linear combination (WLC) and one-at-a-time (OAT) were used to obtain risk and uncertainty maps as well as to perform sensitivity analysis.

Results

Four major risk areas were identified in mainland China, including western (parts of Xinjiang and Tibet), southern (parts of Yunnan, Guizhou, Guangxi, Sichuan and Guangdong), northern (parts of Gansu, Ningxia and Inner Mongolia), and eastern (parts of Hebei, Henan, Anhui, Jiangsu and Shandong). Spring is the main season for FMD outbreaks. Risk areas were associated with the distance to previous outbreak points, grazing areas and cattle density. Receiver operating characteristic (ROC) analysis indicated that the risk map had good predictive power (AUC=0.8634).

Conclusions

These results can be used to delineate FMD risk areas in mainland China, and veterinary services can adopt the targeted preventive measures and control strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-03084-5.

Spatial distribution and risk areas of foot and mouth disease in mainland China

Foot-and-mouth disease (FMD) is a severe infectious disease in animal, which affects regional economies and food security of many countries. A total of 109 FMD outbreaks in China (from 2010 to 2019) were assessed. To investigate whether the FMD outbreaks were significantly aggregated in China, spatio-temporal cluster analysis was performed. A MaxEnt model was established to identify high risk areas for FMD in China and to identify relevant risk factors. As a result, both the FMD serotype A and O had one cluster each. Roads density, isothermality, UV-B seasonality and railways density were identified as important factors that affect the occurrence of FMD serotype A. The minimum temperature of the coldest month contributed most to FMD serotype O outbreak, followed by railways density and markets distribution. This study may provide useful information for decision makers for the tailoring of a risk-based surveillance of FMD in China.

The risk factor assessment of the spread of foot-and-mouth disease in mainland China

In China, foot-and-mouth disease (FMD) serotype O remains prevalent, and its main host is pigs. Infected but undiscovered pigs can carry foot-and-mouth disease virus (FMDV) for a longtime. And, the virus can spread among farms through pig trade. Although individual vaccination at least 2 times a year and monthly monitoring disease and culling all individual in same group for pigs are adopted vigorously in China, the epidemic remains prevalent. Therefore, in this paper, based on these propagation characteristics and control measures of the epidemic in China, we take the pig farms as research individuals, the trade among farms as transmission routes to establish a dynamic model with nonlinear incidence. In addition, we use this model to assess the impact of trade and transport of pigs among farms on the spread of foot-and-mouth disease virus (FMDV), and to assess the effect of the immunization, monitoring and culling adopted presently in China on the control of the epidemic. By the dynamical analysis of the model, it is found that there will appear backward branching under some conditions, which means that there are two spreading thresholds for the disease, and the disease development trend is also related to the current epidemic situation. Besides, we give the threshold conditions of key parameters to control the spread of FMD. By carrying out data fitting and parameter estimation, we confirm the model rationality, and give four evaluation indexes: the basic reproduction number R₀ of FMD serotype O in China, the value of the infected farms at the equilibria, annual probability of a susceptible farm being infected and annual transmission intensity of an infected farm. By carrying out the sensitivity analysis of key parameters on four evaluation indexes, the effect of parameters on the spread of the disease can be intuitively observed. All these can provide a theoretical basis for understanding of the trading-based transmission mechanism, control and prevention of foot-and-mouth disease in pigs in China.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Background

This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.

Methods

Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.

Results

Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Retrospect and Risk Analysis of Foot-and-Mouth Disease in China Based on Integrated Surveillance and Spatial Analysis Tools

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and seriously affects the development of animal husbandry. It is necessary to defend the spread of FMD. To explore the distribution characteristics and transmission of FMD between 2010 and 2017 in China, Global Moran's I test and Getis-Ord Gi index were used to analyze the spatial cluster. A space-time permutation scan statistic was applied to analyze the spatio-temporal pattern. GIS-based method was employed to create a map representing the distribution pattern, directional trend, and hotspots for each outbreak. The number of cases was defined as the number of animals with FMD for the above analysis. We also constructed a phylogenetic tree to compare the homology and variation of FMD virus (FMDV) to provide a clue for the potential development of an effective vaccine. The results indicated that the FMD outbreaks in China had obvious time patterns and clusters in space and space-time, with the outbreaks concentrated in the first half of each year. The outbreaks of FMD decreased each year from 2010 with an obvious downward trend of hotspots. Spatial analysis revealed that the distribution of FMD outbreaks in 2010, 2015, and 2017 exhibited a clustered pattern. Space-time scanning revealed that the spatio-temporal clusters were centered in Guangdong, Tibet and the junction of Wuhan, Jiangxi, Anhui. Comparison of the spatial analysis and space-time analysis of FMD outbreaks revealed that Guangdong was the same cluster of the two in 2010. In addition, the directional trend analysis indicated that the FMD transmission was oriented northwest-southeast. The findings demonstrated that FMDV in China can be divided into three pedigrees and the homology of these strains is very high while comparing the first FMDV strain with the others. The data provide a basis for the effective monitoring and prevention of FMD, and for the development of an FMD vaccine in China.

Review on Outbreak Dynamics, the Endemic Serotypes, and Diversified Topotypic Profiles of Foot and Mouth Disease Virus Isolates in Ethiopia from 2008 to 2018

Foot and mouth disease (FMD) endemicity in Ethiopia's livestock remains an ongoing cause for economic concern, with new topotypes still arising even in previously unaffected areas. FMD outbreaks occur every year almost throughout the country. Understanding the outbreak dynamics, endemic serotypes, and lineage profiles of FMD in this country is very critical in designing control and prevention programs. For this, detailed information on outbreak dynamics in Ethiopia needs to be understood clearly. In this article, therefore, we review the spatial and temporal patterns and dynamics of FMD outbreaks from 2008 to 2018. The circulating serotypes and the topotypic profiles of the virus are also discussed. FMD outbreak data were obtained from; reports of MoARD (Ministry of Agriculture and Rural Development)/MoLF (Ministry of livestock and Fishery, NVI (National Veterinary Institute), and NAHDIC (National Animal Health Diagnostic and Investigation Center); published articles; MSc works; PhD theses; and documents from international organizations. To effectively control and prevent FMD outbreaks, animal health agencies should focus on building surveillance systems that can quickly identify and control ongoing outbreaks and implement efficient preventive measures.

Risk analysis of African swine fever in Poland based on spatio-temporal pattern and Latin hypercube sampling, 2014-2017

Background

African swine fever (ASF) is a devastating infectious disease of pigs. ASF poses a potential threat to the world pig industry, due to the lack of vaccines and treatments. In this study, the Geographic Information System (GIS) spatial analysis was applied to analyze the distribution, dispersion of the epidemic and clustering of ASF in Poland.

Results

The results show that the center of the epidemic moved gradually towards the southwest, and the distribution of the epidemic changed from south-north to east-west. Through space-time scan statistical analysis, the 3 clusters major of wild boar cases involve longer time spans and larger radii, while the other five with higher relative risks involved in domestic pigs. And then, a quantitative model was constructed to analyse the risk of releasing African swine fever virus (ASFV) from Poland by the legal export of pork and pork products. The Latin hypercube sampling results show that the probability is relatively low (the average value is 4.577 × 10^− 7).

Conclusions

All the identification of the spatio-temporal patterns of the epidemic and the risk analysis model would give a further understanding of the dynamics of disease transmission and help to design corresponding measures to minimize the catastrophic consequences of potential ASFV introduction.

Epidemiology and spatial distribution of bluetongue virus in Xinjiang, China

Bluetongue (BT) is a non-contagious disease affecting domestic and wild ruminants. Outbreaks of BT can cause serious economic losses. To investigate the distribution characteristics of bluetongue virus (BTV), two large-scale censuses of BTV prevalence in Xinjiang, China were collected. Spatial autocorrelation analysis, including global spatial autocorrelation and local spatial autocorrelation, was performed. Risk areas for BTV occurrence in Xinjiang were detected using the presence-only maximum entropy model. The global spatial autocorrelation of BTV distribution in Xinjiang in 2012 showed a random pattern. In contrast, the spatial distribution of BTV from 2014 to 2015 was significantly clustered. The hotspot areas for BTV infection included Balikun County (p < 0.05), Yiwu County (p < 0.05) and Hami City (p < 0.05) in 2012. These three regions were also hotspot areas during 2014 and 2015. Sheep distribution (25.6% contribution), precipitation seasonality (22.1% contribution) and mean diurnal range (16.2% contribution) were identified as the most important predictors for BTV occurrence in Xinjiang. This study demonstrated the presence of high-risk areas for BTV infection in Xinjiang, which can serve as a tool to aid in the development of preventative countermeasures of BT outbreaks.

Spatial Patterns and Risk Factors of Bluetongue Virus Infection in Inner Mongolia, China

Bluetongue (BT) is a noncontagious disease affecting domestic and wild ruminants. Outbreaks of BT can cause serious economic losses. Although the causative agent, BT virus (BTV) is endemic in China, a comprehensive analysis has yet to be conducted examining the spatial distribution and risk factors of the virus throughout the Inner Mongolia province. Between June 2013 and February 2015, a total of 6199 blood samples of goats and sheep were collected from 11 leagues and cities. To investigate the distribution characteristics of BTV, spatial autocorrelation analysis, including both global and local spatial autocorrelation, was conducted. To develop a model for the association between BTV infection and specific risk factors, a multiple logistic regression analysis was performed. The global spatial autocorrelation data on the distribution of BTV exhibited a random pattern. Alashan was observed to be a cold spot for BTV infection. During the study period, no hot spots were detected. An increased risk of BTV infection in Inner Mongolia was associated with the breed and age of the animal.