Spatio-temporal cluster and distribution of human brucellosis in Shanxi Province of China between 2011 and 2016

The spatiotemporal epidemiological study on human brucellosis in shenyang, China from 2013 to 2022

Background

Epidemiological characteristics of human brucellosis (HB) have changed over the last decade. In this study, we depicted the spatiotemporal features of HB in Shenyang, China, from 2013 to 2022 and the objective was to visualise spatiotemporal patterns and identify high-risk regions with the purpose to provide evidence for HB prevention and control.

Methods

We performed an observational epidemiological study using HB data obtained from the National Notifiable Disease Reporting System (NNDRS). Joinpoint regression analysis was employed to determine the changing trends in the annual incidence. A vector boundary map of Shenyang was used to visualise spatial distribution. Spatial autocorrelation was identified using both global and local Moran's autocorrelation coefficients, while hotspot areas were determined using the Getis-Ord statistic.

Results

A combined sum of 4103 HB cases were analysed, and the average level of annual incidence of HB was 5.52 per 100,000. The incidence of HB showed obvious seasonality, with a notable peak observed from April to July (summer peak). The annual incidence in Shenyang has been on the rise since 2013, with an annual percentage change (APC) of 6.39% (95%CI 1.29%, 12.39%). Xinmin County exhibited the most elevated average annual incidence rate, with Faku County ranking second. The average annual incidence in rural areas exhibited a significantly greater disparity compared to suburban areas (P < 0.001), whereas the incidence rate in suburban areas demonstrated a significantly higher contrast when compared to urban areas (P < 0.001). A clustered distribution of the annual incidence of HB was observed for all years from 2013 to 2022. Abnormally high values were found in suburban areas, and no abnormally high values were found after 2017. The low-low clustering areas were found in urban as well as suburban areas from 2013 to 2022. Hotspots (P < 0.05) were located in rural areas, while cold spots (P < 0.05) were found in both urban and suburban areas. Since 2020, there have been no hotspots in Shenyang.

Conclusions

Rural areas are high-risk areas for HB and may be key to controlling HB epidemics. Although the annual incidence of HB in rural areas has increased, owing to the stability of spatial relationships and the disappearance of hotspots, there is little possibility of outbreaks; however, stricter monitoring should be applied in rural areas to prevent the emergence of new transmission routes.

The spatiotemporal trend of human brucellosis in China and driving factors using interpretability analysis

Human brucellosis has reemerged in China, with a distinct change in its geographical distribution. The incidence of human brucellosis has significantly risen in inland regions of China. To gain insights into epidemic characteristics and identify factors influencing the geographic spread of human brucellosis, our study utilized the Extreme Gradient Boosting (XGBoost) algorithm and interpretable machine learning techniques. The results showed a consistent upward trend in the incidence of human brucellosis, with a significant increase of 8.20% from 2004 to 2021 (95% CI: 1.70, 15.10). The northern region continued to face a serious human situation, with a gradual upward trend. Meanwhile, the western and southern regions have experienced a gradual spread of human brucellosis, encompassing all regions of China over the past decade. Further analysis using Shapley Additive Explanations (SHAP) demonstrated that higher Gross Domestic Product (GDP) per capita and increased funding for education have the potential to reduce the spread. Conversely, the expansion of human brucellosis showed a positive correlation with bed availability per 1000 individuals, humidity, railway mileage, and GDP. These findings strongly suggest that socioeconomic factors play a more significant role in the spread of human brucellosis than other factors.

Socioeconomic, meteorological factors and spatiotemporal distribution of human brucellosis in China between 2004 and 2019-A study based on spatial panel model

BACKGROUND

Human brucellosis continues to be a great threat to human health in China. The present study aimed to investigate the spatiotemporal distribution of human brucellosis in China from 2004 to 2019, to analyze the socioeconomic factors, meteorological factors and seasonal effect affecting human brucellosis incidence in different geographical regions with the help of spatial panel model, and to provide a scientific basis for local health authorities to improve the prevention of human brucellosis.

METHODS

The monthly reported number and incidence of human brucellosis in China from January 2004 to December 2019 were obtained from the Data Center for China Public Health Science. Monthly average air temperature and monthly average relative humidity of 31 provincial-level administrative units (22 provinces, 5 autonomous regions and 4 municipalities directly under the central government) in China from October 2003 to December 2019 were obtained from the National Meteorological Science Data Centre. The inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield and gross pastoral product of 31 provincial-level administrative units in China from 2004 to 2019 were obtained from the National Bureau of Statistics of China. The temporal and geographical distribution of human brucellosis was displayed with Microsoft Excel and ArcMap software. The spatial autocorrelation and hotspot analysis was used to describe the association among different areas. Spatial panel model was constructed to explore the combined effects on the incidence of human brucellosis in China.

RESULTS

A total of 569,016 cases of human brucellosis were reported in the 31 provincial-level administrative units in China from January 2004 to December 2019. Human brucellosis cases were concentrated between March and July, with a peak in May, showing a clear seasonal increase. The incidence of human brucellosis in China from 2004 to 2019 showed significant spatial correlations, and hotspot analysis indicated that the high incidence of human brucellosis was mainly in the northern China, particularly in Inner Mongolia, Shanxi, and Heilongjiang. The results from spatial panel model suggested that the inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature (the same month, 2-month lagged and 3-month lagged), average relative humidity (the same month) and season variability were significantly associated with human brucellosis incidence in China.

CONCLUSIONS

The epidemic area of human brucellosis in China has been expanding and the spatial clustering has been observed. Inner Mongolia and adjacent provinces or autonomous regions are the high-risk areas of human brucellosis. The inventory of cattle and sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature, average relative humidity and season variability played a significant role in the progression of human brucellosis. The present study strengthens the understanding of the relationship between socioeconomic, meteorological factors and the spatial heterogeneity of human brucellosis in China, through which 'One Health'-based strategies and countermeasures can be provided for the government to tackle the brucellosis menace.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020

BACKGROUND

With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies.

METHODS

Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA).

RESULTS

From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year.

CONCLUSIONS

The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.

Seroprevalence trend of human brucellosis and MLVA genotyping characteristics of Brucella melitensis in Shaanxi Province, China, during 2008-2020

In this study, a total of 179,907 blood samples from populations with suspected Brucella spp. infections were collected between 2008 and 2020 and analyzed by the Rose Bengal plate test (RBPT) and serum agglutination test (SAT). Moreover, conventional biotyping, B. abortus-melitensis-ovis-suis polymerase chain reaction (AMOS-PCR), and multiple-locus variable-number tandem repeat analysis (MLVA) was applied to characterize the isolated strains. A total of 8103 (4.50%) samples were positive in RBPT, while 7705 (4.28%, 95% confidence interval (CI) 4.19-4.37) samples were positive in SAT. There was a significant difference in seroprevalence for human brucellosis over time, in different areas and different cities (districts) (χ² = 2 = 32.23, 1984.14, and 3749.51, p < .05). The highest seropositivity (8.22% (4, 965/60393; 95% CI 8.00-8.44) was observed in Yulin City, which borders Inner Mongolia, Ningxia, and Gansu Province, China, regions that have a high incidence of human brucellosis. Moreover, 174 Brucella strains were obtained, including nine with B. melitensis bv. 1, 145 with B. melitensis bv. 3, and 20 with B. melitensis variants. After random selection, 132 B. melitensis were further genotyped using MLVA-16. The 132 strains were sorted into 100 MLVA-16 genotypes (GTs) (GT 1-100), 81 of which were single GTs represented by singular independent strains. The remaining 19 shared GTs involved 51 strains, and each GT included two to seven isolates from the Shaan northern and Guanzhong areas. These data indicated that although sporadic cases were a dominant epidemic characteristic of human brucellosis in this province, more than 38.6% (51/132) outbreaks were also found in the Shaan northern area and Guanzhong areas. The 47 shared MLVA-16 GTs were observed in strains (n = 71) from this study and strains (n = 337) from 19 other provinces of China. These data suggest that strains from the northern provinces are a potential source of human brucellosis cases in Shaanxi Province. It is urgent to strengthen the surveillance and control of the trade and transfer of infected sheep among regions.

Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017

OBJECTIVE

To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease.

METHODS

The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord G_i^⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence.

RESULTS

Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively.

CONCLUSION

The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.

Human brucellosis epidemiology in the pastoral area of Hulun Buir city, Inner Mongolia autonomous region, China, between 2003 and 2018

Human brucellosis represents a serious public health concern in Hulun Buir and requires a comprehensive epidemiologic analysis to define adapted control measures. The present study describes the case numbers, constituent ratios and incidence rate of human brucellosis. Conventional biotyping, that is abortus, melitensis, ovis and suis (AMOS)-PCR and multi-locus variable-number tandem repeat analysis (MLVA) were used to characterize the Brucella strains. Between 2003 and 2018, a total of 23,897 human brucellosis cases were reported, with an incidence rate of 56.03/100,000, which is 20 times higher than the country's average incidence. This incidence rate increased year after year, culminating in 2005 and decreased between 2011 and 2018. Because Hulun Buir relies on a nomadic livestock rearing system, brucellosis spreads easily among different animal species and humans. In Xin Barag Left Banner and Xin Barag Right Banner, the incidence rates were, respectively, 226.54/100,000 and 199.10/100,000, exceeding those observed in other areas. Most of the cases occurred in the 25- to 45-year-old group, accounting for 65.74% of the cases (15,709/23,897), and among farmers, accounting for 66.71% (15,942/23,897). The male to female incidence ratio was 2.67:1. The higher incidence in younger people and the large gender ratio reflected the unique traditional production and lifestyle of nomads. Most reported cases were observed from April to June, indicating that more than 40% of the cases were related to the delivery of domestic livestock. The biotyping showed that the 44 isolated strains were all B. melitensis, including 12 Brucella melitensis biovar (bv) 1 and 32 B. melitensis bv. 3. The strains displayed a genetic similarity of 80%-100%. Our hypothesis is that human brucellosis outbreak in this region may be originating from a limited source of infection, so further investigation is necessary. The epidemic situation of human brucellosis in Hulun Buir is extremely serious, strengthened surveillance and control in animals' brucellosis should be priority.

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

OBJECTIVES

Human brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.

DESIGN

Time-series study.

SETTING

Mainland China.

METHODS

Data on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.

RESULTS

The number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)₁₂ model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)₁₂ model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.

CONCLUSIONS

The performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data

BACKGROUND

Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.

RESULTS

The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.

CONCLUSIONS

In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.

Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

Reporting on brucellosis, a relatively rare infectious disease caused by Brucella, is often delayed or incomplete in traditional disease surveillance systems in China. Internet search engine data related to brucellosis can provide an economical and efficient complement to a conventional surveillance system because people tend to seek brucellosis-related health information from Baidu, the largest search engine in China. In this study, brucellosis incidence data reported by the CDC of China and Baidu index data were gathered to evaluate the relationship between them. We applied an autoregressive integrated moving average (ARIMA) model and an ARIMA model with Baidu search index data as the external variable (ARIMAX) to predict the incidence of brucellosis. The two models based on brucellosis incidence data were then compared, and the ARIMAX model performed better in all the measurements we applied. Our results illustrate that Baidu index data can enhance the traditional surveillance system to monitor and predict brucellosis epidemics in China.

Epidemiological Characteristics and Spatiotemporal Trend Analysis of Human Brucellosis in China, 1950-2018

The rate of brucellosis, a zoonotic disease, has rapidly increased in humans brucellosis(HB) in recent years. In 1950-2018, a total of 684,380 HB cases (median 2274/year (interquartile range (IQR) 966-8325)) were reported to the National Infectious Disease Surveillance System in mainland China. The incidence of HB peaked in 2014 (4.32/100,000), and then showed a downward trend; we predict that it will maintain a steady downward trend in 2019-2020. Since 2015, the incidence of HB has shown opposite trends in the north and south of China; rates in the north have fallen and rates in the south have increased. In 2004-2018, the most significant increases in incidence of HB were in Yunnan (IQR 0.002-0.463/100,000), Hubei (IQR 0.000-0.338/100,000), and Guangdong (IQR 0.015-0.350/100,000). The areas where HB occurs have little overlap with areas with high per capita GDP in China. The "high-high" clusters of HB are located in northeastern China (Inner Mongolia, Heilongjiang, Jilin, Liaoning, Ningxia, Shanxi, and Gansu), and the "low-low" clusters of HB are located in southern China (Yunnan, Jiangxi, Shanghai, Guangxi, Guangdong, Zhejiang, Guizhou, and Hunan). In recent years, the incidence of HB in China has been controlled to some extent, but the incidence of HB has increased in southern China, and the disease has spread geographically in China from north to south. Further research is needed to address this change and to continue to explore the relationship between the incidence of HB and relevant factors.

Seroprevalence of human brucellosis and molecular characteristics of Brucella strains in Inner Mongolia Autonomous region of China, from 2012 to 2016

In the present study, a total of 1102304 serum samples were collected to detected human brucellosis between the years 2012 and 2016 in Inner Mongolia. Overall, an average of 3.79% anti-Brucella positive in Inner Mongolia was presented but the range of positive rates were among 0.90 to 7.07% in 12 regions. Seroprevalence of human brucellosis increased gradually from 2012 to 2016. However, the incidence rate of human brucellosis showed a declining trend. One hundred and seven Brucella strains were isolated and identified as B. melitensis species, and B. melitensis biovar 3 was the predominant biovar. MLVA-11 genotypes 116 was predominant and had crucial epidemiology to the human population. All 107 strains tested were sorted into 75 MLVA-16 genotypes, with 54 single genotypes representing unique isolates. This result revealed that these Brucellosis cases had epidemiologically unrelated and sporadic characteristics. The remaining 21 shared genotypes among two to four strains, confirming the occurrence of cross-infection and multiple outbreaks. Extensive genotype-events were observed between strains from this study and Kazakhstan, Mongolia, and Turkey, these countries were key members of the grassland silk road. Long-time trade in small ruminants (sheep) in these countries has possibly promoted the spread of Brucella spp. in these regions.