The Responsive Mechanisms Of Spatial Dynamic Of Three Bacterial Zoonoses In Qinghai Lake Area To Eco-Geographical Factors

Bacterial zoonotic infections are one of the zoonotic diseases,which can,in particular,re-emerge after they are considered to be eradicated or under control.They pose a major public health problem around the world due to human close relationship with animals in agriculture,as companions,and in the natural environment.Another route of exposure includes markets selling the meat or by-products of wild animals are particularly high risk due to the large number of new or undocumented pathogens known to exist in some wild animal populations.Agricultural workers in areas with high use of antibiotics for farm animals may be at increased risk of pathogens resistant to current antimicrobial drugs.People living adjacent to wilderness areas or in semi-urban areas with higher numbers of wild animals are at risk of disease from animals such as rats,foxes,or raccoons.These diseases can also cause disruptions in the production and trade of animal products for food and other uses.Bacterial zoonotic diseases in animals are Anthrax,Brucellosis,Plague,bovine tuberculosis,Listeriosis,Salmonellosis,Leptospirosis,Cat scratch disease,and Psittacosis.Therefore,this study chooses to study three out of these bacterial diseases namely Anthrax,Plague,and Brucellosis.Ecological niche modelling was used to inquire about the factors responsible for their distribution and their spatial dynamics.The study collected cases of Anthrax(Bacillus anthracis),Plague(Yersinia pestis),and Brucellosis(Brucella spp)from spatial records provided by the World Organization for Animal Health and publications.The spatial autocorrelation was minimized by filtering all recorded disease occurrence points using SDM Toolbox v1.1c in Arc GIS 10.5.Filtering was performed by limiting the minimum distance between each pair of points to 10 km.In addition,the filtering program plays the role of systematic sampling.It can delete adjacent records to reduce spatial aggregation,which is regarded as the most effective method for correcting sampling bias.Climatic variables were extracted the from World Clim version 2.1 for data from1970–2000 at 30 arc–second resolution.The categorical variable such as soil type and continuous soil variables in 1km grids were extracted from the soil grids database.In addition,the human population density from the Asia Continental Population Datasets,is publicly and freely available both through the World Pop Dataverse Repository and the World Pop project website.Principal component analysis(PCA)was used to reduce the number of continuous environmental variables.In the MaxEnt model,three methods were used to evaluate the model accuracy:one,the Area under the curve(AUC)of the receiver operating characteristic plot.Two,the stepwise elimination approach helps to remove variables that contributed less than ten percent(10%)to the model.And three,a smooth response curve was used as a quality standard in this model.The main results are described as followings:1.Modelling the environmental suitability for B.anthracis in the Qinghai Lake basin,China.Model performance was evaluated using AUC(area under the curve)and ROC(receiver operating characteristics)curves.The four variables that contributed most to the suitability model for B.anthracis are a relatively soil type classified as;cambisols and kastanozems(48%),high annual mean temperature of-2 to 0℃,(40%),human population density of 40individuals per km~2(10%),and sheep population density(3%).The resulting distribution map identifies the permanently inhabited rim of Qinghai Lake as highly suitable for B.anthracis.2.Environmental suitability of Y.pestis and spatial dynamics of Plague in Qinghai Lakebasin,China.The results found that the average minimum temperature in September(-8 to+5℃),sheep population density(250 sheep per km2),elevation(3000-3200 m.a.s.l)and normalized difference of vegetation index(NDVI(-1 to 1))were influential in characterizing the niche.The results show the AUC and TSS values of 0.82 and 0.75,respectively;these are considerably higher than the null model of 0.5.The area belonging to Gangcha county stands out from the entire study area as having a high probability of Plague occurrence,while that of Haiyan,Gonghe,and Tianjun counties have a moderate probability of Plague risk.A low probability of occurrence can be seen in the northern part of the Qinghai Lake Region.The rim of Qinghai Lake showed more favourable conditions for Y.pestis presence than other areas within the study area.3.Predicting the distribution of Brucellosis in Qinghai Lake basin,China using the MaxEnt algorithm.Six variables contributed>1%,namely:mean diurnal range(31%),Isothermality(26%),Sheep population density(17%),December precipitation(13%),precipitation of the driest month(10%),and June Precipitation(3%).The response curves of the predictors show that the mean diurnal range(<12℃),Isothermality(<33),Sheep population density(500 sheep/km2),December precipitation(1mm),precipitation of the driest month(1.0–2.5 mm),and June Precipitation(>40mm)can influence Brucellosis.High-risk areas were distributed in the north and eastern parts of the lake basin.It is worth nothing that the distribution has no specific elevation preference,as both the low and high elevations were found suitable for Brucellosis.4.Modelling the potential future distribution of Anthrax,Plague,and Brucellosis outbreaks under multiple climate change scenarios for Qinghai Lake basin.In Anthrax model,soil at 68.00%in RCP 4.5,the year 2050 has the highest percentage of contribution.Elevation,Bio 1,and Bio 5 have the highest permutation importance among the environmental variables used in the models across the four future models.The potential spatial distribution of each generated model for future distribution in RCP 2.6 and RCP 4.5 in the year2050 and year 2070.For each of the six four predictions,the AUC value ranged from 0.901 to0.911 making all six models well-performing.Among the future models,the probably suitable areas for Anthrax are highest for the year 2050 in RCP 4.5 and the least suitable area is the year2070 in RCP 2.6.All four models had a high probability of species occurrence.In the year 2050,the model present elevation and bio 3 as the principal contributing variables among the six variables.The later,Bio 3 was the most important variable in RCP 4.5(2050).RCP 2.5 and RCP 4.5(2070)models.This model had a range of AUC from 0.827–0.836 which perform excellently more than the null model(0.5).Models of Plague activity in all RCPs based on near future and future climate conditions accurately identified various locations within the study area.All models predicted the highest Plague activity in the low-elevated areas and most specifically the mouth of Qinghai Lake.Future climate conditions will support increased Plague activity in the Qinghai Lake basin.However,Plague risk associated with climate conditions in high altitudes may remain unsuitable in this study area.This model showed the Brucellosis current distribution and predicts suitable habitat shifts under future climate scenarios.In the new representatives;SSP 2.6 and SSP 4.5 for the years the 2050s and 2070s,the model predicts an expansion in the currently suitable areas.This indicates that under the possible climate changes in the future,the living space of Brucellosis in Qinghai Lake basin China will expand significantly.Ecogeographic variables that contributed significantly to the distribution of Brucellosis in the Qinghai Lake basin are revealed by this model.5.Multivariate approach for studying the relationship between environmental variables used in Anthrax,Plague,and Brucellosis ecological niche modelling.Epidemiological studies provide evidence that ecogeographic factors may affect disease distribution through complex mixtures.Formal investigation of the variable is usually achieved by modelling interactions,which rely on assumptions relating to the identity and the number of variables involved in such a model.Investigation of the intrinsic relationship between the variables used in Anthrax,Plague,and Brucellosis distribution modelling was conducted in this study.This result revealed that there is a positive,strong,and significant relationship between the ecogeographic variables.6.Spatial dynamics of Anthrax,Plague,and Brucellosis in Qinghai Lake basin,China.The result of the two distribution classes(moderate and high)in the Qinghai Lake basin revealed the spatial dynamic of Anthrax,Plague,and Brucellosis.The result indicated two distinct ecological niches,Anthrax and Plague occupied the rim of Qinghai Lake,and Brucellosis occupied the northern part of the study area.Anthrax and Plague show similar spatial dynamism and Brucellosis have entirely different spatial dynamics among the three bacterial diseases.ConclusionsAnthraxThe model revealed that increase annual mean temperature,two specific soil types(cambisols and kastanozems),high human population density and sheep population density,were the contributing variables for predicting B.anthracis environmental suitability.Soil type was the only significant categorical variable and the most influential overall variable;this would strengthen the edaphic paradigm for B.anthracis in its role for global B.anthracis suitability and Anthrax epidemiology studies.PlagueThe average minimum temperature in September,sheep population density,elevation,and NDVI were identified as important contributing features governing habitat suitability for Plague in QLB.The model identifies potential Plague risk areas which can help public health authorities decide where to allocate scarce Plague surveillance resources.BrucellosisBrucellosis occurrences in the Qinghai Lake basin exhibit a spatial trend gradually changing from west to east,with the incidence rates in the east far higher than those of other regions;2)The major variables contributing to the model include mean diurnal range,Isothermality,Sheep population density,Temperature seasonality,December precipitation,Mean temperature of wettest quarter,precipitation of the driest month,and June Precipitation;3)The predicted suitable areas cut across all elevation ranging from high to low.4)The future suitability under lower greenhouse gases emission(SSP 2.6)reveal an expansion in the suitable area,which is inversely proportional to the moderate greenhouse emission(SSP 4.5)in the year2050 and 2070.Also,this study revealed statistically that the variables used for Anthrax,Plague,and Brucellosis modelling contributed individually and possessed an intrinsic ability to form synergy among themselves to foster Anthrax,Plague,and Brucellosis distribution in the Qinghai Lake basin.Lastly,Anthrax and Plague among the three bacterial zoonoses under consideration show a similar spatial distribution trend,which means the two shared the same ecological niche.They established a niche at the rim of the Qinghai Lake basin.Conversely,Brucellosis shows a contrasting spatial distribution to the other two(Anthrax and Plague)sharing the same niche.The risk migration pattern between the current and future distribution of the diseases showed a shift toward the high latitude.