基于环境因素预测肾综合征出血热和疟疾传播风险
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摘要
背景:肾综合征出血热(hemorrhagic fever with renal syndrome, HFRS)是由不同型别的汉坦病毒(hantavirus, HV)引起的一种以发热、出血、肾功能损害等为主要临床特征的自然疫源性疾病。在我国引起HFRS的病原体主要是汉滩型(Hantaan virus, HTNV)和汉城型(Seoul virus, SEOV)汉坦病毒,分别引起大约10%和1%的病死率。我国是世界上HFRS发病最多的国家,HFRS病例数占世界报道病例数的90%以上。病例以青壮年人群为主,不仅对人民身体健康和生命安全造成危害,而且对社会经济发展造成严重影响,已经成为一个严重的公共卫生问题。近年来,我国HFRS的流行趋势呈现出一些新特点:由单一型疫区向混合型疫区演变,家鼠型疫区范围不断扩大,并有向大城市蔓延的趋势,形成新疫区,部分姬鼠型疫区依然维持较高的发病水平。但导致新疫区HFRS病例数增加的可能的环境因素尚不清楚,姬鼠型疫区气候因素与HFRS传播之间的定量关系有待进一步确定。
疟疾是一种经疟蚊(Anopheles)叮咬而感染疟原虫而引起的全球性急性虫媒传染病。自2000年以来,安徽省是我国疟疾疫情波动幅度最大的一个省份,疟疾疫情呈骤升趋势,近几年安徽省每年发病数位于全国前列。各个区县的发病率具有很大的差异,那么近几年安徽省疟疾流行的时空分布有何特点、发病热点区域又在何地?这是我们疟疾防控中亟需解决的科学问题。同时,疟疾的流行具有一定周期性的特点,而且发病率的时间序列多是杂乱的、复杂的、非平稳的,以前的研究很少考虑发病率时间序列的这些特点,因此有必要开展我国一些疟疾典型疫区的流行周期性特点以及气候因素对疟疾发病的“驱动效应”的研究。
目的:①探讨HFRS家鼠型新疫区北京市宿主动物感染HV的空间分布特点,确定影响宿主动物感染HV的主要环境因素,建立北京市宿主动物感染HV的空间预测模型;②定量评价气候因素对东北大兴安岭林区姬鼠型疫区HFRS传播的影响;③明确安徽省疟疾流行的时空分布特点,确定安徽省疟疾发病的热点区域;④阐明我国疟疾典型疫区的流行周期性特点,评价气候因素对疟疾发病的“驱动效应”。
方法:通过现场调查与实验室检测,结合遥感影像利用Logistic回归和空间统计分析,对北京市宿主动物感染HV的风险进行预测;利用互相关分析和时间序列泊松回归分析,定量地评价我国东北大兴安岭林区气候因素对姬鼠型疫区HFRS传播的影响;利用空间自相关和空间统计分析安徽省疟疾发病的时空分布特点,利用时空扫描聚集性分析确定安徽省疟疾发病的热点地区;利用交叉小波变换和小波相干技术确定疟疾典型疫区(安徽省、海南省、云南省)流行的周期性特点,确定气候因素对疟疾流行的“驱动效应”。
结果:①在2005年到2007年HFRS的流行季节,在北京市86个调查点共布放鼠夹22250夹夜,捕获啮齿动物1639只。经RT-PCR(Reverse transcription polymerase chain reaction)检测,获得阳性宿主动物117只,其带毒率7.14%。多因素Logistic回归分析结果显示园地、水田和40-80 m海拔是宿主动物感染HV的危险因素,而林地则是宿主动物感染HV的保护因素。最终模型是:Logit( P ) = 1.059×水田+ 0.115×园地+ 2.285×(40-80 m海拔)? 1.909×林地。建立的风险预测图显示北京市HFRS的高风险区主要位于市区和近郊区县。同时利用HFRS病例的发病地点对风险预测图的可靠性进行了验证。②在内蒙古自治区鄂伦春族自治旗和莫力达瓦达斡尔族自治旗,互相关分析的结果显示月平均降雨量、月平均地表温度、月平均相对湿度和多变量厄尔尼诺南方涛动指数(Multivariate El Ni?o Southern Oscillation Index, MEI)都与HFRS发病是相关的,但存在3-5个月不等的滞后效应。在鄂伦春族自治旗,在控制了自回归、季节性、长期趋势后,3个月前的月均降雨量、4个月前的月均地表温度、3个月前的月均相对湿度、4个月前的MEI在HFRS的传播中起了重要的作用。最终时间序列泊松回归模型提示地表温度每升高1°C HFRS发病数将会增加11.4%。降雨每增加1mm、相对湿度每增加1%、MEI每增加1个单位,HFRS发病数可能会增加1.1%、2.9%、55.3%。HFRS发病数与预期数拟合的非常好(伪R2 = 79.43%)。在莫力达瓦达斡尔族自治旗,在控制了自回归、季节性、长期趋势后,4个月前的月均降雨量、5个月前的月均地表温度、4个月前的月均相对湿度、4个月前的MEI在HFRS的传播中发挥了重要的作用。最终时间序列泊松回归模型提示地表温度每升高1°C HFRS发病数将会增加16.8%。降雨每增加1mm、相对湿度每增加1%、MEI每增加1个单位,HFRS发病数将会增加0.5%、3.2%和73.6%。建立的模型效果较好(伪R2 = 75.91%)。③安徽省各县区疟疾发病率呈现明显的地区差异,90年代后期疫情主要在安徽中部地区,2001年以后疟疾疫区流行区域迅速扩大,高发地区从安徽中部转移到淮北地区。空间趋势分析结果显示疟疾发病率在东西方向和南北方向均具有明显的趋势变化,总体上北方高于南方,在东西方向上发病率呈现“∩”型。空间自相关的结果显示在安徽省范围内疟疾的空间分布存在一定的聚集性的特点。时空聚集性分析(最大空间窗口半径为安徽省50%的总人口、最大时间窗口为研究时期的50%)确定了一级聚类区分布于安徽省北部13个县市,其高发时段为2003.06-2008.10。当时空半径设为25%时,时空聚集性分析结果显示一级聚类区分布于安徽省北部10个县市,其高发时段为2005.07-2007.11。二级聚类区分布于安徽省中东部14个县市,其高发时段为2002.06-2003.10。④连续小波变换的结果显示安徽省疟疾发病率除了发现1年的周期外,还发现了5-6年和12年的年际间的周期。海南省疟疾发病率存在8年左右的周期,1年的周期没有达到统计学水平。而云南省疟疾发病率只发现了1年的周期。当地气候因素(降雨、温度、湿度)的周期以1年周期为主,而MEI除了1年周期还发现了年际周期。交叉小波和小波相干分析结果显示:在安徽省,月均疟疾发病率和月均降雨量、温度(月平均温度、月最高温度、月最低温度)、相对湿度、MEI之间是相干的,频域上两者以1年周期模式为主(除MEI以2-4年频域为主外),两者的位相关系从同步到滞后5个月不等,并且两者之间关系是间断的、不连续的。在海南省,月均疟疾发病率和月均降雨量、温度是持续相干的,与相对湿度、MEI之间存在短暂的相干,两者的位相关系从同步到滞后1个月。在云南省,月均疟疾发病率和月均降雨量、温度、相对湿度、MEI之间是持续相干的,两者的位相关系从同步到滞后1个月,频域上都是以1年周期模式为主。
结论:本研究明确了HFRS新疫区北京市宿主动物感染HV的空间分布特点,确定了影响宿主动物感染HV的主要环境因素,建立了北京市宿主动物感染HV的空间风险预测地图;定量地评价了我国东北大兴安岭林区气候因素对姬鼠型疫区HFRS传播的影响;明确了安徽省疟疾流行的时空分布特点,确定了安徽省疟疾发病时空热点区域;确定了我国疟疾典型疫区的疟疾流行的周期性特点,评价了气候因素对疟疾发病的“驱动效应”。
Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by different species of Hantavirus (HV). China is one of the most severe endemic countries, where there are 90% of the total reported HFRS cases in the world. The causative agents of HFRS in China are predominately Hantaan virus (HTNV) and Seoul virus (SEOV), which cause case fatality rates around 10% and 1%, respectively. HFRS has become a significant public health problem in mainland China because it not only affects the people’s health and safety, but also impacts on the socio-economic development. In recent years, the prevalence of HFRS has shown some new features: on the one hand, the scope of HFRS endemic area is expanding and HFRS has spread to major cities, and on the other hand, HFRS incidence still maintains high in the HTNV-type natural foci. However, the environmental factors facilitating the spread and expansion of the virus in a newly-identified focus remain unclear, and the quantitative relationship between climate variation and the transmission of HFRS remains to be determined in HTNV-type foci.
Malaria is a parasitic disease caused by the bite of Anopheles. Since 2000, malaria resurgence has occurred in China. And Anhui Province is the most seriously affected area with the highest number of malaria cases after 2005. The incidence of malaria shows high variability at the county level in Anhui Province. What are the characteristics of temporal and spatial distribution of malaria in this province, and where are the hot spots? This is the urgent scientific questions addressed in the prevention and control of malaria. Meanwhile, the prevalence of malaria has a certain cyclical characteristics, and the incidence time series are typically noisy, complex and strongly non-stationary. However, previous studies have rarely considered these features of the incidence time series, so it is necessary to characterize the seasonality of the malaria in the typical endemic areas in China and also to identify the association between climatic factors and malaria incidences.
Objectives:①T o understand the spatial distribution of HV infection in rodent hosts in Beijing, and to identify environmental factors contributing to the presence of HV in rodent population, and also to predict spatial distribution of HFRS for possible preemptive public health warnings.②To evaluate the quantitative relationship between climate variation and the transmission of HFRS in northeastern China.③To characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and to identify the distribution of the hot spots at the county level.④To characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to identify the association between climatic factors and malaria incidences.
Methods: The spatial distribution of HV infections in host rodents from Beijing were predicted by using Logistic regression and spatial statistical analysis in combination with field investigation and laboratory testing. The cross correlation analysis and time-series Poisson regression model were used to evaluate the quantitative relationship between climate variation and the transmission of HFRS in HTNV-type foci in northeastern China. Spatial autocorrelation analysis and spatial statistics were used to characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and space-time scanning cluster analysis was used to determine the distribution of the hot spots at the county level. Cross wavelet transform (XWT) and wavelet coherence (WTC) techniques were employed to characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to assess and compare the associations between climatic factors and malaria incidences.
Results:①A total of 1,639 rodents were at 86 sites during HFRS epidemic seasons from 2005 to 2007 in Beijing. 117 rodents were positive for SEOV by RT-PCR test, with an overall infection rate of 7.14%. Multivariate logistic regression analysis indicated that orchards, rice agriculture and moderate elevation were significantly associated with the prevalence of HVs infection in rodents, while the forest was the only protective factor for the infection. The final logistic regression function for predicting the risk areas was Logit(P) = 1.059×Rice agriculture+0.115×Orchards+2.285 Moderate elevation-1.909 Forest. The constructed prediction risk map showed that the highest risk regions for HVs in rodents mainly focused on the downtown and several suburbs. Meanwhile, the locations of HFRS cases were used to test the validity of the constructed risk map.②In Elunchun and Molidawahaner county, the results of cross correlation analysis showed that monthly mean rainfall, land surface temperature, relative humidity, and MEI were significantly correlated with the monthly reported HFRS cases with lags of 3-5 months. In Elunchun county, after controlling for the autocorrelation, seasonality and long-term trend, rainfall at a lag of 3 months, LST at a lag of 4 months, RH at a lag of 3 months, and MEI at a lag of 4 months appeared to play significant roles in the transmission of HFRS. The final time-series Poisson regression model suggests that a 1°C increase in the monthly mean LST may be associated with an 11.4% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 1.1%, 2.9% and 55.3% increases in HFRS cases, respectively. The observed and expected number of cases from the final model matched reasonably well for Elunchun. The pseudo R2 value for the fitted model was 79.43%. In Molidawahaner county, after controlling for the autocorrelation, seasonality, and long-term trend, rainfall at a lag of 4 months, LST at a lag of 5 months, RH at a lag of 4 months, and MEI at a lag of 4 months were significantly associated with HFRS. The final model indicated that a 1°C increase in the monthly mean LST was associated with a 16.8% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 0.5%, 3.2% and 73.6% increases in HFRS cases, respectively. The pseudo R2 value for the fitted model was equal to 75.91%.③The incidence of malaria showed high variability at the county level. Malaria epidemic mainly occurred in the central parts of Anhui Province in the late 1990s, and then expanded to the northern region of this province since 2001. Trend analysis showed that the incidence of malaria changed obviously in the East-West and North-South directions. In general, the incidence in the north was higher than the south in this province, and the incidence in the East-West direction showed the“∩”type. The results of spatial autocorrelation showed the incidence of malaria in Anhui Province was clustered at the county level. Using the maximum spatial cluster size of < 50% of the total population and the maximum temporal cluster size of < 50% of the study period, the spatio-temporal cluster analysis identified a most likely cluster that included 13 counties, which all located in the north of Huai River. The highest endemic period occurred from June 2003 to October 2008. To investigate the possibility of smaller clusters, the same analysis was performed with a modification of the maximum spatial cluster size defined as < 25% total population and the maximum temporal cluster size of < 25% of the study period. A most likely cluster and one secondary cluster were identified. The most likely cluster was almost the same as in the 50% analysis. The secondary sub-cluster included 14 counties, which located in the central and eastern part of this province.④In Anhui province, the continuous wavelet transform (CWT) showed significant periodicity on the 1-y scale. High power was also present in the 5–6-y period and 12-y range. In Hainan province, the CWT showed significant periodicity on the 8-y scale. High power was also present on the 1-y scale, but did not reach significance compared to the null hypothesis. In Yunnan province, the CWT showed significant periodicity on the 1-y scale. We analyzed the relationship between MEI, local weather (monthly mean rainfall, monthly mean average temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity), and malaria incidence in these three provinces using XWT and WTC analyses to identify time- and frequency-specific association. In Anhui province, malaria incidence showed significantly coherence with local weather on the annual scale with a 1–2-mo lag and with MEI in the 2–4-y mode with a 5-mo lag. However, the relationship between malaria incidence and climate wasn’t consistent. In Yunnan province, malaria incidence showed consistent and strong coherence with the monthly rainfall and mean temperature and transient coherence with relative humidity and MEI. In Yunnan province, malaria incidence showed significantly coherence with local weather and MEI on the 1-y scale.
Conclusion: This study clarified the spatial distribution of HV infection in rodent hosts in Beijing, and determined the environmental factors contributing to the presence of HV in rodent population, and also constructed a risk map of HFRS in Beijing. This study also evaluated the quantitative relationship between climate variation and the transmission of HFRS in northeastern China. Meanwhile, this study characterized the temporal and spatial distribution patterns of malaria in Anhui Province, and identified the distribution of the hot spots at the county level. Also, the periodicity of the malaria in the typical endemic areas in China was characterized and the association between climatic factors and malaria incidences was identified.
疟疾是一种经疟蚊(Anopheles)叮咬而感染疟原虫而引起的全球性急性虫媒传染病。自2000年以来,安徽省是我国疟疾疫情波动幅度最大的一个省份,疟疾疫情呈骤升趋势,近几年安徽省每年发病数位于全国前列。各个区县的发病率具有很大的差异,那么近几年安徽省疟疾流行的时空分布有何特点、发病热点区域又在何地?这是我们疟疾防控中亟需解决的科学问题。同时,疟疾的流行具有一定周期性的特点,而且发病率的时间序列多是杂乱的、复杂的、非平稳的,以前的研究很少考虑发病率时间序列的这些特点,因此有必要开展我国一些疟疾典型疫区的流行周期性特点以及气候因素对疟疾发病的“驱动效应”的研究。
目的:①探讨HFRS家鼠型新疫区北京市宿主动物感染HV的空间分布特点,确定影响宿主动物感染HV的主要环境因素,建立北京市宿主动物感染HV的空间预测模型;②定量评价气候因素对东北大兴安岭林区姬鼠型疫区HFRS传播的影响;③明确安徽省疟疾流行的时空分布特点,确定安徽省疟疾发病的热点区域;④阐明我国疟疾典型疫区的流行周期性特点,评价气候因素对疟疾发病的“驱动效应”。
方法:通过现场调查与实验室检测,结合遥感影像利用Logistic回归和空间统计分析,对北京市宿主动物感染HV的风险进行预测;利用互相关分析和时间序列泊松回归分析,定量地评价我国东北大兴安岭林区气候因素对姬鼠型疫区HFRS传播的影响;利用空间自相关和空间统计分析安徽省疟疾发病的时空分布特点,利用时空扫描聚集性分析确定安徽省疟疾发病的热点地区;利用交叉小波变换和小波相干技术确定疟疾典型疫区(安徽省、海南省、云南省)流行的周期性特点,确定气候因素对疟疾流行的“驱动效应”。
结果:①在2005年到2007年HFRS的流行季节,在北京市86个调查点共布放鼠夹22250夹夜,捕获啮齿动物1639只。经RT-PCR(Reverse transcription polymerase chain reaction)检测,获得阳性宿主动物117只,其带毒率7.14%。多因素Logistic回归分析结果显示园地、水田和40-80 m海拔是宿主动物感染HV的危险因素,而林地则是宿主动物感染HV的保护因素。最终模型是:Logit( P ) = 1.059×水田+ 0.115×园地+ 2.285×(40-80 m海拔)? 1.909×林地。建立的风险预测图显示北京市HFRS的高风险区主要位于市区和近郊区县。同时利用HFRS病例的发病地点对风险预测图的可靠性进行了验证。②在内蒙古自治区鄂伦春族自治旗和莫力达瓦达斡尔族自治旗,互相关分析的结果显示月平均降雨量、月平均地表温度、月平均相对湿度和多变量厄尔尼诺南方涛动指数(Multivariate El Ni?o Southern Oscillation Index, MEI)都与HFRS发病是相关的,但存在3-5个月不等的滞后效应。在鄂伦春族自治旗,在控制了自回归、季节性、长期趋势后,3个月前的月均降雨量、4个月前的月均地表温度、3个月前的月均相对湿度、4个月前的MEI在HFRS的传播中起了重要的作用。最终时间序列泊松回归模型提示地表温度每升高1°C HFRS发病数将会增加11.4%。降雨每增加1mm、相对湿度每增加1%、MEI每增加1个单位,HFRS发病数可能会增加1.1%、2.9%、55.3%。HFRS发病数与预期数拟合的非常好(伪R2 = 79.43%)。在莫力达瓦达斡尔族自治旗,在控制了自回归、季节性、长期趋势后,4个月前的月均降雨量、5个月前的月均地表温度、4个月前的月均相对湿度、4个月前的MEI在HFRS的传播中发挥了重要的作用。最终时间序列泊松回归模型提示地表温度每升高1°C HFRS发病数将会增加16.8%。降雨每增加1mm、相对湿度每增加1%、MEI每增加1个单位,HFRS发病数将会增加0.5%、3.2%和73.6%。建立的模型效果较好(伪R2 = 75.91%)。③安徽省各县区疟疾发病率呈现明显的地区差异,90年代后期疫情主要在安徽中部地区,2001年以后疟疾疫区流行区域迅速扩大,高发地区从安徽中部转移到淮北地区。空间趋势分析结果显示疟疾发病率在东西方向和南北方向均具有明显的趋势变化,总体上北方高于南方,在东西方向上发病率呈现“∩”型。空间自相关的结果显示在安徽省范围内疟疾的空间分布存在一定的聚集性的特点。时空聚集性分析(最大空间窗口半径为安徽省50%的总人口、最大时间窗口为研究时期的50%)确定了一级聚类区分布于安徽省北部13个县市,其高发时段为2003.06-2008.10。当时空半径设为25%时,时空聚集性分析结果显示一级聚类区分布于安徽省北部10个县市,其高发时段为2005.07-2007.11。二级聚类区分布于安徽省中东部14个县市,其高发时段为2002.06-2003.10。④连续小波变换的结果显示安徽省疟疾发病率除了发现1年的周期外,还发现了5-6年和12年的年际间的周期。海南省疟疾发病率存在8年左右的周期,1年的周期没有达到统计学水平。而云南省疟疾发病率只发现了1年的周期。当地气候因素(降雨、温度、湿度)的周期以1年周期为主,而MEI除了1年周期还发现了年际周期。交叉小波和小波相干分析结果显示:在安徽省,月均疟疾发病率和月均降雨量、温度(月平均温度、月最高温度、月最低温度)、相对湿度、MEI之间是相干的,频域上两者以1年周期模式为主(除MEI以2-4年频域为主外),两者的位相关系从同步到滞后5个月不等,并且两者之间关系是间断的、不连续的。在海南省,月均疟疾发病率和月均降雨量、温度是持续相干的,与相对湿度、MEI之间存在短暂的相干,两者的位相关系从同步到滞后1个月。在云南省,月均疟疾发病率和月均降雨量、温度、相对湿度、MEI之间是持续相干的,两者的位相关系从同步到滞后1个月,频域上都是以1年周期模式为主。
结论:本研究明确了HFRS新疫区北京市宿主动物感染HV的空间分布特点,确定了影响宿主动物感染HV的主要环境因素,建立了北京市宿主动物感染HV的空间风险预测地图;定量地评价了我国东北大兴安岭林区气候因素对姬鼠型疫区HFRS传播的影响;明确了安徽省疟疾流行的时空分布特点,确定了安徽省疟疾发病时空热点区域;确定了我国疟疾典型疫区的疟疾流行的周期性特点,评价了气候因素对疟疾发病的“驱动效应”。
Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by different species of Hantavirus (HV). China is one of the most severe endemic countries, where there are 90% of the total reported HFRS cases in the world. The causative agents of HFRS in China are predominately Hantaan virus (HTNV) and Seoul virus (SEOV), which cause case fatality rates around 10% and 1%, respectively. HFRS has become a significant public health problem in mainland China because it not only affects the people’s health and safety, but also impacts on the socio-economic development. In recent years, the prevalence of HFRS has shown some new features: on the one hand, the scope of HFRS endemic area is expanding and HFRS has spread to major cities, and on the other hand, HFRS incidence still maintains high in the HTNV-type natural foci. However, the environmental factors facilitating the spread and expansion of the virus in a newly-identified focus remain unclear, and the quantitative relationship between climate variation and the transmission of HFRS remains to be determined in HTNV-type foci.
Malaria is a parasitic disease caused by the bite of Anopheles. Since 2000, malaria resurgence has occurred in China. And Anhui Province is the most seriously affected area with the highest number of malaria cases after 2005. The incidence of malaria shows high variability at the county level in Anhui Province. What are the characteristics of temporal and spatial distribution of malaria in this province, and where are the hot spots? This is the urgent scientific questions addressed in the prevention and control of malaria. Meanwhile, the prevalence of malaria has a certain cyclical characteristics, and the incidence time series are typically noisy, complex and strongly non-stationary. However, previous studies have rarely considered these features of the incidence time series, so it is necessary to characterize the seasonality of the malaria in the typical endemic areas in China and also to identify the association between climatic factors and malaria incidences.
Objectives:①T o understand the spatial distribution of HV infection in rodent hosts in Beijing, and to identify environmental factors contributing to the presence of HV in rodent population, and also to predict spatial distribution of HFRS for possible preemptive public health warnings.②To evaluate the quantitative relationship between climate variation and the transmission of HFRS in northeastern China.③To characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and to identify the distribution of the hot spots at the county level.④To characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to identify the association between climatic factors and malaria incidences.
Methods: The spatial distribution of HV infections in host rodents from Beijing were predicted by using Logistic regression and spatial statistical analysis in combination with field investigation and laboratory testing. The cross correlation analysis and time-series Poisson regression model were used to evaluate the quantitative relationship between climate variation and the transmission of HFRS in HTNV-type foci in northeastern China. Spatial autocorrelation analysis and spatial statistics were used to characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and space-time scanning cluster analysis was used to determine the distribution of the hot spots at the county level. Cross wavelet transform (XWT) and wavelet coherence (WTC) techniques were employed to characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to assess and compare the associations between climatic factors and malaria incidences.
Results:①A total of 1,639 rodents were at 86 sites during HFRS epidemic seasons from 2005 to 2007 in Beijing. 117 rodents were positive for SEOV by RT-PCR test, with an overall infection rate of 7.14%. Multivariate logistic regression analysis indicated that orchards, rice agriculture and moderate elevation were significantly associated with the prevalence of HVs infection in rodents, while the forest was the only protective factor for the infection. The final logistic regression function for predicting the risk areas was Logit(P) = 1.059×Rice agriculture+0.115×Orchards+2.285 Moderate elevation-1.909 Forest. The constructed prediction risk map showed that the highest risk regions for HVs in rodents mainly focused on the downtown and several suburbs. Meanwhile, the locations of HFRS cases were used to test the validity of the constructed risk map.②In Elunchun and Molidawahaner county, the results of cross correlation analysis showed that monthly mean rainfall, land surface temperature, relative humidity, and MEI were significantly correlated with the monthly reported HFRS cases with lags of 3-5 months. In Elunchun county, after controlling for the autocorrelation, seasonality and long-term trend, rainfall at a lag of 3 months, LST at a lag of 4 months, RH at a lag of 3 months, and MEI at a lag of 4 months appeared to play significant roles in the transmission of HFRS. The final time-series Poisson regression model suggests that a 1°C increase in the monthly mean LST may be associated with an 11.4% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 1.1%, 2.9% and 55.3% increases in HFRS cases, respectively. The observed and expected number of cases from the final model matched reasonably well for Elunchun. The pseudo R2 value for the fitted model was 79.43%. In Molidawahaner county, after controlling for the autocorrelation, seasonality, and long-term trend, rainfall at a lag of 4 months, LST at a lag of 5 months, RH at a lag of 4 months, and MEI at a lag of 4 months were significantly associated with HFRS. The final model indicated that a 1°C increase in the monthly mean LST was associated with a 16.8% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 0.5%, 3.2% and 73.6% increases in HFRS cases, respectively. The pseudo R2 value for the fitted model was equal to 75.91%.③The incidence of malaria showed high variability at the county level. Malaria epidemic mainly occurred in the central parts of Anhui Province in the late 1990s, and then expanded to the northern region of this province since 2001. Trend analysis showed that the incidence of malaria changed obviously in the East-West and North-South directions. In general, the incidence in the north was higher than the south in this province, and the incidence in the East-West direction showed the“∩”type. The results of spatial autocorrelation showed the incidence of malaria in Anhui Province was clustered at the county level. Using the maximum spatial cluster size of < 50% of the total population and the maximum temporal cluster size of < 50% of the study period, the spatio-temporal cluster analysis identified a most likely cluster that included 13 counties, which all located in the north of Huai River. The highest endemic period occurred from June 2003 to October 2008. To investigate the possibility of smaller clusters, the same analysis was performed with a modification of the maximum spatial cluster size defined as < 25% total population and the maximum temporal cluster size of < 25% of the study period. A most likely cluster and one secondary cluster were identified. The most likely cluster was almost the same as in the 50% analysis. The secondary sub-cluster included 14 counties, which located in the central and eastern part of this province.④In Anhui province, the continuous wavelet transform (CWT) showed significant periodicity on the 1-y scale. High power was also present in the 5–6-y period and 12-y range. In Hainan province, the CWT showed significant periodicity on the 8-y scale. High power was also present on the 1-y scale, but did not reach significance compared to the null hypothesis. In Yunnan province, the CWT showed significant periodicity on the 1-y scale. We analyzed the relationship between MEI, local weather (monthly mean rainfall, monthly mean average temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity), and malaria incidence in these three provinces using XWT and WTC analyses to identify time- and frequency-specific association. In Anhui province, malaria incidence showed significantly coherence with local weather on the annual scale with a 1–2-mo lag and with MEI in the 2–4-y mode with a 5-mo lag. However, the relationship between malaria incidence and climate wasn’t consistent. In Yunnan province, malaria incidence showed consistent and strong coherence with the monthly rainfall and mean temperature and transient coherence with relative humidity and MEI. In Yunnan province, malaria incidence showed significantly coherence with local weather and MEI on the 1-y scale.
Conclusion: This study clarified the spatial distribution of HV infection in rodent hosts in Beijing, and determined the environmental factors contributing to the presence of HV in rodent population, and also constructed a risk map of HFRS in Beijing. This study also evaluated the quantitative relationship between climate variation and the transmission of HFRS in northeastern China. Meanwhile, this study characterized the temporal and spatial distribution patterns of malaria in Anhui Province, and identified the distribution of the hot spots at the county level. Also, the periodicity of the malaria in the typical endemic areas in China was characterized and the association between climatic factors and malaria incidences was identified.
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