空间流行病空间流行病学在血吸虫病防控实践中的应用进展
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摘要
血吸虫病是我国重点防控的重大疾病之一,消除血吸虫病对于保障人民健康、促进社会经济发展有重要意义。随着空间流行病学的发展,血吸虫病空间分布描述、时空趋势预测、环境影响因素分析等方面取得了长足进步。本文综述了空间流行病学在血吸虫病防控中的应用,对血吸虫病时空分布、空间模型和遥感技术的应用等方面进行了阐述。
Schistosomiasis is one of the key diseases of surveillance and prevention in China.The elimination of schistosomiasis is of great significance to people's health and social economy.With the development of spatial epidemiology,progress has been made in the spatial distribution of schistosomiasis,the prediction of spatial and temporal trends,and analysis of the environmental factors.This paper reviews the application of spatial epidemiology in the control and prevention of schistosomiasis and introduces the spatio-temporal distribution methods,spatial model,and application of remote sensing technology.
Schistosomiasis is one of the key diseases of surveillance and prevention in China.The elimination of schistosomiasis is of great significance to people's health and social economy.With the development of spatial epidemiology,progress has been made in the spatial distribution of schistosomiasis,the prediction of spatial and temporal trends,and analysis of the environmental factors.This paper reviews the application of spatial epidemiology in the control and prevention of schistosomiasis and introduces the spatio-temporal distribution methods,spatial model,and application of remote sensing technology.
引文
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[5]Chowell G,Rothenberg R.Spatial infectious disease epidemiology:on the cusp[J].BMC Med,2018,16(1):192.
[6]Kirby RS,Delmelle E,Eberth JM.Advances in spatial epidemiology and geographic information systems[J].Ann Epidemiol,2017,27(1):1-9.
[7]Carroll R,Lawson AB,Faes C,et al.Spatially-dependent Bayesian model selection for disease mapping[J].Stat Methods Med Res,2018,27(1):250-268.
[8]Carroll R,Lawson AB,Faes C,Spatio-temporal Bayesian model selection for disease mapping[J].Environmetrics,2016,27(8):466-478.
[9]周晓农.空间流行病学[M].北京:科学出版社,2009:146-150,236-247.
[10]O'Hanlon SJ,Slater HC,Cheke RA,et al.Model-Based geostatistical mapping of the prevalence of Onchocerca volvulus in West Africa[J].PLoS Negl Trop Dis,2016,10(1):e0004328.
[11]付海军.基于高分一号与Landsat-8影像的水体浊度反演比较[J].测绘与空间地理信息,2017,40(6):109-112.
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[14]刘元波,傅巧妮,宋平,等.卫星遥感反演降水研究综述[J].地球科学进展,2011,26(11):1162-1172.
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[17]张莹.遥感影像监督分类和非监督分类方法探讨[J].黑龙江科技信息,2016(02):79.
[18]Guo JG,Chen HG,Lin DD,et al.A method of rapid identification snail habitat in marshland of Poyang Lake region by remote sensing[J].中国寄生虫病防治杂志,2002,15(5):39-44.
[19]李源培.东洞庭湖区水位和气候因素对日本血吸虫中间宿主钉螺分布的影响及其孳生地探测[D].上海:复旦大学,2011.
[20]王增亮,高杰,陶波,等.应用高分辨率中巴资源卫星遥感图像探测湖沼地区钉螺孳生地的初步研究[J].中国血吸虫病防治杂志,2012,24(6):640-644.
[21]薛靖波,张利娟,王强,等.高分辨率遥感技术在日本血吸虫病监测中的应用[J].中国血吸虫病防治杂志,2015,27(5):551-554.
[22]董毅,李朝晖,冯锡光,等.环境遥感替代指标与山丘地区钉螺分布的关系[J].中国血吸虫病防治杂志,2011,23(3):258-261.
[23]张文馨.钉螺空间分布格局和抽样方法优化研究[D].南昌:江西师范大学,2015.
[24]汪训平,赵安.以植物丰度为辅助变量的钉螺空间分层采样研究[J].中国血吸虫病防治杂志,2017,29(4):420-425.
[25]Wang XY,He J,Yang K,et al.Applications of spatial technology in schistosomiasis control programme in the People's Republic of China[J].Adv Parasitol,2016,92:143-163.
[26]Chen Y.New approaches for calculating Moran's index of spatial autocorrelation[J].PLoS One,2013,8(7):e68336.
[27]Ord JK,Getis A.Local spatial autocorrelation statistics:distributional issues and an application[J].Geogr Anal,1995,27(4):286-306.
[28]常变蓉,李仁东,徐兴建,等.基于GIS空间自相关的江汉平原钉螺分布特征[J].长江流域资源与环境,2014,23(7):972-978.
[29]Clennon JA,King CH,Muchiri EM,et al.Spatial patterns of urinary schistosomiasis infection in a highly endemic area of coastal Kenya[J].Am J Trop Med Hyg,2004,70(4):443-448.
[30]唐咸艳,李峤,黄秋兰,等.时空扫描统计量三维可视化的实现[J].中国卫生统计,2015,32(4):693-694.
[31]Kulldorff M.A spatial scan statistic[J].Commun Stat,1997,26(6):1481-1496.
[32]董倩楠.基于Kulldorff扫描统计量的聚类方法研究及应用[D].北京:中国石油大学,2016.
[33]Block R.Software review:Scanning for clusters in space and time:A tutorial review of SaTScan[J].Soc Sci Comput Rev,2007,25(2):272-278.
[34]赵飞,朱蓉,张利娟,等.SaTScan在湖沼型血吸虫病聚集区域探测中的应用[J].中国血吸虫病防治杂志,2011,23(1):28-31.
[35]Meurs L,Mbow M,Boon N,et al.Micro-geographical heterogeneity in Schistosoma mansoni and S.haematobium infection and morbidity in a co-endemic community in northern Senegal[J].PLoSNegl Trop Dis,2013,7(12):e2608.
[36]Tango T,Takahashi K.A flexibly shaped spatial scan statistic for detecting clusters[J].Int J Health Geogr,2005,4:11.
[37]Zhu H,Cai SX,Liu JB,et al.A spatial analysis of human Schistosoma japonicum infections in Hubei,China,during 2009-2014[J].Parasit Vectors,2016,9(1):529.
[38]Magalhaes RJ,Clements AC,Patil AP,et al.The applications of model-based geostatistics in helminth epidemiology and control[J].Adv Parasitol,2011,74:267-296.
[39]Schur N,Utzinger J,Vounatsou P.Modelling age-heterogeneous Schistosoma haematobium and S.mansoni survey data via alignment factors[J].Parasit Vectors,2011,4:142.
[40]Chammartin F,Hürlimann E,Raso G,et al.Statistical methodological issues in mapping historical schistosomiasis survey data[J].Acta Trop,2013,128(2):345-352.
[41]Sturrock HJW,Pullan RL,Kihara JH,et al.The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya[J].PLoS Negl Trop Dis,2013,7(1):e2016.
[42]Schur N,Hürlimann E,Stensgaard A,et al.Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling[J].Acta Trop,2013,128(2):365-377.
[43]Seto EYW,Sousa-Figueiredo JC,Betson M,et al.Patterns of intestinal schistosomiasis among mothers and young children from Lake Albert,Uganda:water contact and social networks inferred from wearable global positioning system dataloggers[J].Geospat Health,2012,7(1):1-13.
[44]Rathbun S L.Interpolation of spatial data:Some theory for Kriging[J].Technometrics,2000,42(4):436-437.
[45]Pullan RL,Sturrock HJ,Soares MR,et al.Spatial parasite ecology and epidemiology:a review of methods and applications[J].Parasitology,2012,139(14):1870-1887.
[46]Vounatsou P,Raso G,Tanner M,et al.Bayesian geostatistical modelling for mapping schistosomiasis transmission[J].Parasitology,2009,136(13):1695-1705.
[47]Pullan RL,Kabatereine NB,Quinnell RJ,et al.Spatial and genetic epidemiology of hookworm in a rural community in Uganda[J].PLoS Negl Trop Dis,2010,4(6):e713.
[48]Soares MR,Barnett AG,Clements AC.Geographical analysis of the role of water supply and sanitation in the risk of helminth infections of children in West Africa[J].Proc Natl Acad Sci U S A,2011,108(50):20084-20089.
[49]Oliveira VD.Bayesian analysis of conditional autoregressive models[J].Ann Inst Statist Math,2012,64(1):107-133.
[50]Hoef JMV,Peterson EE,Hooten MB,et al.Spatial autoregressive models for statistical inference from ecological data[J].Ecol Monogr,2018,88(1):36-59.
[51]Yang GJ,Vounatsou P,Zhou XN,et al.A Bayesian-based approach for spatio-temporal modeling of county level prevalence of Schistosoma japonicum infection in Jiangsu province,China[J].Int J Parasitol,2005,35(2):155-162.
[52]Hu Y,Ward MP,Xia C,et al.Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach[J].Sci Rep,2016,6:24173.
[53]Lee D.A comparison of conditional autoregressive models used in Bayesian disease mapping[J].Spat Spatiotemporal Epidemiol,2011,2(2):79-89.
[54]Manyangadze T,Chimbari MJ,Gebreslasie M,et al.Risk factors and micro-geographical heterogeneity of Schistosoma haematobium in Ndumo area,Mkhanyakude district,KwaZulu-Natal,South Africa[J].Acta Trop,2016,159:176-184.
[55]Huang B,Wu B,Barry M.Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J].Int J Geogr Infor Sci,2010,24(3):383-401.
[56]Ge L,Zhao Y,Sheng Z,et al.Construction of a seasonal differencegeographically and temporally weighted regression(SD-GTWR)model and comparative analysis with GWR-based models for hemorrhagic fever with renal syndrome(HFRS)in Hubei Province(China)[J].Int J Environ Res Public Health,2016,13(11):1062.
[57]Indrayani F,Pramoedyo H,Iriany A,et al.Geographically and temporally weighted regression modeling in analyzing factors affecting the spread of dengue fever in Malang[J].J Exp Life Sci,2018,8(2):71-74.
[58]Goldstein H,Browne W,Rasbash J.Multilevel modelling of medical data[J].Stat Med,2002,21(21):3291-3315.
[59]Browne WJ,Goldstein H,Rasbash J.Multiple membership multiple classification(MMMC)models[J].StatModel,2001,1(2):103-124.
[60]Manyangadze T,Chimbari MJ,Gebreslasie M,et al.Application of geo-spatial technology in schistosomiasis modelling in Africa:a review[J].Geospat Health,2015,10(2):326.2018-12-18
[2]张利娟,徐志敏,钱颖骏,等.2015年全国血吸虫病疫情通报[J].中国血吸虫病防治杂志,2016,28(6):611-617.
[3]Song LG,Wu XY,Sacko M,et al.History of schistosomiasis epidemiology,current status,and challenges in China:on the road to schistosomiasis elimination[J].Parasitol Res,2016,115(11):4071-4081.
[4]Scobie S.Spatial epidemiology:methods and applications[J].JEpidemiol Community Health,2003,57(3):232.
[5]Chowell G,Rothenberg R.Spatial infectious disease epidemiology:on the cusp[J].BMC Med,2018,16(1):192.
[6]Kirby RS,Delmelle E,Eberth JM.Advances in spatial epidemiology and geographic information systems[J].Ann Epidemiol,2017,27(1):1-9.
[7]Carroll R,Lawson AB,Faes C,et al.Spatially-dependent Bayesian model selection for disease mapping[J].Stat Methods Med Res,2018,27(1):250-268.
[8]Carroll R,Lawson AB,Faes C,Spatio-temporal Bayesian model selection for disease mapping[J].Environmetrics,2016,27(8):466-478.
[9]周晓农.空间流行病学[M].北京:科学出版社,2009:146-150,236-247.
[10]O'Hanlon SJ,Slater HC,Cheke RA,et al.Model-Based geostatistical mapping of the prevalence of Onchocerca volvulus in West Africa[J].PLoS Negl Trop Dis,2016,10(1):e0004328.
[11]付海军.基于高分一号与Landsat-8影像的水体浊度反演比较[J].测绘与空间地理信息,2017,40(6):109-112.
[12]廖志宏.基于地面传感器数据与遥感数据的地表温度反演研究[D].徐州:中国矿业大学,2014.
[13]谷松岩,邱红,杨忠东,等.应用先进微波探测器AMSU资料遥感反演春季陆地表层湿度[J].气候与环境研究,2004,9(1):43-53.
[14]刘元波,傅巧妮,宋平,等.卫星遥感反演降水研究综述[J].地球科学进展,2011,26(11):1162-1172.
[15]邓辉.高精度卫星遥感技术在地质灾害调查与评价中的应用[D].成都:成都理工大学,2007.
[16]Atkinson PM,Lewis P.Geostatistical classification for remote sensing:an introduction[J].ComputGeosci,2000,26(4):361-371.
[17]张莹.遥感影像监督分类和非监督分类方法探讨[J].黑龙江科技信息,2016(02):79.
[18]Guo JG,Chen HG,Lin DD,et al.A method of rapid identification snail habitat in marshland of Poyang Lake region by remote sensing[J].中国寄生虫病防治杂志,2002,15(5):39-44.
[19]李源培.东洞庭湖区水位和气候因素对日本血吸虫中间宿主钉螺分布的影响及其孳生地探测[D].上海:复旦大学,2011.
[20]王增亮,高杰,陶波,等.应用高分辨率中巴资源卫星遥感图像探测湖沼地区钉螺孳生地的初步研究[J].中国血吸虫病防治杂志,2012,24(6):640-644.
[21]薛靖波,张利娟,王强,等.高分辨率遥感技术在日本血吸虫病监测中的应用[J].中国血吸虫病防治杂志,2015,27(5):551-554.
[22]董毅,李朝晖,冯锡光,等.环境遥感替代指标与山丘地区钉螺分布的关系[J].中国血吸虫病防治杂志,2011,23(3):258-261.
[23]张文馨.钉螺空间分布格局和抽样方法优化研究[D].南昌:江西师范大学,2015.
[24]汪训平,赵安.以植物丰度为辅助变量的钉螺空间分层采样研究[J].中国血吸虫病防治杂志,2017,29(4):420-425.
[25]Wang XY,He J,Yang K,et al.Applications of spatial technology in schistosomiasis control programme in the People's Republic of China[J].Adv Parasitol,2016,92:143-163.
[26]Chen Y.New approaches for calculating Moran's index of spatial autocorrelation[J].PLoS One,2013,8(7):e68336.
[27]Ord JK,Getis A.Local spatial autocorrelation statistics:distributional issues and an application[J].Geogr Anal,1995,27(4):286-306.
[28]常变蓉,李仁东,徐兴建,等.基于GIS空间自相关的江汉平原钉螺分布特征[J].长江流域资源与环境,2014,23(7):972-978.
[29]Clennon JA,King CH,Muchiri EM,et al.Spatial patterns of urinary schistosomiasis infection in a highly endemic area of coastal Kenya[J].Am J Trop Med Hyg,2004,70(4):443-448.
[30]唐咸艳,李峤,黄秋兰,等.时空扫描统计量三维可视化的实现[J].中国卫生统计,2015,32(4):693-694.
[31]Kulldorff M.A spatial scan statistic[J].Commun Stat,1997,26(6):1481-1496.
[32]董倩楠.基于Kulldorff扫描统计量的聚类方法研究及应用[D].北京:中国石油大学,2016.
[33]Block R.Software review:Scanning for clusters in space and time:A tutorial review of SaTScan[J].Soc Sci Comput Rev,2007,25(2):272-278.
[34]赵飞,朱蓉,张利娟,等.SaTScan在湖沼型血吸虫病聚集区域探测中的应用[J].中国血吸虫病防治杂志,2011,23(1):28-31.
[35]Meurs L,Mbow M,Boon N,et al.Micro-geographical heterogeneity in Schistosoma mansoni and S.haematobium infection and morbidity in a co-endemic community in northern Senegal[J].PLoSNegl Trop Dis,2013,7(12):e2608.
[36]Tango T,Takahashi K.A flexibly shaped spatial scan statistic for detecting clusters[J].Int J Health Geogr,2005,4:11.
[37]Zhu H,Cai SX,Liu JB,et al.A spatial analysis of human Schistosoma japonicum infections in Hubei,China,during 2009-2014[J].Parasit Vectors,2016,9(1):529.
[38]Magalhaes RJ,Clements AC,Patil AP,et al.The applications of model-based geostatistics in helminth epidemiology and control[J].Adv Parasitol,2011,74:267-296.
[39]Schur N,Utzinger J,Vounatsou P.Modelling age-heterogeneous Schistosoma haematobium and S.mansoni survey data via alignment factors[J].Parasit Vectors,2011,4:142.
[40]Chammartin F,Hürlimann E,Raso G,et al.Statistical methodological issues in mapping historical schistosomiasis survey data[J].Acta Trop,2013,128(2):345-352.
[41]Sturrock HJW,Pullan RL,Kihara JH,et al.The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya[J].PLoS Negl Trop Dis,2013,7(1):e2016.
[42]Schur N,Hürlimann E,Stensgaard A,et al.Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling[J].Acta Trop,2013,128(2):365-377.
[43]Seto EYW,Sousa-Figueiredo JC,Betson M,et al.Patterns of intestinal schistosomiasis among mothers and young children from Lake Albert,Uganda:water contact and social networks inferred from wearable global positioning system dataloggers[J].Geospat Health,2012,7(1):1-13.
[44]Rathbun S L.Interpolation of spatial data:Some theory for Kriging[J].Technometrics,2000,42(4):436-437.
[45]Pullan RL,Sturrock HJ,Soares MR,et al.Spatial parasite ecology and epidemiology:a review of methods and applications[J].Parasitology,2012,139(14):1870-1887.
[46]Vounatsou P,Raso G,Tanner M,et al.Bayesian geostatistical modelling for mapping schistosomiasis transmission[J].Parasitology,2009,136(13):1695-1705.
[47]Pullan RL,Kabatereine NB,Quinnell RJ,et al.Spatial and genetic epidemiology of hookworm in a rural community in Uganda[J].PLoS Negl Trop Dis,2010,4(6):e713.
[48]Soares MR,Barnett AG,Clements AC.Geographical analysis of the role of water supply and sanitation in the risk of helminth infections of children in West Africa[J].Proc Natl Acad Sci U S A,2011,108(50):20084-20089.
[49]Oliveira VD.Bayesian analysis of conditional autoregressive models[J].Ann Inst Statist Math,2012,64(1):107-133.
[50]Hoef JMV,Peterson EE,Hooten MB,et al.Spatial autoregressive models for statistical inference from ecological data[J].Ecol Monogr,2018,88(1):36-59.
[51]Yang GJ,Vounatsou P,Zhou XN,et al.A Bayesian-based approach for spatio-temporal modeling of county level prevalence of Schistosoma japonicum infection in Jiangsu province,China[J].Int J Parasitol,2005,35(2):155-162.
[52]Hu Y,Ward MP,Xia C,et al.Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach[J].Sci Rep,2016,6:24173.
[53]Lee D.A comparison of conditional autoregressive models used in Bayesian disease mapping[J].Spat Spatiotemporal Epidemiol,2011,2(2):79-89.
[54]Manyangadze T,Chimbari MJ,Gebreslasie M,et al.Risk factors and micro-geographical heterogeneity of Schistosoma haematobium in Ndumo area,Mkhanyakude district,KwaZulu-Natal,South Africa[J].Acta Trop,2016,159:176-184.
[55]Huang B,Wu B,Barry M.Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J].Int J Geogr Infor Sci,2010,24(3):383-401.
[56]Ge L,Zhao Y,Sheng Z,et al.Construction of a seasonal differencegeographically and temporally weighted regression(SD-GTWR)model and comparative analysis with GWR-based models for hemorrhagic fever with renal syndrome(HFRS)in Hubei Province(China)[J].Int J Environ Res Public Health,2016,13(11):1062.
[57]Indrayani F,Pramoedyo H,Iriany A,et al.Geographically and temporally weighted regression modeling in analyzing factors affecting the spread of dengue fever in Malang[J].J Exp Life Sci,2018,8(2):71-74.
[58]Goldstein H,Browne W,Rasbash J.Multilevel modelling of medical data[J].Stat Med,2002,21(21):3291-3315.
[59]Browne WJ,Goldstein H,Rasbash J.Multiple membership multiple classification(MMMC)models[J].StatModel,2001,1(2):103-124.
[60]Manyangadze T,Chimbari MJ,Gebreslasie M,et al.Application of geo-spatial technology in schistosomiasis modelling in Africa:a review[J].Geospat Health,2015,10(2):326.2018-12-18