安徽疟疾疫情时空分析及影响因素研究
|
摘要
背景:疟疾是一种由疟原虫引起的、经按蚊叮咬而传播的重要的寄生虫病。全球仍有107个国家和地区,约32亿人口受到疟疾的威胁,每年有3亿多人受感染,病死者超过100万。上世纪50、60年代,我国疟疾流行严重,经过半个多世纪的努力,我国疟疾防治工作取得巨大成效,20世纪末全国疟疾疫情下降到最低,但近年来疟疾发病数呈明显回升。2000年以来以安徽省为代表的中部地区在北纬32°以北的单一中华按蚊区陆续出现疟疾疫情回升、小暴发点或局部暴发流行。影响疟疾流行的因素复杂,寄生虫、蚊媒、人类宿主和环境等因素及其交互作用决定了疟疾传播和感染的风险。近年来基于地理信息系统的3S已成为疟疾等自然疫源性传染病研究新的技术手段与方法,而将统计学方法与空间技术相结合也成为一个新的研究方向。
目的:通过对20世纪90年代以来安徽省疟疾疫情时空分析及影响因素研究,为该省及类似地区的疟疾防控提供科学依据。通过尝试3S空间分析技术与统计分析方法在疟疾研究领域的综合应用,为类似的研究提供方法学参考。
方法:收集整理1990～2006年安徽省疟疾疫情监测数据,安徽省1:1 000 000县区级数字区,并加工处理安徽省1:5万的乡镇边界图。采用空间分析技术提取安徽省各乡镇温度、降雨量等气象监测数据,海拔、NDVI、湿度指数、水体等环境数据以及人口、GDP等数据,建立乡镇疟疾流行的地理信息系统数据库。对淮北地区高发自然村庄采用抽样调查及GPS定位的方法,收集居民生产生活及行为因素、疟疾病例诊治及水体定位等相关信息,建立村庄尺度的研究数据库。综合应用时空扫描聚类分析方法、时间序列分析方法、主成分分析及logistic回归模型、Poisson回归模型等统计分析方法对资料进行分析处理。所采用的软件包括Arc GIS 9.0软件、SaTScan7.0空间聚类分析软件、SAS9.1及SPSS13.0统计分析软件。
结果:①“2004～2006年淮北地区”是安徽省20世纪90年代以来疟疾流行新“热点”,淮北地区疟疾传播季节延长。②时间纵向上,温度、降雨量与疟疾发病率序列呈现显著的互相关关系,即“月平均气温”升高则“月疟疾发病率”上升,呈现正相关关系,在滞后0～3个月的相关性均显著;“月降雨量”增加则“月疟疾发病率”上升,在滞后1～3个月的相关性显著,不同的是,其相关性出现在至少滞后1个月,表明降雨量与疟疾发病间的关系相对缓慢一些。③地区横向上,安徽省各乡镇的疟疾发病率与温度、降雨量、NDVI和海拔因素有关,即“冬季/年最低气温”、“年降雨总量”、“海拔”和“NDVI”4个因素与安徽乡镇是否发生疟疾有关。在控制模型中其它自变量不变的情况下,前3个因素数值增加,则乡镇疟疾发生的可能性越小,而NDVI增加,则乡镇发生疟疾的可能性越大。④淮北地区居民不良生活生产行为使该地区疟疾发病风险增加,分析结果显示,村民露宿习惯比例每增加1%,村发生疟疾病例的风险增加18%,而耕种豆类农作物也增加了村民发生疟疾的危险。⑤采用2000～2007年5月安徽省疟疾月发病率建立的ARIMA时间序列模型对2007年6月份的发病率的预测值为5.437/10万,95%可信区间为[2.308/10万,12.808/0万],实际监测发病率为5.334/10万,预测的相对误差为1.9%,预测效果良好。
结论:本课题研究重点回答了2000年以来安徽省疟疾疫情回升的新的时空热点地区,并重点回答了安徽省南北地区疟疾疫情存在差异的主要影响因素。淮北地区除当地地形地貌、自然环境等特点影响其疟疾流行外,当地居民不良的生产生活行为因素加大了蚊媒接触机率,增加了疟疾发生的机率,研究结果具有很现实的指导意义。研究中虽然没有直接得出病例诊治及时性对该地区疟疾疫情的实际影响,但从控制传染源的理论出发,疟疾病人的及时发现和治疗无疑也是控制疟疾疫情流行的重要方面。课题研究结果将为安徽等我国中部疟疾流行地区建立疟疾的早期预警预测系统奠定基础。
此外,时间序列模型可以很好地拟合疟疾发病率在时间序列上的变动趋势,在人群免疫状态、人口流动、防制措施等人群疟疾易感性指标没有发生大幅度变化的情况下,可以用来对未来的疟疾发病率进行预测,为疟疾防治工作提供服务。
Background:Malaria is one of the important verminosis caused by a parasite that is transmitted to humans by a female mosquito's bite.Some 3.2 billion people live in areas at risk of malaria transmission in 107 countries.Each year 300 million are diagnosed with malaria and more the one million people die of malaria.
There were serious malaria epidemics in our country during 1950's and 1960's.Huge progress has been made in preventing and treating malaria after efforts for over half a century.The incidence rate of malaria reached the lowest level at the end of last century, and started to increase again at the central Anopheles sinensis area represented by Anhui province located 32°north latitude since 2000.Some small and large outbreaks at local areas were also reported.The complexity of malaria,its transmission and infection risks is determined by parasite,human host,environment and the interactions between them.The GIS,GPS and RS(abbreviated as 3S technology) have become the new methods and technical measures to study malaria and other zoonotic and vector-borne diseases.The combination of statistics and space technology has become the new research direction.
Objectives:The study explores the spatio-temporal characters of malaria epidemic situation and its determinants in Anhui province since 1990 in order that provide empirical evidence for the prevention and control policy of malaria's in the Anhui and similar districts. In addition to it also pave a way for the synthetic technology for malaria research field by using spatial statistics and 3S technologies,which can be used in the other similar study.
Methods:The surveillance data of malaria epidemic was collected and digital maps at the county(1:1 000 000),township(1:50 000) levels were processed.The township level data for the temperature,precipitations,altitude,NDVI,humidity and water environment,also including the population and GDP was extracted through the spatial analysis technology, and the corresponding GIS database was created.Then collecting village level data for the life styles,behavior factors,and disease diagnosis and treatment information of local residents in the high incidence rate area in Huaibei districts by the sample field survey, and water body locations can be obtained by GPS localizer.The village level analysis database was also created accordingly.Spatio-temporal scanning cluster analysis, time-series analysis,principal component analysis and logistic regression model,and Poisson regression model was used and the software used includes Arc GIS 9.0, SaTScan7.0,SAS9.1 and SPSS13.0.
Results:
1.The Huaibei area in Anhui province from 2004 to 2006 is the hot spot of malaria epidemic since 1990s',the duration of malaria transmission season was extended in Huaibei area.
2.Longitudinally significant correlation was found between malaria incidence rate and environmental factors(temperature and precipitation).The positive correlation existed between the 'increase' of monthly average temperature and monthly malaria incidence rates when the data were staggered to allow a lag of 0-3 month.The increasing monthly precipitation was accompanied by increase of monthly malaria incidence rate when they were transformed by 1-3 month lag.The correlations were significant for the malaria incidence rates with at least one month lag.This indicated that the relationship between precipitation and malaria incidence was relatively slow.
3.The incidence of malaria was related to temperature,precipitation,NDVI and altitude at township level analysis in Anhui province.Winter/the lowest temperature,annual precipitation,altitude and NDVI are the influencing factors for malaria.When other variables in the model are controlled,the higher the value of former 3 factors is the less chance of the malaria occurs.However,the increase of NDVI increases the chances to have malaria.
4.The poor life style and local behavior of residents in this area put them in higher risk to get infected.The results show that if the percentage of outdoor sleeping increases by 1%, the relative ratio of infected malaria will increase 8%.The cultivation of bean crop will increase the risk of infected malaria.
5.The ARIMA time series model established based upon the incidence data of Jan.2000 to May.2007,forecast that the incidence of malaria in June 2007 is 5.437/100 000(CI 2.308/100 000,12.808/100 000).The real incidence is 5.334/100000.The relative forecast error is 1.9%.
Conclusions:
This study mainly answered the reasons of the rebound of malaria epidemics in the new spatio-temporal hot spot at Anhui province since 2000.The combination of 3S spatial analysis techniques and statistics were used to identify the main factors explaining the differences of incidence rate between southern and northern areas.In addition to the terrain and natural environment in Huaibei area,the tillage style and poor lifestyle behavior factors increased contact with mosquito,so this increased the chance to be infected for the residents.The results nail down the focus of malaria prevention and control,which is of momentous current significance for the anti-malaria.The results did not support that the effects of the timeliness of malaria diagnosis,but based upon the theory of controlling infectious source,it is very important to find and treat patient timely in order to control the malaria.The research will lay a solid foundation for early warning and early forecast in the central areas such as Anhui.
Moreover,time series model can be used to fit the changing trends of malaria incidence rate at different times.If the population immunization status,population mobility, prevention measures and other susceptibility indicators are relative stable,it can be used to forecast the incidence of malaria in the future,and to provide services for prevention and control of malaria epidemics.
目的:通过对20世纪90年代以来安徽省疟疾疫情时空分析及影响因素研究,为该省及类似地区的疟疾防控提供科学依据。通过尝试3S空间分析技术与统计分析方法在疟疾研究领域的综合应用,为类似的研究提供方法学参考。
方法:收集整理1990～2006年安徽省疟疾疫情监测数据,安徽省1:1 000 000县区级数字区,并加工处理安徽省1:5万的乡镇边界图。采用空间分析技术提取安徽省各乡镇温度、降雨量等气象监测数据,海拔、NDVI、湿度指数、水体等环境数据以及人口、GDP等数据,建立乡镇疟疾流行的地理信息系统数据库。对淮北地区高发自然村庄采用抽样调查及GPS定位的方法,收集居民生产生活及行为因素、疟疾病例诊治及水体定位等相关信息,建立村庄尺度的研究数据库。综合应用时空扫描聚类分析方法、时间序列分析方法、主成分分析及logistic回归模型、Poisson回归模型等统计分析方法对资料进行分析处理。所采用的软件包括Arc GIS 9.0软件、SaTScan7.0空间聚类分析软件、SAS9.1及SPSS13.0统计分析软件。
结果:①“2004～2006年淮北地区”是安徽省20世纪90年代以来疟疾流行新“热点”,淮北地区疟疾传播季节延长。②时间纵向上,温度、降雨量与疟疾发病率序列呈现显著的互相关关系,即“月平均气温”升高则“月疟疾发病率”上升,呈现正相关关系,在滞后0～3个月的相关性均显著;“月降雨量”增加则“月疟疾发病率”上升,在滞后1～3个月的相关性显著,不同的是,其相关性出现在至少滞后1个月,表明降雨量与疟疾发病间的关系相对缓慢一些。③地区横向上,安徽省各乡镇的疟疾发病率与温度、降雨量、NDVI和海拔因素有关,即“冬季/年最低气温”、“年降雨总量”、“海拔”和“NDVI”4个因素与安徽乡镇是否发生疟疾有关。在控制模型中其它自变量不变的情况下,前3个因素数值增加,则乡镇疟疾发生的可能性越小,而NDVI增加,则乡镇发生疟疾的可能性越大。④淮北地区居民不良生活生产行为使该地区疟疾发病风险增加,分析结果显示,村民露宿习惯比例每增加1%,村发生疟疾病例的风险增加18%,而耕种豆类农作物也增加了村民发生疟疾的危险。⑤采用2000～2007年5月安徽省疟疾月发病率建立的ARIMA时间序列模型对2007年6月份的发病率的预测值为5.437/10万,95%可信区间为[2.308/10万,12.808/0万],实际监测发病率为5.334/10万,预测的相对误差为1.9%,预测效果良好。
结论:本课题研究重点回答了2000年以来安徽省疟疾疫情回升的新的时空热点地区,并重点回答了安徽省南北地区疟疾疫情存在差异的主要影响因素。淮北地区除当地地形地貌、自然环境等特点影响其疟疾流行外,当地居民不良的生产生活行为因素加大了蚊媒接触机率,增加了疟疾发生的机率,研究结果具有很现实的指导意义。研究中虽然没有直接得出病例诊治及时性对该地区疟疾疫情的实际影响,但从控制传染源的理论出发,疟疾病人的及时发现和治疗无疑也是控制疟疾疫情流行的重要方面。课题研究结果将为安徽等我国中部疟疾流行地区建立疟疾的早期预警预测系统奠定基础。
此外,时间序列模型可以很好地拟合疟疾发病率在时间序列上的变动趋势,在人群免疫状态、人口流动、防制措施等人群疟疾易感性指标没有发生大幅度变化的情况下,可以用来对未来的疟疾发病率进行预测,为疟疾防治工作提供服务。
Background:Malaria is one of the important verminosis caused by a parasite that is transmitted to humans by a female mosquito's bite.Some 3.2 billion people live in areas at risk of malaria transmission in 107 countries.Each year 300 million are diagnosed with malaria and more the one million people die of malaria.
There were serious malaria epidemics in our country during 1950's and 1960's.Huge progress has been made in preventing and treating malaria after efforts for over half a century.The incidence rate of malaria reached the lowest level at the end of last century, and started to increase again at the central Anopheles sinensis area represented by Anhui province located 32°north latitude since 2000.Some small and large outbreaks at local areas were also reported.The complexity of malaria,its transmission and infection risks is determined by parasite,human host,environment and the interactions between them.The GIS,GPS and RS(abbreviated as 3S technology) have become the new methods and technical measures to study malaria and other zoonotic and vector-borne diseases.The combination of statistics and space technology has become the new research direction.
Objectives:The study explores the spatio-temporal characters of malaria epidemic situation and its determinants in Anhui province since 1990 in order that provide empirical evidence for the prevention and control policy of malaria's in the Anhui and similar districts. In addition to it also pave a way for the synthetic technology for malaria research field by using spatial statistics and 3S technologies,which can be used in the other similar study.
Methods:The surveillance data of malaria epidemic was collected and digital maps at the county(1:1 000 000),township(1:50 000) levels were processed.The township level data for the temperature,precipitations,altitude,NDVI,humidity and water environment,also including the population and GDP was extracted through the spatial analysis technology, and the corresponding GIS database was created.Then collecting village level data for the life styles,behavior factors,and disease diagnosis and treatment information of local residents in the high incidence rate area in Huaibei districts by the sample field survey, and water body locations can be obtained by GPS localizer.The village level analysis database was also created accordingly.Spatio-temporal scanning cluster analysis, time-series analysis,principal component analysis and logistic regression model,and Poisson regression model was used and the software used includes Arc GIS 9.0, SaTScan7.0,SAS9.1 and SPSS13.0.
Results:
1.The Huaibei area in Anhui province from 2004 to 2006 is the hot spot of malaria epidemic since 1990s',the duration of malaria transmission season was extended in Huaibei area.
2.Longitudinally significant correlation was found between malaria incidence rate and environmental factors(temperature and precipitation).The positive correlation existed between the 'increase' of monthly average temperature and monthly malaria incidence rates when the data were staggered to allow a lag of 0-3 month.The increasing monthly precipitation was accompanied by increase of monthly malaria incidence rate when they were transformed by 1-3 month lag.The correlations were significant for the malaria incidence rates with at least one month lag.This indicated that the relationship between precipitation and malaria incidence was relatively slow.
3.The incidence of malaria was related to temperature,precipitation,NDVI and altitude at township level analysis in Anhui province.Winter/the lowest temperature,annual precipitation,altitude and NDVI are the influencing factors for malaria.When other variables in the model are controlled,the higher the value of former 3 factors is the less chance of the malaria occurs.However,the increase of NDVI increases the chances to have malaria.
4.The poor life style and local behavior of residents in this area put them in higher risk to get infected.The results show that if the percentage of outdoor sleeping increases by 1%, the relative ratio of infected malaria will increase 8%.The cultivation of bean crop will increase the risk of infected malaria.
5.The ARIMA time series model established based upon the incidence data of Jan.2000 to May.2007,forecast that the incidence of malaria in June 2007 is 5.437/100 000(CI 2.308/100 000,12.808/100 000).The real incidence is 5.334/100000.The relative forecast error is 1.9%.
Conclusions:
This study mainly answered the reasons of the rebound of malaria epidemics in the new spatio-temporal hot spot at Anhui province since 2000.The combination of 3S spatial analysis techniques and statistics were used to identify the main factors explaining the differences of incidence rate between southern and northern areas.In addition to the terrain and natural environment in Huaibei area,the tillage style and poor lifestyle behavior factors increased contact with mosquito,so this increased the chance to be infected for the residents.The results nail down the focus of malaria prevention and control,which is of momentous current significance for the anti-malaria.The results did not support that the effects of the timeliness of malaria diagnosis,but based upon the theory of controlling infectious source,it is very important to find and treat patient timely in order to control the malaria.The research will lay a solid foundation for early warning and early forecast in the central areas such as Anhui.
Moreover,time series model can be used to fit the changing trends of malaria incidence rate at different times.If the population immunization status,population mobility, prevention measures and other susceptibility indicators are relative stable,it can be used to forecast the incidence of malaria in the future,and to provide services for prevention and control of malaria epidemics.
引文
1.田庆久,闵祥军.植被指数研究进展.地球科学进展,327-333.
2.Robert KW,B.L.Application of remote sensing to arthropod vector surveillance.Am J Trop Med Hyg 2002,134-144.
3.李立明主编.流行病学(第四版)[M](人民卫生出版社,2000,北京).
4.WHO.(2005WHO.http://www.who.int/features/2005/malaria/zh/).
5.朱继民,汤林华.疟疾及其蚊媒的预测方法研究进展[J].中国寄生虫学与寄生虫病杂志,2007 25.150-153.
6.Kazembe,L.N.Spatial modeling and risk factors of malaria incidence in northern Malawi[J].Acta Tropica.2007,126-137.
7.Marlies H Craig,B.L.S.,Musawenkosi LH Mabaso.Developing a spatial-statistical model and map of hospital malaria prevalence in Botswana using a staged variable selection procedure[J].International Journal of Health Geographics.2007 6,1-15.
8.http://pages.unibas.ch/diss/2001/DissB 5934.pdf.
9.方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志2005,26,839-842.
10.方积乾.医学统计学与电脑实验(第二版)(ed.方积乾,宇传华,高桂明)(上海科学技术出版社,上海,2001).
11.陈峰编著,陈启光审阅.医用多元统计分析方法.(中国统计出版社,北京,2001).
12.郭祖超.医学统计学(ed.胡良平)(人民军医出版社,北京,2001).
13.Lawless,J.E.Negative Binomial and Mixed Poisson Regression.The Canadian Journal of Statistics,209-225(1987).
14.中华人民共和国传染病防治法.(中国法制出版社).
15.Frank C Tanser,B.S.,David le Sueur.Potential effect of climate change on malaria transmission in Africa.Lancet 2003 362,1792-98(2003).
16.许信旺,潘根兴,曹志红,等.安徽省土壤有机碳空间差异及影响因素.地理研究,2007年11月26,1077-1086.
17.蔡爱民,查良松.安徽省1949-2003年洪旱灾害时序分形特征研究.水土保持通报,200525.73-77.
18.高琪,尚乐园,顾政诚.我国中部地区目前的疟疾形势.中国寄生虫病防治杂志,2002年8月15,193-194.
19.M,K.A spatial scan statistic,Communications in Statistics:Theory and Methods.1997,26,1481-1496.
20.EmmaKC,JamesIM.Connecticut.Emerging Infectious Disease 2002,8,943-948(Spatial analysis of human granulocytic ehrilichiosis near Lyme).
21.方立群,曹务春,敦哲,等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布.中华流行病学杂志2003,24,1020-1023.
22.周水森,王漪,汤林华.2005年全国疟疾形势.中国寄生虫学与寄生虫病学杂志2006,24,401-403.
23.刘亦仁.对我国中部地区疟疾流行与控制的几点认识.中国寄生虫病防治杂志,2005年8月18,241-243.
24.焦岩,孟庆跃.我国疟疾防治形势、政策干预和挑战分析.中国初级卫生保健,2006年1月20.
25.温亮,徐德忠,张治英.基于GIS的疟疾预警系统构建的初步分析.中国公共卫生,2004年5月20,626-627.
26.沈毓祖.安徽省中华按蚊地区疟疾传播因素的调查.中国病原生物学杂志,2006年8月1.301-303.
27.于国伟,汤林华.云南疟疾疫情及媒介评价体系与地理信息系统研究.中国疾病预防控制中心博士论文.
28.朱继民,汤林华,李白坤,等.部分社会因素与疟疾疫情关系的初步分析.国际医学寄生虫病杂志,2007年3月34,76-78.
29.孙振球,徐勇勇.医学统计学[M](人民卫生出版社,2002,北京).
30.张晋昕,何大卫,王亚拉等.ARIMA模型在医院卫生消耗材料需求量预测中的应用.中国医院统计,1999年12月6,210-212.
31.温亮,徐德忠,林明和等.应用时间序列模型预测疟区疟疾发病率.第四军医大学学报,2004 25.507-510.
32.PR,H.Climate change and waterborne and vector-borne disease.Journal of Applied Microbiology,37-46(2003).
33.Hoshen MB,M.A.A weather-driven model of malaria transmission[J].Malaria Journal.2004 6,32-46.
34.Serandour J,G.J.,Boyer S,et al.How human practices have affected vector-borne diseases in the past:a study of malaria transmission in Alpine valleys.Mala J 2007,9,115-125.
35.Malone等,杨.周.J.B.多因素空间复合模型预测我国疟疾流行区分布态势.中国寄生虫学与寄生虫病学杂志,2002年6月20,145-147.
36.程义亮,樊天宝.山东省间日疟暴发流行因素的探讨[C].
37.刘亦仁.我国中部地区消灭间日疟策略初步探讨[C].(湖北襄樊市卫生防疫站印,1974).
38.Su SZ,C.Y.,Qu MQ,et al.Bionomics of Anophelec sinensis Wiedmann in Henan Province[J].Chin Med J 1983.96,701-708.
39.Hartman J,E.K.,McConnell K,Chan N,Weyant J.Climate suitability for stable malaria transmission in Zimbabwe under different climate change scenarios.Glob Change Hum Health 2002,42-54.
40.P,T.The malaria problem in Zimbabwe epidemiology.Centr Afr J Med 1985,63-165.
41.Taylor P,M.S.A review of the malaria situation in Zimbabwe with a special reference to the period 1972-1248.Trans R Soc Trop Med Hyg 1986,12-19.
42.Baird JK,A.S.,Utz GC,Koram K,Barcus MJ,Jones TR,Fryauff DJ,Binka FN,Hoffman SL,Nkrumah FN.Seasonal malaria attack rates in infants and young children in Northern Ghana.Am J Trop Med Hyg 2002 66,280-286.
43.Thomson M,I.M.,Connor S,Dilley M,Ward N.Malaria early warning and seasonal climate forecasts.Lancet 2003 362,280.
44.王凌.2003年中国主要天气气候特点及异常气候事件.气象知识(2004).
45.贾尚春综述,邹铮,徐伏牛审校.全球气候变暖对疟疾传播的潜在影响.中国寄生虫病防治杂志,2002年17,63-65(2004年).
46.于新蕊,陶立林.厄尔尼诺与人类健康[J].国外医学社会医学分册,2001,175-178.
47.张怀念,单勇,汪付华.in气象条件对不同土壤类型施肥效果的影响(安徽省气象局).
48.2006年安徽省八大重要气象事件.(安徽省气象学会).
49.于国伟综述,汤林华审校.遥感地理信息系统技术在疟疾研究中的应用.中国寄生虫学 与寄生病防治杂志,2004年22,119-121.
50.Jean-Olivier Guintran,C.D.a.P.T.Systems for the early detection of malaria epidemics in Africa.WHO/HTM/MAL/2006.1115(2006).
51.徐天德.
52.张晋昕,王亚拉,何大卫等.含缺失值时间序列的ARMA模型拟合.中国卫生统计,2000年8月17,197-199.
53.张晋昕,方积乾,凌莉等.基于预报效果的ARIMA模型筛选.中国卫生统计,2004年2月21,6-9.
54.朱淮民.疟疾监测及早期预警系统.第二军医大学学报25,5-8(2004).
55.王丽萍,郭岩,郭青,等.2006年全国法定传染病网络报告信息质量评价.疾病监测,2007,412-414.
56.Thomson MC,M.S.,Phindela T,Connor SJ.Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana.Am J Trop Med Hyg 2005 73,214-221.
57.WHO.
58.Teklehaimanot HD,S.J.,Teklehaimanot A,et al.Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅱ.Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.Mala J 2004,19,44-54.
59.Hay SI,C.J.,Rogers DJ,et al.Climate change and the resurgence of malaria in the East African highlands.Nature 2002 415,905-09.
60.Patz J,H.M.,Rosenzweig C,et al.Climate change:regional warming and malaria resurgence.Nature 2002 420,627-28.
61.Mabaso MLH,C.M.,Vounatsou P,Smith T.Towards empirical description of malaria seasonality in southern Africa:the example of Zimbabwe Trop Med Int Health 2005,909-918.
62.Hailay D Teklehaimanot,M.L.,Awash Teklehaimanot,Joel Schwartz.Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅰ.Patterns of lagged weather effects reflect biological mechanisms.Malaria Journal 41(2004).
1、李立明主编.流行病学(第四版)[M].北京:人民卫生出版社,2000:311.
2、《2005年世界疟疾报告》,2005WHO.http://www.who.int/features/2005/malaria/zh/.
3、朱继民综述,汤林华审校.疟疾及其蚊媒的预测方法研究进展.中国寄生虫学与寄生虫病杂志,2007,25(2):150-153.
4、Clark KC,Mclafferty SL,Tempalski BJ.On epidemiology and Geographic information systems:a review and discussion of future directions[J].Emerging InfDis,1996,2:85-92.
5、Aronoff S.Geographic information systems:A management perspective[M].WDL Publication,Ottawa,Canada,1989:5.
6、李宪中,崔颖,罗宾.地理信息系统(GIS)及其应用.交通世界,2002,12:48-50.
7、赵文娟,方立群,冯丹等.基于空间信息技术的北京市肾综合征出血热宿主动物感染与环境因素关系研究,中国病原生物学杂志,2006,1(1):3-7.
8、伍卫平GeorgeDavis等,遥感用于鄱阳湖区钉螺孳生地的监测,中国寄生虫学与寄生虫病杂志,2002,20(4):205-208.
9、杨国静,周晓农,JB Malone,等.应用遥感资料预测疟疾流行趋势的可行性研究[J].中国寄生虫病防治杂志,2002(15)6:339-341.
10、杨国静,周晓农,JB Malone,等.江苏省疟疾流行地理信息系统预测模型的研究.中华预防医学杂志, 2002(36)2:103-105.
11、于国伟综述,汤林华审校.遥感地理信息系统技术在疟疾研究中的应用,中国寄生虫学与寄生虫病杂志,2004,22(2)119-121.
12、李奔福,李崇珍,张再兴,等.应用GIS技术分析云南省2004年疟疾流行状况.中国病原生物学杂志,2007(2)2:147-149.
13、张小萍,金小林,杨国静,等.江苏省疟疾流行态势的空间分析.中国人兽共患病杂志,2002(18)1:120-121.
14、苏永强,张治英,徐德忠等.运用空间局部内插研究海南省疟疾空间分布特征.中华流行病学杂志,2003(24)4:269-271.
15、Briggs DJ,Elliott P.The use of geographic information system in studies on environment and health[J].Wld Hlth Statist Quart,1995,48:85-94.
16、Indartna K,Hutubeddy R,Chupraphawan S,et al.Application of geographic information system to co-analysis of disease and economic research:dengue and malaria in Thailand[J].Southeast Asia J Trop Med Public Health,2000,29:669-676.
17、方立群,曹务春.3S技术在自然疫源性疾病中的应用及发展前景.中国人兽共患病杂志,2004,20(9):807-810.
18、方立群,曹务春,敦哲等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布[J].中华流行病学杂志.2003,24:1020-1023.
19、方立群,曹务春,陈化新等.应用地理信息系统分析中国肾综合征出血热的空间分布[J].中华流行病学杂志.2003,24:181-184.
20、盛慧锋,汤林华.疟疾暴发流行的监测与预测.国际医学寄生虫病杂志,2007,34(3):163-168.
21、Omumbo JA,Hay SI,Goetz SJ,Snow RW,Rogers DJ(2002).Updating historical maps of malaria transmission intensity in East Africa using remote sensing.Photogram metric Engineering and Remote Sensing 68(2):161-166.
22、Thomson MC,Graves P,Bamston T,Bell M,Ceccato P,del Corral J,Gianini A,Woldo Geogis T,Jaiteh M,Levy M,Lukang L,Connor S(2004).Towards a malaria early warning system for Eritrea.Interim Report,IRI.
23、WHO,RBM.Prevention and control of malaria epidemics,3rd meeting of the technical support network (2002).Available at:http://www.who.int/malaria/cmc_upload/0/000/015/827/3epidemics_report.pdf.
24、Beck LR,Rodriguez MH,Dister SW,Rodriguez AD,Rejmankva E,Ulloa A,Meza RA,Roberts DR,Paris JF,Spanner MA,Washino RK,Hacker C,Legters LJ(1994).Remote sensing as a landscape epidemiological tool to identify villages at high risk for malaria transmission.American Journal of Tropical Medicine and Hygiene 51(3):271-280.
25、Beck LR,Rodriguez MH,Dister SW,Rodriguez AD,Washino RK,Roberts DR,Spanner MA(1997).Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas,Mexico.American Journal Of Tropical Medicine And Hygiene 56(1):99-106.
26、G.Dennis Shanks,Simon I.Hay,Judy A.Omumbo.amlaria in Kenya's western highlands[J].Emerging infectious diseases,2005(11)9:1425-1432.
27、杨国静,周晓农,JB Malong,等.多因素空间复合模型预测我国疟疾流行区分布态势[J].中国寄生虫学与寄生虫病杂志,2002(20)3:145-147.
28、Thomas Kistemann,Friederike Dangendorf.New perspectives on the use of Geographical Information Systems(GIS) in environmental health Sciences.Int.J.Hyg.Environ.Health 205,169-181(2002).
29、Lawrence N.Kazembe.Spatial modeling and risk factors of malaria incidence in northern Malawi[J].Acta Tropica.2007(102):126-137.
30. Marlies H Craig, Brain L Sharp, Musawenkosi LH Mabaso. Developing a spatial-statistical model and map of hospital malaria prevalence in Botswana using a staged variable selection procedure[J]. International Journal of Health Geographics. 2007(6)44: 1-15.
31. Spatial statistical analysis, modeling and mapping of malaria in Africa. Available at http://pages.unibas.ch/diss/2001/DissB_5934.pdf
32. WHO, RBM. a framework for field research in Africa concepts indicators and partners (2001).
33. Lawrence N. Kazembe, Immo Kleinschmidt, Timothy H Holtz and Brian L Sharp. Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. International Journal of Health Geographics, 2006,5:41, Http://www.ij-healthgeographics.com/content/5/1/41.
1.IPCC.Climate Change 2007 synthesis report[OL],http://www.ipcc.ch/pdf/assessment-repo rt/ar4/syr/ar4_syr_spm.pdf
2.UNFCCC.联合国气候变化框架公约[OL].http://unfccc.int/resource/docs/convkp/convchin.pdf
3.UNFCCC.Climate change information kit[OL].http://www.unep.org/Themes/climatechang e/PDF/infokit2003-E.pdf
4.UNFCCC.Climate Change:Impacts,Vulnerabilities and Adaptation in Developing Count ries[OL],http://unfccc.int/files/essential_background/background_publications_htmlpdf/applicati on/txt/pub_07_impacts.pdf
5.Hunter PR.Climate change and waterbome and vector-borne disease[J].Journal of Ap plied Microbiology.2003,94 Supplement:37-46.
6.Hoshen MB,Morse AP.A weather-driven model of malaria transmission[J].MalaJ.2004,6(3):32-46.
7.Serandour J,Girel J,Boyer S,et al.How human practices have affected vector-borne d iseases in the past:a study of malaria transmission in Alpine valleys[J].Mala J.2007,9(6):115-125.
8.WHO.The Africa Malaria Report 2003[OL].http://www.rbm.who.int/amd2003/amr2003/p df/amr2003.pdf
9.Rita R.Colwell.global climate infectious disease:the cholera paradigm[J].science.1996,274(5295):2025-2031.
10.Lipp EK,Huq A,Colwell RR.Effects of global climate on infectious disease:the cho lera model[J].Clin Micro Rev.2002,15(4):757-770.
11.Koelle K,Rodo X,Pascual M,et al.Refractory periods and climate forcing in choler a dynamics[J].Nature.2005,436(7051):696-700.
12.WHO.Using climate to predict disease outbreaks:a review lOLl.http://www.who.int/e ntity/globalchange/publications/en/oeh0401.pdf
13.WHO.global strategic plan roll back malaria 2005-2015[OL].http://www.rbm.who.int/f orumV/docs/gsp_en.pdf
14.WHO.A framework for field research in Africa Malaria Early Warning Systems:conc epts,indicators and partners[OL],http://www.who.int/malaria/cmc_upload/0/000/014/807/mew s2.pdf
15.Grover-Kopec EK,Blumenthal MB,Ceccato P,et al.Web-based climate information re sources for malaria control in Africa[J].Mala J.2006,11(5):38-47.
16.Grover-Kopec E,Kawano M,Klaver RW,et al.An online operational rainfall-monitori ng resource for epidemic malaria early warning systems in Africa[J].Mala J.2005,21(4):6-11.
17.Teklehaimanot HD,Schwartz J,Teklehaimanot A,et al.Weather-based prediction of Pl asmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅱ.Weather-based predi ction systems perform comparably to early detection systems in identifying times for interv entions[J].Mala J.2004,19(3):44-54.
18.谈荣梅,陈坤,屠春丽.气象因素变化与霍乱发病的相关性研究[J].中国公共卫生.2003,19(4):416-417.
1、李立明主编.流行病学(第四版)[M].北京:人民卫生出版社,2000:311-313.
2、周水森,王漪,汤林华.2005年全国疟疾形势.中国寄生虫学与寄生虫病学杂志,2006,24(6):401-403.
3、中华人民共和国传染病防治法(2004年8月28日修订).中国法制出版社.
4、方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志,2005,26(11):839-842.
5、Kulldorff M.A spatial scan statistic.Communications in Statistics:Theory and Methods,1997,26:1481-1496.
6、EmmaKC,JamesIM.Spatial analysis of human granulocytic ehrilichiosis near Lyme,Connecticut.Emerging Infectious Disease,2002,8:943-948.
7、方立群,曹务春,敦哲,等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布.中华流行病学杂志,2003,24(11):1020-1023.
2.Robert KW,B.L.Application of remote sensing to arthropod vector surveillance.Am J Trop Med Hyg 2002,134-144.
3.李立明主编.流行病学(第四版)[M](人民卫生出版社,2000,北京).
4.WHO.(2005WHO.http://www.who.int/features/2005/malaria/zh/).
5.朱继民,汤林华.疟疾及其蚊媒的预测方法研究进展[J].中国寄生虫学与寄生虫病杂志,2007 25.150-153.
6.Kazembe,L.N.Spatial modeling and risk factors of malaria incidence in northern Malawi[J].Acta Tropica.2007,126-137.
7.Marlies H Craig,B.L.S.,Musawenkosi LH Mabaso.Developing a spatial-statistical model and map of hospital malaria prevalence in Botswana using a staged variable selection procedure[J].International Journal of Health Geographics.2007 6,1-15.
8.http://pages.unibas.ch/diss/2001/DissB 5934.pdf.
9.方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志2005,26,839-842.
10.方积乾.医学统计学与电脑实验(第二版)(ed.方积乾,宇传华,高桂明)(上海科学技术出版社,上海,2001).
11.陈峰编著,陈启光审阅.医用多元统计分析方法.(中国统计出版社,北京,2001).
12.郭祖超.医学统计学(ed.胡良平)(人民军医出版社,北京,2001).
13.Lawless,J.E.Negative Binomial and Mixed Poisson Regression.The Canadian Journal of Statistics,209-225(1987).
14.中华人民共和国传染病防治法.(中国法制出版社).
15.Frank C Tanser,B.S.,David le Sueur.Potential effect of climate change on malaria transmission in Africa.Lancet 2003 362,1792-98(2003).
16.许信旺,潘根兴,曹志红,等.安徽省土壤有机碳空间差异及影响因素.地理研究,2007年11月26,1077-1086.
17.蔡爱民,查良松.安徽省1949-2003年洪旱灾害时序分形特征研究.水土保持通报,200525.73-77.
18.高琪,尚乐园,顾政诚.我国中部地区目前的疟疾形势.中国寄生虫病防治杂志,2002年8月15,193-194.
19.M,K.A spatial scan statistic,Communications in Statistics:Theory and Methods.1997,26,1481-1496.
20.EmmaKC,JamesIM.Connecticut.Emerging Infectious Disease 2002,8,943-948(Spatial analysis of human granulocytic ehrilichiosis near Lyme).
21.方立群,曹务春,敦哲,等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布.中华流行病学杂志2003,24,1020-1023.
22.周水森,王漪,汤林华.2005年全国疟疾形势.中国寄生虫学与寄生虫病学杂志2006,24,401-403.
23.刘亦仁.对我国中部地区疟疾流行与控制的几点认识.中国寄生虫病防治杂志,2005年8月18,241-243.
24.焦岩,孟庆跃.我国疟疾防治形势、政策干预和挑战分析.中国初级卫生保健,2006年1月20.
25.温亮,徐德忠,张治英.基于GIS的疟疾预警系统构建的初步分析.中国公共卫生,2004年5月20,626-627.
26.沈毓祖.安徽省中华按蚊地区疟疾传播因素的调查.中国病原生物学杂志,2006年8月1.301-303.
27.于国伟,汤林华.云南疟疾疫情及媒介评价体系与地理信息系统研究.中国疾病预防控制中心博士论文.
28.朱继民,汤林华,李白坤,等.部分社会因素与疟疾疫情关系的初步分析.国际医学寄生虫病杂志,2007年3月34,76-78.
29.孙振球,徐勇勇.医学统计学[M](人民卫生出版社,2002,北京).
30.张晋昕,何大卫,王亚拉等.ARIMA模型在医院卫生消耗材料需求量预测中的应用.中国医院统计,1999年12月6,210-212.
31.温亮,徐德忠,林明和等.应用时间序列模型预测疟区疟疾发病率.第四军医大学学报,2004 25.507-510.
32.PR,H.Climate change and waterborne and vector-borne disease.Journal of Applied Microbiology,37-46(2003).
33.Hoshen MB,M.A.A weather-driven model of malaria transmission[J].Malaria Journal.2004 6,32-46.
34.Serandour J,G.J.,Boyer S,et al.How human practices have affected vector-borne diseases in the past:a study of malaria transmission in Alpine valleys.Mala J 2007,9,115-125.
35.Malone等,杨.周.J.B.多因素空间复合模型预测我国疟疾流行区分布态势.中国寄生虫学与寄生虫病学杂志,2002年6月20,145-147.
36.程义亮,樊天宝.山东省间日疟暴发流行因素的探讨[C].
37.刘亦仁.我国中部地区消灭间日疟策略初步探讨[C].(湖北襄樊市卫生防疫站印,1974).
38.Su SZ,C.Y.,Qu MQ,et al.Bionomics of Anophelec sinensis Wiedmann in Henan Province[J].Chin Med J 1983.96,701-708.
39.Hartman J,E.K.,McConnell K,Chan N,Weyant J.Climate suitability for stable malaria transmission in Zimbabwe under different climate change scenarios.Glob Change Hum Health 2002,42-54.
40.P,T.The malaria problem in Zimbabwe epidemiology.Centr Afr J Med 1985,63-165.
41.Taylor P,M.S.A review of the malaria situation in Zimbabwe with a special reference to the period 1972-1248.Trans R Soc Trop Med Hyg 1986,12-19.
42.Baird JK,A.S.,Utz GC,Koram K,Barcus MJ,Jones TR,Fryauff DJ,Binka FN,Hoffman SL,Nkrumah FN.Seasonal malaria attack rates in infants and young children in Northern Ghana.Am J Trop Med Hyg 2002 66,280-286.
43.Thomson M,I.M.,Connor S,Dilley M,Ward N.Malaria early warning and seasonal climate forecasts.Lancet 2003 362,280.
44.王凌.2003年中国主要天气气候特点及异常气候事件.气象知识(2004).
45.贾尚春综述,邹铮,徐伏牛审校.全球气候变暖对疟疾传播的潜在影响.中国寄生虫病防治杂志,2002年17,63-65(2004年).
46.于新蕊,陶立林.厄尔尼诺与人类健康[J].国外医学社会医学分册,2001,175-178.
47.张怀念,单勇,汪付华.in气象条件对不同土壤类型施肥效果的影响(安徽省气象局).
48.2006年安徽省八大重要气象事件.(安徽省气象学会).
49.于国伟综述,汤林华审校.遥感地理信息系统技术在疟疾研究中的应用.中国寄生虫学 与寄生病防治杂志,2004年22,119-121.
50.Jean-Olivier Guintran,C.D.a.P.T.Systems for the early detection of malaria epidemics in Africa.WHO/HTM/MAL/2006.1115(2006).
51.徐天德.
52.张晋昕,王亚拉,何大卫等.含缺失值时间序列的ARMA模型拟合.中国卫生统计,2000年8月17,197-199.
53.张晋昕,方积乾,凌莉等.基于预报效果的ARIMA模型筛选.中国卫生统计,2004年2月21,6-9.
54.朱淮民.疟疾监测及早期预警系统.第二军医大学学报25,5-8(2004).
55.王丽萍,郭岩,郭青,等.2006年全国法定传染病网络报告信息质量评价.疾病监测,2007,412-414.
56.Thomson MC,M.S.,Phindela T,Connor SJ.Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana.Am J Trop Med Hyg 2005 73,214-221.
57.WHO.
58.Teklehaimanot HD,S.J.,Teklehaimanot A,et al.Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅱ.Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.Mala J 2004,19,44-54.
59.Hay SI,C.J.,Rogers DJ,et al.Climate change and the resurgence of malaria in the East African highlands.Nature 2002 415,905-09.
60.Patz J,H.M.,Rosenzweig C,et al.Climate change:regional warming and malaria resurgence.Nature 2002 420,627-28.
61.Mabaso MLH,C.M.,Vounatsou P,Smith T.Towards empirical description of malaria seasonality in southern Africa:the example of Zimbabwe Trop Med Int Health 2005,909-918.
62.Hailay D Teklehaimanot,M.L.,Awash Teklehaimanot,Joel Schwartz.Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅰ.Patterns of lagged weather effects reflect biological mechanisms.Malaria Journal 41(2004).
1、李立明主编.流行病学(第四版)[M].北京:人民卫生出版社,2000:311.
2、《2005年世界疟疾报告》,2005WHO.http://www.who.int/features/2005/malaria/zh/.
3、朱继民综述,汤林华审校.疟疾及其蚊媒的预测方法研究进展.中国寄生虫学与寄生虫病杂志,2007,25(2):150-153.
4、Clark KC,Mclafferty SL,Tempalski BJ.On epidemiology and Geographic information systems:a review and discussion of future directions[J].Emerging InfDis,1996,2:85-92.
5、Aronoff S.Geographic information systems:A management perspective[M].WDL Publication,Ottawa,Canada,1989:5.
6、李宪中,崔颖,罗宾.地理信息系统(GIS)及其应用.交通世界,2002,12:48-50.
7、赵文娟,方立群,冯丹等.基于空间信息技术的北京市肾综合征出血热宿主动物感染与环境因素关系研究,中国病原生物学杂志,2006,1(1):3-7.
8、伍卫平GeorgeDavis等,遥感用于鄱阳湖区钉螺孳生地的监测,中国寄生虫学与寄生虫病杂志,2002,20(4):205-208.
9、杨国静,周晓农,JB Malone,等.应用遥感资料预测疟疾流行趋势的可行性研究[J].中国寄生虫病防治杂志,2002(15)6:339-341.
10、杨国静,周晓农,JB Malone,等.江苏省疟疾流行地理信息系统预测模型的研究.中华预防医学杂志, 2002(36)2:103-105.
11、于国伟综述,汤林华审校.遥感地理信息系统技术在疟疾研究中的应用,中国寄生虫学与寄生虫病杂志,2004,22(2)119-121.
12、李奔福,李崇珍,张再兴,等.应用GIS技术分析云南省2004年疟疾流行状况.中国病原生物学杂志,2007(2)2:147-149.
13、张小萍,金小林,杨国静,等.江苏省疟疾流行态势的空间分析.中国人兽共患病杂志,2002(18)1:120-121.
14、苏永强,张治英,徐德忠等.运用空间局部内插研究海南省疟疾空间分布特征.中华流行病学杂志,2003(24)4:269-271.
15、Briggs DJ,Elliott P.The use of geographic information system in studies on environment and health[J].Wld Hlth Statist Quart,1995,48:85-94.
16、Indartna K,Hutubeddy R,Chupraphawan S,et al.Application of geographic information system to co-analysis of disease and economic research:dengue and malaria in Thailand[J].Southeast Asia J Trop Med Public Health,2000,29:669-676.
17、方立群,曹务春.3S技术在自然疫源性疾病中的应用及发展前景.中国人兽共患病杂志,2004,20(9):807-810.
18、方立群,曹务春,敦哲等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布[J].中华流行病学杂志.2003,24:1020-1023.
19、方立群,曹务春,陈化新等.应用地理信息系统分析中国肾综合征出血热的空间分布[J].中华流行病学杂志.2003,24:181-184.
20、盛慧锋,汤林华.疟疾暴发流行的监测与预测.国际医学寄生虫病杂志,2007,34(3):163-168.
21、Omumbo JA,Hay SI,Goetz SJ,Snow RW,Rogers DJ(2002).Updating historical maps of malaria transmission intensity in East Africa using remote sensing.Photogram metric Engineering and Remote Sensing 68(2):161-166.
22、Thomson MC,Graves P,Bamston T,Bell M,Ceccato P,del Corral J,Gianini A,Woldo Geogis T,Jaiteh M,Levy M,Lukang L,Connor S(2004).Towards a malaria early warning system for Eritrea.Interim Report,IRI.
23、WHO,RBM.Prevention and control of malaria epidemics,3rd meeting of the technical support network (2002).Available at:http://www.who.int/malaria/cmc_upload/0/000/015/827/3epidemics_report.pdf.
24、Beck LR,Rodriguez MH,Dister SW,Rodriguez AD,Rejmankva E,Ulloa A,Meza RA,Roberts DR,Paris JF,Spanner MA,Washino RK,Hacker C,Legters LJ(1994).Remote sensing as a landscape epidemiological tool to identify villages at high risk for malaria transmission.American Journal of Tropical Medicine and Hygiene 51(3):271-280.
25、Beck LR,Rodriguez MH,Dister SW,Rodriguez AD,Washino RK,Roberts DR,Spanner MA(1997).Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas,Mexico.American Journal Of Tropical Medicine And Hygiene 56(1):99-106.
26、G.Dennis Shanks,Simon I.Hay,Judy A.Omumbo.amlaria in Kenya's western highlands[J].Emerging infectious diseases,2005(11)9:1425-1432.
27、杨国静,周晓农,JB Malong,等.多因素空间复合模型预测我国疟疾流行区分布态势[J].中国寄生虫学与寄生虫病杂志,2002(20)3:145-147.
28、Thomas Kistemann,Friederike Dangendorf.New perspectives on the use of Geographical Information Systems(GIS) in environmental health Sciences.Int.J.Hyg.Environ.Health 205,169-181(2002).
29、Lawrence N.Kazembe.Spatial modeling and risk factors of malaria incidence in northern Malawi[J].Acta Tropica.2007(102):126-137.
30. Marlies H Craig, Brain L Sharp, Musawenkosi LH Mabaso. Developing a spatial-statistical model and map of hospital malaria prevalence in Botswana using a staged variable selection procedure[J]. International Journal of Health Geographics. 2007(6)44: 1-15.
31. Spatial statistical analysis, modeling and mapping of malaria in Africa. Available at http://pages.unibas.ch/diss/2001/DissB_5934.pdf
32. WHO, RBM. a framework for field research in Africa concepts indicators and partners (2001).
33. Lawrence N. Kazembe, Immo Kleinschmidt, Timothy H Holtz and Brian L Sharp. Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. International Journal of Health Geographics, 2006,5:41, Http://www.ij-healthgeographics.com/content/5/1/41.
1.IPCC.Climate Change 2007 synthesis report[OL],http://www.ipcc.ch/pdf/assessment-repo rt/ar4/syr/ar4_syr_spm.pdf
2.UNFCCC.联合国气候变化框架公约[OL].http://unfccc.int/resource/docs/convkp/convchin.pdf
3.UNFCCC.Climate change information kit[OL].http://www.unep.org/Themes/climatechang e/PDF/infokit2003-E.pdf
4.UNFCCC.Climate Change:Impacts,Vulnerabilities and Adaptation in Developing Count ries[OL],http://unfccc.int/files/essential_background/background_publications_htmlpdf/applicati on/txt/pub_07_impacts.pdf
5.Hunter PR.Climate change and waterbome and vector-borne disease[J].Journal of Ap plied Microbiology.2003,94 Supplement:37-46.
6.Hoshen MB,Morse AP.A weather-driven model of malaria transmission[J].MalaJ.2004,6(3):32-46.
7.Serandour J,Girel J,Boyer S,et al.How human practices have affected vector-borne d iseases in the past:a study of malaria transmission in Alpine valleys[J].Mala J.2007,9(6):115-125.
8.WHO.The Africa Malaria Report 2003[OL].http://www.rbm.who.int/amd2003/amr2003/p df/amr2003.pdf
9.Rita R.Colwell.global climate infectious disease:the cholera paradigm[J].science.1996,274(5295):2025-2031.
10.Lipp EK,Huq A,Colwell RR.Effects of global climate on infectious disease:the cho lera model[J].Clin Micro Rev.2002,15(4):757-770.
11.Koelle K,Rodo X,Pascual M,et al.Refractory periods and climate forcing in choler a dynamics[J].Nature.2005,436(7051):696-700.
12.WHO.Using climate to predict disease outbreaks:a review lOLl.http://www.who.int/e ntity/globalchange/publications/en/oeh0401.pdf
13.WHO.global strategic plan roll back malaria 2005-2015[OL].http://www.rbm.who.int/f orumV/docs/gsp_en.pdf
14.WHO.A framework for field research in Africa Malaria Early Warning Systems:conc epts,indicators and partners[OL],http://www.who.int/malaria/cmc_upload/0/000/014/807/mew s2.pdf
15.Grover-Kopec EK,Blumenthal MB,Ceccato P,et al.Web-based climate information re sources for malaria control in Africa[J].Mala J.2006,11(5):38-47.
16.Grover-Kopec E,Kawano M,Klaver RW,et al.An online operational rainfall-monitori ng resource for epidemic malaria early warning systems in Africa[J].Mala J.2005,21(4):6-11.
17.Teklehaimanot HD,Schwartz J,Teklehaimanot A,et al.Weather-based prediction of Pl asmodium falciparum malaria in epidemic-prone regions of Ethiopia Ⅱ.Weather-based predi ction systems perform comparably to early detection systems in identifying times for interv entions[J].Mala J.2004,19(3):44-54.
18.谈荣梅,陈坤,屠春丽.气象因素变化与霍乱发病的相关性研究[J].中国公共卫生.2003,19(4):416-417.
1、李立明主编.流行病学(第四版)[M].北京:人民卫生出版社,2000:311-313.
2、周水森,王漪,汤林华.2005年全国疟疾形势.中国寄生虫学与寄生虫病学杂志,2006,24(6):401-403.
3、中华人民共和国传染病防治法(2004年8月28日修订).中国法制出版社.
4、方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志,2005,26(11):839-842.
5、Kulldorff M.A spatial scan statistic.Communications in Statistics:Theory and Methods,1997,26:1481-1496.
6、EmmaKC,JamesIM.Spatial analysis of human granulocytic ehrilichiosis near Lyme,Connecticut.Emerging Infectious Disease,2002,8:943-948.
7、方立群,曹务春,敦哲,等.应用地理信息系统分析北京市海淀区肾综合征出血热的空间分布.中华流行病学杂志,2003,24(11):1020-1023.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |