| OBJECTIVE ①To establish multifactor evaluation model of malaria epidemic situation and vector density; ②To establish malaria and vector evaluation geographic information system of Yunnan Province; ③To study the application of remote sensing vegetation NDVI index on malaria epidemic situation and vector evaluation and forecast; ④To explore malaria and vector risk distribution map. METHOD ①There are 33 townships of 14 counties in Yunnan Province have been chosen as study fields. Successive surveillance data of malaria, vector, malaria prevention and control, climate, environmental, population and remote sensing vegetation surveillence data have been collected from 1984-1993. Monthly average manpower hourly density data of An. minimus and An. sinensis has been collected from 27 townships with An. minimus being the main transmission vector, and entomological inoculation rate of An. anthropophagus and An. sinensis have been collected in 6 townships with An. anthropophagus being the main transmission vector. Malaria epidemic situation data include incidence rate and mortality rate of malaria. Monthly average temperature, temperaturemax, temperaturemin, rainfall, sunlight amount, longitude, latitude, elevation, paddy field percentage, population density, agricultural population percentage, percentage of indoor residual spraying and tent using has been collected also. Digital map of Yunnan Province (1:1000000) has been extracted from Minimum Medical Database Spatial Decision Support System depending on ArcView3.0a and Erdas 8.6 software. Remote sensing NDVI have been extracted from NOAA/AVHRR pathfinder products, which has been downloaded from http://eosdata.gsfc.nasa.gov. The resolving power of NDVI value is 8km×8km. NDVI=(Ch2-Ch1)/ (Ch2+Ch1). ②Principle component analysis and factor analysis have been used to study the relationship of climate, environmental and remote sensing NDVI indexes with vector density to choose the principle evaluation indexes of vector density; ③Analytical hierarchy process (AHP) has been used to establish An. minimus analytical hierarchy evaluation model; ④Data of 15 townships in 1984―1993 has been chosen as the model establishing data. There are 18 indexes of climate, environmental, remote sensing NDVI etc., which have been chosen as the initial evaluation indexes of vector denxity. Vector summation density has been chosen as the main factor, and the grey correlation analysis has been done to choose principle evaluation indexes depending on certain grey threshold. Weights of indexes have been given depending on grey correlation order and E has been formed based on addition method. The relationship of E and vector density has been studied to establish vector density fitness evaluation model; ⑤Vector summation density has been chosen as the first factor and An. minimus density has been chosen as the second factor. The average grey correlation degree and the grey correlation order have been caculated. 10 n has been given as the weight to index with minimum grey correlation degree( n=0), and which has been used as common difference of index weight. The weight of maximum grey correlation degree index has been used as the basic value, and 2 has been used as the common ratio to give weights to indexes to form vector synthesis evaluation model. ⑥Malaria incidence rate has been used as the main factor, and average grey correlation degree of An. minimus density and summation vector density has been calculated. Vector summation density, An. minimus density, population density and agriculture population percentage have been chosen as the evaluation indexes of malaria epidemic situation. Depending on former method malaria epidemic situation synthesis evaluation model has been established; ⑦The vector density of another 12 townships has been fitted depending on vector density fitness evaluation model. The correlation analysis has been done to compare vector density forecast value and actual vector density standard value. The vector density of Jinghong, Mengla, Menglian, Simao, Yuanjiang, Zhengyuan counties in 1994, 1999, 2000 have been evaluated and malaria incidence rate has been forecasted. The goodness of fit test has been done to test thefitness of evaluation model. ⑧Based on ArcView3.0a and Erdas8.6 software Visual Basic and MapObjects have been used as developing tools to establish malaria epidemic situation and vector evaluation geographic information system of Yunnan Province. RESULT ①Remote sensing NDVI has marked correlativity with An. minimus density and summation vector density in 4 townships of 27 counties, and in the other 23 townships the relativity is not marked. The coalition analysis result of Mangguoshu and Mengpeng townships of Mengla shows that the correlation coefficient of NDVI is: r= 0.6190, P<0.05; ②Principle component analysis result shows that cumulative contribution rate of Z1, Z2, Z3 is above 80%, therefore the first three principle components have been chosen. Principle component Z1 have bigger load of NDVI, dry season NDVI and wet season NDVI. Quartimax rotation of factor analysis result shows that common factor 1 has more load of sunlight amount and remote sensing NDVI; common factor 2 has more load of monthly average temperature, temperaturemax, temperaturemin; common factor 3 has more load of rainfall. ③An. minimus analytical hierarchy model shows that cumulative weight of climate indexes is 87.5%, and the analysis result shows that there are 10 townships An. minimus density index is above 0.6, and those counties are belonged to Yuanjiang, Jinghong, Mengla and Malipo counties. ④Vector summation density fitness evaluation model is as following. The testing result shows that: e '0.5= 21%, average relative error=19%. The correlation coefficient is r =0.810, P<0.05. Y=0.2146X10′+0.1878X12′+0.1610X4′+0.1342X11′+0.1073X9′+0.0805X15′+ 0.0537X7′+0.0268X8′⑤An. minimus density fitness evaluation model is as following: Y =0.0578 e 0 .0780(8X10 '+7X11' +6X12' +5X15'+4X9 ' +3X4'+2X8'+1X7') ⑥Vector density synthesis evaluation model is as following: R1=0.2353L′+0.2353L1′+0.1176X12′+0.1029 X10′+0.0882 X9′+0.0735 X4′+ 0.0588 X11′+0.0441 X15′+0.0294 X8′+0.0147 X7′⑦Malaria epidemic situation synthesis evaluation model is as following: R2=0.2857Y′+0.1429X10′+0.1270L′+0.1111X9′+0.0952X12′+0.0794X8′+0.0635X15′+0.0476X11′+0.0317 X7′+0.0159L1′⑧An. Anthropophagus entomological inoculation rate synthesis evaluation model is as following: R3=0.0192X15′+0.0385X1′+0.0577X14′+0.0769X4′+0.0962X13′+0.1154X17′+ 0.1346X′+0.1538T′+0.3077 T ' ' ⑨The vector density of Jinghong, Mengla, Menglian, Simao, Yuanjiang, Zhengyuan counties in 1994, 1999, 2000 have been evalauated, and malaria epidemic situation has been forecasted. The goodness test for the simulation has been done: χ2=2.30, P>0.995. Therefore malaria and vector density synthesis evaluation model have good goodness. Malaria epidemic situation has increased in Jinghong and Mnegla, but decreased in Yuanjiang county. ⑩Malaria epidemic situation and vector evaluation geographic information system of Yunnan Province has been established. There are seven databases of this system includes malaria epidemic, vector, climate, environmental, remote sensing, population and malaria prevention databases. Document management, database establishment, map creation, data inquiry functions can be realized in this system, and malaria and vector evaluation and forecast functions can be finished also. CONCLUSION ①Depending on geographic information system and remote sensing and techniques, the malaria epidemic situation and vector evaluation model system and geographic information system can be developed. ②Depending on vector summation density fitness evaluation model, vector density can be simulated by climate, environmental and remote sensing surveillance indexes; Depending on vector density and malria epidemic situation synthesis evaluation model, malaria transmission trend and vector density can be evaluated and forecasted. ③Malaria epidemic situation and vector evaluation geographic information system of Yunnan Province can provide scientific supports for malaria control prevention and early warning system of Yunnan Province. ④Remote sensing NDVI is one of good evaluation indexes of malaria epidemic situation and vector density.
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