关键词: 遗传规划, 疟疾, 遥感数据, 空间分析, 预测

Abstract:

This paper delineates the relationship between remote sensing monitoring indexes and malaria incidences using genetic programming (GP) method based on factors derived from remote sensing data. Thus, the spatial distribution of malaria incidence is predicted, the prediction results are analyzed, and the modeling precision is evaluated. Malaria is considered to be the severest parasite disease and Anhui Province is one of the typical mid-latitude areas coping with high malaria risk. This paper studies the issue of predicting malaria spatial distribution using GP method, as GP is a striking optimization method which has the capability of exploring a proper solution for sophisticated issues through evolutionary algorithms. And this process is further explained with an example adopting the monthly average malaria incidences in each county of Anhui Province from 2004 to 2010. Also, remote sensing data is regarded to be the main source of factors, considering its large spatial scale and fast data acquisition, and that various meteorological and environmental indexes, could be converted from remote sensing data. These factors include remote sensing indexes, such as normalized difference vegetation index (NDVI) and land surface temperature (LST), plus natural attribute (elevation) and social attributes (population, immigrant and GDP data) in the county level. Results demonstrate that NDVI and LST have influences of two months’ and one month’s lag respectively. Compared with the result of linear regression (R² = 0.470 for training data and R² = 0.408 for test data), the predicting precision is improved using GP method (R² = 0.558 for training data and R² = 0.429 for test data), which is benefited from illustrating the non-linear relation between remote sensing indexes and malaria incidences. GP method contributes to increase the precision of predicting the spatial distribution of malaria incidence. Conclusively, this paper provides a basis for future scientific research on predicting spatial distribution and mapping malaria using remote sensing data.

Key words: genetic programming, malaria, remote sensing data, spatial analysis, prediction