AI Article Synopsis

  • The study aimed to analyze the spatial distribution of malaria epidemics in Hainan province and examine how environmental factors affect these outbreaks while also creating a predictive model for malaria incidences.
  • Data on malaria rates and environmental factors were gathered across 19 counties in Hainan from May to October 2000, and various correlations were calculated using statistical software.
  • Results indicated that higher malaria incidence correlated positively with elevation and land covered by forests and grasslands, but negatively with urban areas and land surface temperatures; a predictive model was developed using negative binomial regression analysis.

Article Abstract

Objective: To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics.

Methods: Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0.

Results: The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST).

Conclusion: Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.

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