Introduction:Malaria is a mosquito-borne infectious disease that poses a serious threat to human health.Although Anhui Province achieved malaria elimination in 2019,the risk of retransmission from imported cases persi...Introduction:Malaria is a mosquito-borne infectious disease that poses a serious threat to human health.Although Anhui Province achieved malaria elimination in 2019,the risk of retransmission from imported cases persists due to cross-border human mobility.Given the strong correlation between meteorological and environmental factors and malaria transmission,this study selected four distinct geographic regions in Anhui Province to investigate the relationship between these factors and malaria vector abundance using remote sensing technology.Methods:We collected density data of Anopheles sinensis(An.sinensis),meteorological parameters(temperature,humidity,rainfall),and normalized difference vegetation index(NDVI)from 18 surveillance sites in Anhui Province from 2019 to 2023.The data underwent preprocessing through multi-band composition,image mosaicking,and surface reflectance calibration to construct a spatiotemporal database.A generalized additive model(GAM)was developed using data from 2019 to 2022 and subsequently validated by predicting mosquito vector density in 2023.Results:Univariate GAM analysis revealed that nonlinear models provided a better fit than linear models based on Akaike Information Criterion(AIC)values.Temperature,lagged temperature(temperature_1),humidity,lagged humidity(humidity_1),rainfall,lagged rainfall(rainfall_1),NDVI,and lagged NDVI(NDVI_1)all demonstrated significant nonlinear relationships with An.sinensis density(P<0.05).Specifically,NDVI(0.34–0.81),temperature(10.55℃–30.68℃),humidity(46.82%–97.61%),and rainfall(9.67 mm–440.52 mm)showed significant positive correlations with An.sinensis density.The optimal multivariate GAM incorporated lagged variables:humidity_1,NDVI_1,rainfall_1,and temperature_1.This model achieved an R²value of 0.76 on the test set,with a mean squared error(MSE)of 0.19 and a mean absolute error(MAE)of 0.28.Conclusions:NDVI,temperature,humidity,and rainfall constitute the key environmental drivers influencing temporal patterns of An.sinensis density in Anhui Province.The GAM-based prediction model provides quantitative decision support for dynamic mosquito vector monitoring and resource allocation for malaria control.展开更多
基金supported by the Anhui Provincial Health Commission Research Project(AHWJ2024Aa20278).
文摘Introduction:Malaria is a mosquito-borne infectious disease that poses a serious threat to human health.Although Anhui Province achieved malaria elimination in 2019,the risk of retransmission from imported cases persists due to cross-border human mobility.Given the strong correlation between meteorological and environmental factors and malaria transmission,this study selected four distinct geographic regions in Anhui Province to investigate the relationship between these factors and malaria vector abundance using remote sensing technology.Methods:We collected density data of Anopheles sinensis(An.sinensis),meteorological parameters(temperature,humidity,rainfall),and normalized difference vegetation index(NDVI)from 18 surveillance sites in Anhui Province from 2019 to 2023.The data underwent preprocessing through multi-band composition,image mosaicking,and surface reflectance calibration to construct a spatiotemporal database.A generalized additive model(GAM)was developed using data from 2019 to 2022 and subsequently validated by predicting mosquito vector density in 2023.Results:Univariate GAM analysis revealed that nonlinear models provided a better fit than linear models based on Akaike Information Criterion(AIC)values.Temperature,lagged temperature(temperature_1),humidity,lagged humidity(humidity_1),rainfall,lagged rainfall(rainfall_1),NDVI,and lagged NDVI(NDVI_1)all demonstrated significant nonlinear relationships with An.sinensis density(P<0.05).Specifically,NDVI(0.34–0.81),temperature(10.55℃–30.68℃),humidity(46.82%–97.61%),and rainfall(9.67 mm–440.52 mm)showed significant positive correlations with An.sinensis density.The optimal multivariate GAM incorporated lagged variables:humidity_1,NDVI_1,rainfall_1,and temperature_1.This model achieved an R²value of 0.76 on the test set,with a mean squared error(MSE)of 0.19 and a mean absolute error(MAE)of 0.28.Conclusions:NDVI,temperature,humidity,and rainfall constitute the key environmental drivers influencing temporal patterns of An.sinensis density in Anhui Province.The GAM-based prediction model provides quantitative decision support for dynamic mosquito vector monitoring and resource allocation for malaria control.
