Study on the prediction of malaria incidence in the northern Anhui Province based on remote sensing techniques and time series analysis

Objective To explore the relationship between malaria incidence and land surface temperature (LST) and normalized difference vegetation index (NDVI), assess the adjusted effect on autoregressive integrated moving average model (ARIMA) prediction using LST and NDVI. Methods Five counties in northern Anhui province were selected in this study. We collected the reported malaria epidemic data, LST and NDVI remote sensing images from 2004 to 2011. Then data extraction and synthesis from MODIS images were performed. SPSS 17.0 software was used for statistical analysis. Results The incidence of malaria in 2010 predicted by ARIMA models based on malaria data from 2004 to 2009 was higher than the reported incidence with an average error of 0.721/100 000. The results of multiple regression analysis showed a significant association (P<0.001) between malaria incidence and the nearly three-month average LST (lst_{_012}, β=0.295) and the average NDVI of last month and before the last month (ndvi_{_12}, β=0.280). After adjusting predictive results of ARIMA by Lst_{_012} and ndvi_{_12} (relative ratio was 2：1), the average error decreased to 0.018/100 000. The correction effect of lst_{_012} and ndvi_{_12} on the predicted malaria incidence by ARIMA model based on malaria data from 2004 to 2010 was evaluated again based on reported malaria incidence in 2011. The results indicated that the prediction error (＜0.001/100 000) after adjustment was significantly lower than that before correction (0.293/100 000). Conclusions ARIMA model could be applied to the incidence of malaria fitting and prediction. The predicted results would be better when the predicted results were adjusted by environmental remote sensing alternate index.