The Use of Remote Sensing for Estimating the Risk of Transmission and Predicting Cases of Malaria in Argentina

The early warning systems based on statistical prediction models; indicators of environmental risks and remote sensing constitute an essential source of environmental information for the development of these warning systems. The present work is focused on the use of remote sensing for the estimation of the risk of transmission and the prediction of malaria cases in the northwest of Argentina. The study was carried out in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The existent relationship between reported malaria cases and climatic/environmental variables (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST)), obtained from Landsat 5 and 7 satellite images, was analyzed through multilevel Poisson regression analyses. A higher abundance of reported cases of malaria in summer was observed. A model of ARIMA (Autoregressive Integrated Moving Average) temporal series, which included the environmental variables, was generated to forecast malaria cases in the year 2000. In turn, the relationship between malaria cases and environmental/climatic factors showed that malaria cases were associated with an increase in LST and mean temperature and a decrease in NDVI. Early warning systems provide information about spatial and temporal predictions of epidemics might help control and prevent malaria outbreaks. Based on the results, this study is expected to be used for the development of future prevention and control actions by the health officials.