Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005)

TL;DR

This study uses satellite data to predict malaria cases in Argentina by analyzing environmental factors like temperature and vegetation.

Contribution

The novel contribution is the use of remote sensing data with statistical models to estimate malaria transmission risk in a specific region.

Findings

Malaria cases increased in summer and were linked to higher land surface temperature and mean temperature.

A decrease in the normalized difference vegetation index (NDVI) was associated with increased malaria cases.

An ARIMA model incorporating environmental variables successfully forecasted malaria cases in the year 2000.

Abstract

Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the 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 using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials.

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