Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration

Time-series of coarse-resolution greenness values derived through remote sensing have been used as a surrogate environmental variable to help monitor and predict occurrences of a number of vector-borne and zoonotic diseases, including malaria. Often, relationships between a remotely-sensed index of greenness, e.g. the normalized difference vegetation index (NDVI), and disease occurrence are established using temporal correlation analysis. However, the strength of these correlations can vary depending on type and change of land cover during the period of record as well as inter-annual variations in the climate drivers (precipitation, temperature) that control the NDVI values. In this paper, the correlation between a long (260 months) time-series of monthly disease case rates and NDVI values derived from the Global Inventory Modeling and Mapping Studies (GIMMS) data set were analysed for two departments (administrative units) located in the Atlantic Forest biome of eastern Paraguay. Each of these departments has undergone extensive deforestation during the period of record and our analysis considers the effect on correlation of active versus quiescent periods of case occurrence against a background of changing land cover. Our results show that time-series data, smoothed using the Fourier Transform tool, showed the best correlation. A moving window analysis suggests that four years is the optimum time frame for correlating these values, and the strength of correlation depends on whether it is an active or a quiescent period. Finally, a spatial analysis of our data shows that areas where land cover has changed, particularly from forest to non-forest, are well correlated with malaria case rates

Spatio-Temporal Analysis of Malaria in Paraguay

Malaria is a mosquito-borne disease that has afflicted humans for thousands of years. Today it is considered a re-emerging disease. Malaria is most prevalent in tropical and subtropical parts of the world. The disease has been linked to several environmental parameters, including precipitation, temperature, and deforestation. However, these relationships have mainly been studied in Africa and have not been explored in other parts of the world. The study area for this thesis was the South American country of Paraguay. Paraguay has experienced an oscillation in malaria cases over the past 20 years, with monthly cases ranging from 0 to 1200. Additionally, the country has experienced vast amounts of deforestation and climate variations. The thesis study area was two Paraguayan departments, Alto Parana and Canindeyú. Both departments had a record of monthly malaria cases for the years of 1981-2003. It was discovered that there was a positive correlation between malaria and temperature and vegetation strength and a negative correlation between precipitation and malaria. Spatial comparisons of deforestation maps and maps of malaria risk based on the selected environmental parameters, suggests recent deforestation increases the probably of malaria occurrence. Additionally, time series analysis provides evidence that an increase in temperature increases malaria cases every 2-3 years. The annual oscillation of temperature, precipitation, and vegetation change from the wet and dry seasons corresponds with the low and high activity time periods for malaria case rates. Adviser: Professor James Merchan