Abstract. 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.
BACKGROUND Since the early 1990s, programs to control Chagas disease in South America have focused on eradicating domiciliary Triatoma infestans, the main vector. Seroprevalence studies of the chagasic infection are included as part of the vector control programs; they are essential to assess the impact of vector control measures and to monitor the prevention of vector transmission.OBJECTIVE To assess the interruption of domiciliary vector transmission of Chagas disease by T. infestans in Paraguay by evaluating the current state of transmission in rural areas.METHODS A survey of seroprevalence of Chagas disease was carried out in a representative sample group of Paraguayans aged one to five years living in rural areas of Paraguay in 2008. Blood samples collected on filter paper from 12,776 children were tested using an enzyme-linked immunosorbent assay. Children whose serology was positive or undetermined (n = 41) were recalled to donate a whole blood sample for retesting. Their homes were inspected for current triatomine infestation. Blood samples from their respective mothers were also collected and tested to check possible transmission of the disease by a congenital route.FINDINGS A seroprevalence rate of 0.24% for Trypanosoma cruzi infection was detected in children under five years of age among the country’s rural population. Our findings indicate that T. cruzi was transmitted to these children vertically. The total number of infected children, aged one to five years living in these departments, was estimated at 1,691 cases with an annual incidence of congenital transmission of 338 cases per year.MAIN CONCLUSION We determined the impact of vector control in the transmission of T. cruzi, following uninterrupted vector control measures employed since 1999 in contiguous T. infestans-endemic areas of Paraguay, and this allowed us to estimate the degree of risk of congenital transmission in the country.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.