Analysis of Chagas disease vectors occurrence data: the Argentinean triatomine species database.

BACKGROUND

Chagas disease is a neglected tropical disease and (its etiological agent) is mainly transmitted by triatomines (Hemiptera: Reduviidae). All triatomine species are considered as potential vectors; thus, their geographic distribution and habitat information should be a fundamental guide for the surveillance and control of Chagas disease. Currently, of the 137 species distributed in the Americas (Justi and Galvão 2017), 17 species are cited for Argentina: , , , , , , , , , , , , , , , and . Almost 20 years have passed since the publication of the "Atlas of the Triatominae" by Carcavallo et al. (1998) and no work has been done to provide an updated complete integration and analysis of the existing information for Argentinean triatomine species. Here we provide a detailed temporal, spatial and ecological analysis of updated occurrence data for triatomines present in Argentina.

NEW INFORMATION

This is the first database of the 17 triatomine species present in Argentina (15917 records), with a critical analysis of the temporal, spatial and ecological characteristics of 9788 records. The information spans the last 100 years (1918-2019) and it was mostly obtained from the DataTri database and from the Argentinean Vector Reference Center. As 70% of the occurrences corresponded to the last 20 years, the information was split into two broad periods (pre-2000 and post-2000). Occurrence data for most species show distribution range contractions, which, from the pre-2000 to post-2000 period, became restricted mainly to the dry and humid Chaco ecoregions. Concurrently, the highest species richness foci occurred within those ecoregions. The species , , and mostly colonise human dwelling habitats. This study provides the most comprehensive picture available for Argentinean triatomine species and we hope that any knowledge gaps will encourage others to keep this information updated to assist health policy-makers to make decisions based on the best evidence.