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MODELING DISEASE VECTOR OCCURRENCE WHEN DETECTION IS IMPERFECT: INFESTATION OF AMAZONIAN PALM TREES BY TRIATOMINE BUGS AT THREE SPATIAL SCALES
Trypanosoma Cruzi (Kinetoplastida Trypanosomatidae)
Parasitic Protozoan
Disease Vector Occurrence
Attalea Palm Tree Infestation
Rhodnius spp. (Triatominae)
Autor
Afiliación
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil.
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil.
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil.
Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Pontificia Universidad Católica del Ecuador. Centro de Investigación en Enfermedades Infecciosas. Quito, Ecuador.
Fundación Internacional de Ciencias Sociales y Salud. Instituto Juan César García. Quito, Ecuador
London School of Hygiene & Tropical Medicine. Department of Infectious and Tropical Diseases. London, UK.
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil.
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil.
Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Pontificia Universidad Católica del Ecuador. Centro de Investigación en Enfermedades Infecciosas. Quito, Ecuador.
Fundación Internacional de Ciencias Sociales y Salud. Instituto Juan César García. Quito, Ecuador
London School of Hygiene & Tropical Medicine. Department of Infectious and Tropical Diseases. London, UK.
Resumen en ingles
Background: Failure to detect a disease agent or vector where it actually occurs constitutes a serious drawback in
epidemiology. In the pervasive situation where no sampling technique is perfect, the explicit analytical treatment of
detection failure becomes a key step in the estimation of epidemiological parameters. We illustrate this approach with a
study of Attalea palm tree infestation by Rhodnius spp. (Triatominae), the most important vectors of Chagas disease (CD) in
northern South America.
Methodology/Principal Findings: The probability of detecting triatomines in infested palms is estimated by repeatedly
sampling each palm. This knowledge is used to derive an unbiased estimate of the biologically relevant probability of palm
infestation. We combine maximum-likelihood analysis and information-theoretic model selection to test the relationships
between environmental covariates and infestation of 298 Amazonian palm trees over three spatial scales: region within
Amazonia, landscape, and individual palm. Palm infestation estimates are high (40–60%) across regions, and well above the
observed infestation rate (24%). Detection probability is higher (,0.55 on average) in the richest-soil region than elsewhere
(,0.08). Infestation estimates are similar in forest and rural areas, but lower in urban landscapes. Finally, individual palm
covariates (accumulated organic matter and stem height) explain most of infestation rate variation.
Conclusions/Significance: Individual palm attributes appear as key drivers of infestation, suggesting that CD surveillance
must incorporate local-scale knowledge and that peridomestic palm tree management might help lower transmission risk.
Vector populations are probably denser in rich-soil sub-regions, where CD prevalence tends to be higher; this suggests a
target for research on broad-scale risk mapping. Landscape-scale effects indicate that palm triatomine populations can
endure deforestation in rural areas, but become rarer in heavily disturbed urban settings. Our methodological approach has
wide application in infectious disease research; by improving eco-epidemiological parameter estimation, it can also
significantly strengthen vector surveillance-control strategies.
Palabras clave en ingles
Chagas DiseaseTrypanosoma Cruzi (Kinetoplastida Trypanosomatidae)
Parasitic Protozoan
Disease Vector Occurrence
Attalea Palm Tree Infestation
Rhodnius spp. (Triatominae)
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