Modeling disease vector occurrence when detection is imperfect: Infestation of Amazonian palm trees by triatomine bugs at three spatial scales

dc.contributor.authorAbad-Franch, Fernando
dc.contributor.authorFerraz, Gonçalo
dc.contributor.authorCampos, Ciro
dc.contributor.authorPalomeque, Francisco S.
dc.contributor.authorGrijalva, Mario J.
dc.contributor.authorAguilar, Hugo Marcelo V.
dc.contributor.authorMiles, Michael Alexander
dc.date.accessioned2020-06-02T20:08:33Z
dc.date.available2020-06-02T20:08:33Z
dc.date.issued2010
dc.description.abstractBackground: 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. Copyright: © 2010 Abad-Franch et al.en
dc.identifier.doi10.1371/journal.pntd.0000620
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/16267
dc.language.isoenpt_BR
dc.publisher.journalPLoS Neglected Tropical Diseasespt_BR
dc.relation.ispartofVolume 4, Número 3pt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectAttaleaen
dc.subjectChagas Diseaseen
dc.subjectField Studyen
dc.subjectGeographic Distributionen
dc.subjectInfection Risken
dc.subjectMaximum Likelihood Methoden
dc.subjectNonhumanen
dc.subjectParasite Vectoren
dc.subjectRhodniusen
dc.subjectTheoretical Modelen
dc.subjectTreeen
dc.subjectAnimalsen
dc.subjectArecaceaeen
dc.subjectDisease Carrieren
dc.subjectEctoparasitosisen
dc.subjectGrowth, Development And Agingen
dc.subjectHumanen
dc.subjectParasitologyen
dc.subjectPlant Diseaseen
dc.subjectSouth Americaen
dc.subjectStatistical Modelen
dc.subjectAnimalen
dc.subjectArecaceaeen
dc.subjectDisease Vectorsen
dc.subjectEctoparasitic Infestationsen
dc.subjectHumansen
dc.subjectModels, Statisticalen
dc.subjectPlant Diseasesen
dc.subjectRhodniusen
dc.subjectSouth Americaen
dc.titleModeling disease vector occurrence when detection is imperfect: Infestation of Amazonian palm trees by triatomine bugs at three spatial scalesen
dc.typeArtigopt_BR

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