Evidence map›Paper›PMID 35441255›Full record

ArticleEcoHealth2022

Ovipositional Reproduction of the Dengue Vector for Identifying High-Risk Urban Areas.

Mariana de Oliveira Lage, Gerson Barbosa, Valmir Andrade, Henrique Gomes, Francisco Chiaravalloti, José Alberto Quintanilha

Abstract read
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In one paragraph

Article in EcoHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mariana de Oliveira LageUniversidade de São Paulo - USP, PROCAM USP - Programa de Pós-Graduação em Ciências Ambientais, Av. Prof. Luciano Gualberto, 1289, Cidade Universitária, Butantã, São Paulo, SP, CEP: 05508-090, Brazil. mariana_lage@usp.br.
Gerson BarbosaSuperintendência de Controle de Endemias - SUCEN, R. Paula Sousa, Centro, São Paulo, SP, 166 - CEP: 01027-000 Centro, Brazil.
Valmir AndradeSuperintendência de Controle de Endemias - SUCEN, R. Paula Sousa, Centro, São Paulo, SP, 166 - CEP: 01027-000 Centro, Brazil.
Henrique GomesSuperintendência de Controle de Endemias - SUCEN, R. Paula Sousa, Centro, São Paulo, SP, 166 - CEP: 01027-000 Centro, Brazil.
Francisco ChiaravallotiUniversidade de São Paulo - USP, FSP USP - Programa de Pós-Graduação em Saúde Pública, Av. Dr. Arnaldo, 715. CEP: 03178-200 Cerqueira César, São Paulo, SP, Brazil.
José Alberto QuintanilhaInstitute of Energy and Environment - IEEUSP, Universidade de São Paulo - USP, Av. Prof. Luciano Gualberto, 1289, Cidade Universitária, Butantã, São Paulo, SP, CEP: 05508-090, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification and classification of high-risk areas for the presence of Aedes aegypti is not an easy task. To develop suitable methods to identify this areas is an essential task that will increase the efficiency and effectiveness of control measures and to optimize the use of resources. The objectives of this study were to identify high- risk areas for the presence of Ae. aegypti using mosquito traps and household visits to identify breeding sites; to identify and validate aspects of the remote sensing images that could characterize these areas; to evaluate the relationship between this spatial risk classification and the occurrence of Ae. aegypti; and provide a methodology to the health and control vector services and prioritize these areas for development of control measure. Information about the geographical coordinates of these traps will enable us to apply the kriging spatial analysis tool to generate maps with the predicted numbers of Ae. aegypti. Satellite images were used to identify the characteristic features the four areas, so that other areas could also be classified using only the sensing remote images. The developed methodology enables the identification of high-risk areas for Ae. aegypti and for the occurrence of Dengue, as well as Zika fever and Chikungunya fever using only sensing remote images. These results allow health and vector control services to prioritize these areas for developing surveillance and control measures. The use of the available resources can be optimized and potentially promote a decrease in the expected incidences of these diseases, particularly Dengue.

Indexed as

AedesDengueZika VirusZika Virus InfectionAnimalsMosquito VectorsReproductionAedes aegyptiOrdinary KrigingRemote SensingTraps

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.