Evidence map›Paper›PMID 41179806›Full record

ArticleGigaByte (Hong Kong, China)2025

Collection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde.

Lara Ferrero Gómez, Keily Lucienne Fonseca Silva, Bruno Dos Santos Pina, Patrick Silva, Ulisses António Lima da Cruz, José Moniz Lopes Fernandes, Hélio Daniel Ribeiro Rocha

Abstract read
In one paragraph

Article in GigaByte (Hong Kong, China), 2025. 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

7 authors.

Lara Ferrero GómezGrupo de Investigação em Doenças Tropicais (GIDTPiaget), Unidade das Ciências da Vida, da Natureza e do Ambiente (UNCVA), Universidade Jean Piaget de Cabo Verde, Praia, Cabo Verde, Palmarejo Grande, Praia, 7600, Cabo Verde.ORCID https://orcid.org/0000-0001-8166-3663
Keily Lucienne Fonseca SilvaGrupo de Investigação em Doenças Tropicais (GIDTPiaget), Unidade das Ciências da Vida, da Natureza e do Ambiente (UNCVA), Universidade Jean Piaget de Cabo Verde, Praia, Cabo Verde, Palmarejo Grande, Praia, 7600, Cabo Verde.ORCID https://orcid.org/0000-0002-1641-6911
Bruno Dos Santos PinaInstituto de Higiene e Medicina Tropical (IHMT), Universidade NOVA de Lisboa, Rua da Junqueira, 100, 1349-008 Lisboa, Portugal.ORCID https://orcid.org/0009-0007-8434-5856
Patrick SilvaFacultade de Ciência e Tecnologia (FCT), Universidade de Cabo Verde (UniCV), Campus do Palmarejo Grande, Praia, 7943-010, Cabo Verde.ORCID https://orcid.org/0000-0002-0578-1884
Ulisses António Lima da CruzInstituto Nacional de Estadística (INE), Rua da Caixa Económica n° 18, Fazenda, Praia, 7600, Cabo Verde.ORCID https://orcid.org/0009-0000-2655-1829
José Moniz Lopes FernandesFacultade de Ciência e Tecnologia (FCT), Universidade de Cabo Verde (UniCV), Campus do Palmarejo Grande, Praia, 7943-010, Cabo Verde.ORCID https://orcid.org/0000-0002-9143-3041
Hélio Daniel Ribeiro RochaGrupo de Investigação em Doenças Tropicais (GIDTPiaget), Unidade das Ciências da Vida, da Natureza e do Ambiente (UNCVA), Universidade Jean Piaget de Cabo Verde, Praia, Cabo Verde, Palmarejo Grande, Praia, 7600, Cabo Verde.ORCID https://orcid.org/0000-0002-4919-4956

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vector-borne diseases, primarily those transmitted by mosquitoes, are a serious public health problem. Some, such as dengue, put half of the world's population at risk. Combating these diseases requires multifaceted strategies, with vector surveillance and control playing key roles. Robust and predictive surveillance systems for vector-borne diseases, based on risk stratification, enable the implementation of appropriate interventions across time and space. Here, we present a collection of entomological, demographic, water and sanitation, and climatic data from Praia (Cabo Verde), a hotspot for mosquito-borne diseases. These data were collected from June to November 2022, at 40 sentinel points scattered across the urban area of Praia. They constitute a valuable source of information for developing predictive scenarios of arbovirus outbreak risk using statistical models applied to spatial and non-spatial indicators. These data demonstrate the utility of GBIF in transforming large volumes of occurrence data into valuable information for arbovirus surveillance and vector control.

Identifiers

PMID41179806
PMCPMC12571994

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Registered trials

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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.