Evidence map›Paper›PMID 35328909›Full record

ArticleInternational journal of environmental research and public health2022

Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito

Damiana Ravasi, Francesca Mangili, David Huber, Laura Azzimonti, Lukas Engeler, Nicola Vermes, Giacomo Del Rio, Valeria Guidi, Mauro Tonolla, Eleonora Flacio

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

10 authors.

Damiana RavasiDepartment for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.ORCID 0000-0003-2480-484X
Francesca MangiliDepartment of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.ORCID 0000-0002-3215-1028
David HuberDepartment of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.ORCID 0000-0002-2892-7348
Laura AzzimontiDepartment of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.
Lukas EngelerDepartment for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.
Nicola VermesDepartment of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.
Giacomo Del RioDepartment of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence Studies (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6962 Lugano-Viganello, Switzerland.
Valeria GuidiDepartment for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.
Mauro TonollaDepartment for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.
Eleonora FlacioDepartment for Environment Constructions and Design, Institute of Microbiology (IM), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6850 Mendrisio, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn Switzerland,

methodsA regularized logistic regression was used to link the long-term dataset of

resultsThe model performed well, with an AUC of 0.86. Ten socio-environmental predictors were selected as informative, including the road-based distance in minutes of travel by car from the nearest cell established in the previous year. The risk maps showed high suitability for

conclusionsThe areas identified as suitable for

Indexed as

AedesAnimalsCitiesEnvironmentMosquito ControlMosquito VectorsSwitzerlandAedes albopictusecological niche modelenvironmental factorsovitrapregularized logistic regressionsurveillance

Identifiers

PMID35328909
PMCPMC8955472

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.