Evidence map›Paper›PMID 34624026›Full record

ArticlePloS one2021

Ensemble ecological niche modeling of West Nile virus probability in Florida.

Sean P Beeman, Andrea M Morrison, Thomas R Unnasch, Robert S Unnasch

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

4 authors.

Sean P BeemanCenter for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America.ORCID 0000-0002-9965-8923
Andrea M MorrisonBureau of Epidemiology, Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, Florida, United States of America.
Thomas R UnnaschCenter for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America.ORCID 0000-0001-5099-7820
Robert S UnnaschCenter for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America.

Funding

ACL HHS U01CK000510NCEZID CDC HHS U01 CK000510
6 · The paper itself

Abstract

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models-boosted regression tree, random forest, and maximum entropy-developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422-0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988-1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800-0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

Indexed as

AnimalsClimateCulicidaeEcosystemFloridaHumansMachine LearningProbabilityRisk FactorsSentinel SurveillanceWest Nile FeverWest Nile virusWetlands

Identifiers

PMID34624026
PMCPMC8500454

What OpenQuestion holds

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LicenceCC BY
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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.