Evidence map›Paper›PMID 40838020›Full record

ArticlePNAS nexus2025

Using mosquito and arbovirus data to computationally predict West Nile virus in unsampled areas of the Northeast United States.

Joseph R McMillan, James Sun, Luis Fernando Chaves, Philip M Armstrong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 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

4 authors.

Joseph R McMillanDepartment of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, USA.ORCID https://orcid.org/0000-0002-6909-950X
James SunClark Scholars Program, Department of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, USA.
Luis Fernando ChavesDepartment of Environmental and Occupational Health, School of Public Health and Department of Geography, Indiana University, 1025 E. Seventh Street, Suite 111, Bloomington, IN 47405, USA.ORCID https://orcid.org/0000-0002-5301-2764
Philip M ArmstrongDepartment of Entomology, The Connecticut Agricultural Experiment Station, 123 Huntington St., New Haven, CT 06511, USA.ORCID https://orcid.org/0000-0002-7261-9748

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting and projecting risk of West Nile virus (WNV) to humans in areas without mosquito surveillance data is a key limitation of many WNV surveillance programs. To better inform risk of WNV, we analyzed 20 years (2001-2020) of point-level mosquito surveillance data from Connecticut (CT), United States, using machine learning methods to determine the most informative weather variables and land cover classes associated with monthly

Indexed as

Culex pipienshierarchical modelingmachine learningrisk mappingWest Nile virus

Identifiers

PMID40838020
PMCPMC12362355

What OpenQuestion holds

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LicenceCC BY
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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.