Evidence map›Paper›PMID 42548946›Full record

ArticleOne health (Amsterdam, Netherlands)2026

Developing machine learning models to forecast mosquito abundance for climate change impacts and early warning system.

Seun Jung, Hyemin Jeong, Yoonnoh Lee, Seungho Han, Seonguk Son, Byoung-Hak Jeon, Heesun Chung, Wooae Byun, Dong Gun Kim, Sangchul Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in One health (Amsterdam, Netherlands), 2026. 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

10 authors.

Seun JungDivision of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
Hyemin JeongDepartment of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
Yoonnoh LeeDepartment of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
Seungho HanDepartment of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
Seonguk SonDepartment of Convergence Science, Graduate School of Sahmyook University, Seoul 01795, Republic of Korea.
Byoung-Hak JeonInfectious Disease Control Division Citizen'health Bureau, Seoul Metropolitan Government, Seoul 04524, Republic of Korea.
Heesun ChungInfectious Disease Control Division Citizen'health Bureau, Seoul Metropolitan Government, Seoul 04524, Republic of Korea.
Wooae ByunInfectious Disease Control Division Citizen'health Bureau, Seoul Metropolitan Government, Seoul 04524, Republic of Korea.
Dong Gun KimThe Faculty of General Education, Sahmyook University, Seoul 01795, Republic of Korea.
Sangchul LeeDivision of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk of mosquito-borne diseases is expected to increase due to climate change, and thus it is needed to forecast how mosquito abundance would vary in response to dynamic climatic conditions. Both short-term prediction and long-term projection are essential for supporting timely mosquito control and mitigating vector-borne disease risk. This study developed a machine learning-based forecasting framework to support an early warning system for short-term mosquito abundance prediction and to project long-term mosquito abundance. Short-term prediction was conducted using machine learning models (MLMs) with short-term meteorological forecast data. Long-term projection was made by future climate data (∼2100) under SSP1-2.6 and SSP2-4.5. Results showed that MLMs using short-term meteorological forecast data produced mosquito abundance comparable to those using observational data (R

Indexed as

Climate changeEarly warning systemMachine learningMosquito abundanceTemporal shift

Identifiers

PMID42548946
PMCPMC13430193

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.