Evidence map›Paper›PMID 42894539›Full record

ArticleiScience2026

High-resolution prediction and risk mapping of

Xue-Kun Bian, Shi-Pan Chen, Jia-Yi Xu, Han Xue, Ao-Long Xu, Shi-Jun Song, Ying-Zhi Chong, Fei Wang, Na Tian, Hao-Ran Ding and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

13 authors.

Xue-Kun BianSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Shi-Pan ChenSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Jia-Yi XuSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Han XueSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Ao-Long XuSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Shi-Jun SongSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Ying-Zhi ChongSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Fei WangSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Na TianSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Hao-Ran DingSchool of Medicine, Qingdao Binhai University, Qingdao, Shandong 266555, People's Republic of China.
Jing-Han FengSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.
Yan LuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Key Laboratory on Parasite and Vector Biology, Ministry of Health, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, People's Republic of China.
Lan-Hua LiSchool of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ticks are vectors of numerous human pathogens, and accurate predictions of their spatial distribution are essential for the effective prevention and control of tick-borne diseases. High-resolution predictions remain scarce in China. To address this gap, a tick survey was conducted across the Jiaodong Peninsula at 2,647 points during the summer of 2024, integrating high-resolution environmental variables. Seven machine-learning algorithms were used, and ensemble learning was applied, resulting in a habitat-suitability map for

Indexed as

Chinaensemble modelJiaodong Peninsulamachine learningspecies distribution predictiontick

Identifiers

PMID42894539
PMCPMC13676737

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.