Spatiotemporal analysis of plague risk in Tibet: Multi-source data-driven ensemble model development and validation.

Luo Guo, Xiaoyan Zhang, Zhan Lin, Zhuang Cui, Changping Li, Su Wu

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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.

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1 · What the graph read from it

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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.

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2 · The registry

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5 · Who and what money

Authors and funding

6 authors.

Luo GuoDepartment of Industrial Engineering, Tsinghua University, Beijing, China.

Xiaoyan ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Zhan LinDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Zhuang CuiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Changping LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Su WuDepartment of Industrial Engineering, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Plague, caused by the highly lethal bacterium Methods: Using Tibet plague surveillance data (2000-2021), we integrated eight environmental variables (maximum temperature, precipitation, normalized difference vegetation index, land use and land cover, soil moisture, slope, aspect, and population spatial distribution). Ten algorithms via BIOMOD2 modeled plague risk across four phases (2000-2004, 2005-2009, 2010-2015, 2016-2021), validated with 2022-2023 data. We simulated risk distribution, calculated area/centroid changes, and elucidated spatiotemporal evolution. Results: The ensemble model (EM) showed excellent external validation (2022: Conclusion: Multi-model integration and spatiotemporal simulation revealed plague foci distribution and centroid evolution in Tibet, capturing human-climate impacts, providing a scientific basis for targeted prevention in high-risk areas.

Indexed as

Environmental driversPlague natural fociSpatiotemporal dynamicsSpecies distribution modelTibet autonomous region

Identifiers

PMID41883808

DOI10.1016/j.onehlt.2026.101354

PMCPMC13010948

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

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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 Socratic graph.