Evidence map›Paper›PMID 42180664›Full record

ArticleChina CDC weekly2026

Risk Assessment Method for Global Monkeypox Importation into China - May 2022-September 2025.

Shihui Shan, Yan Zhang, Yanhe Wang, Shenghong Lin, Qiang Xu, Chenlong Lyu, Yao Tian, Jun Ma, Xinjing Zhao, Yufeng Yang and 3 more

Abstract read
In one paragraph

Article in China CDC weekly, 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.

Shihui Shan *State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Yan Zhang *State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Yanhe Wang *The 968th Hospital of Joint Logistics Support Force of PLA, Jinzhou City, Liaoning Province, China.
Shenghong LinState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Qiang XuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Chenlong LyuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Yao TianState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Jun MaSchool of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang City, Guizhou Province, China.
Xinjing ZhaoState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Yufeng YangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Yong ZhangSchool of Mathematical Sciences, Beijing Normal University, Beijing, China.
Gang DongState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
Liqun FangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the context of globalization, the risk of cross-border transmission of infectious diseases is continuously increasing. There is an urgent need to develop a scientific, dynamic, and practical tool that captures regional heterogeneity in importation risk, thereby facilitating more targeted prevention and control measures at ports of entry. Methods: We devised an extensible importation risk assessment framework by selecting key indicators through a Delphi expert consultation process and assigning their respective weights using the Analytic Hierarchy Process (AHP). Taking monkeypox (mpox) as a case study, we applied the framework to the global mpox epidemic spanning from May 2022 to September 2025 under three risk scenarios: Clade I, Clade II, and a non-clade-specific (aggregated) scenario. We then analyzed the temporal evolution of major source countries, the spatial distributions of provincial risk levels, and the association between clade-specific importation risk indices and domestically reported mpox cases across the study period. Results: Our clade-stratified risk assessment framework demonstrates a high degree of consistency and reveals significant disparities between Clade I and Clade II in both international source contributions and provincial vulnerability. The importation risk associated with Clade I is predominantly influenced by Central African countries, particularly the Democratic Republic of the Congo and Uganda. In contrast, the risk related to Clade II is primarily associated with the United States and Southeast Asian nations, such as Thailand and the Philippines. Provincial importation risks generally adhere to a spatial pattern of "east-high, west-low": Shandong Province consistently exhibits a very high risk under Clade I, while Fujian Province maintains a very high risk under Clade II. Notably, the Clade II-specific risk index exhibited a statistically significant positive correlation with national mpox case counts (Spearman's Conclusion: This framework effectively captures the structural shift in the sources of mpox importation risk and the regional heterogeneity in risk levels across PLADs in China. It thereby provides a risk-driven decision-support framework for prioritizing port-of-entry screening and tailoring surveillance strategies to high-risk areas.

Indexed as

Importation riskmpoxRisk Assessment

Identifiers

PMID42180664
PMCPMC13191293

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.