Evidence map›Paper›PMID 41089301›Full record

ArticleVeterinary medicine international2025

Spatiotemporal Distribution Characteristic and Influencing Factors of African Swine Fever Outbreaks (2018/8-2019/12) in China.

Juan Li, Bingxin Nie, Shubo Li, Junhui Zhang, Lu Gao

Abstract read
In one paragraph

Article in Veterinary medicine international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Juan LiSchool of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, China.ORCID https://orcid.org/0000-0003-1715-9141
Bingxin NieSchool of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, China.ORCID https://orcid.org/0009-0005-1204-5163
Shubo LiLiaoning Center for Animal Disease Control and Prevention, Shenyang, China.ORCID https://orcid.org/0009-0007-4535-678X
Junhui ZhangSchool of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, China.ORCID https://orcid.org/0009-0006-9066-5058
Lu GaoChina Animal Health and Epidemiology Center, Qingdao, China.ORCID https://orcid.org/0000-0003-2299-1004

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

African swine fever (ASF), a highly lethal viral disease with no effective vaccines or treatments, poses a significant threat to the global pig industry. Since its first report in China in August 2018, it has spread rapidly, severely impacting China's pig industry. This study developed a Bayesian spatiotemporal model to explore ASF's spatiotemporal patterns, assess relative risk (RR), and identify key factors, aiming to inform targeted prevention strategies. Data (disease-related deaths, pig inventory, GDP, temperature, and 6 other factors) were collected from 31 mainland Chinese provinces from August 2018 to December 2019. The INLA algorithm estimated parameters, with the optimal model selected via DIC and WAIC. Multicollinearity was addressed using VIF and Spearman's correlation coefficient. Univariate and multivariate models quantified factor effects, with risk classified by natural breaks. Significant spatiotemporal patterns emerged: high-risk clusters in Liaoning, Heilongjiang, and Beijing, lower risk in Yunnan and Chongqing. Economic factors and veterinary resources were crucial: GDP per capita correlated positively (RR = 1.8814, 95% CI: 1.1264, 3.1362), while veterinarian numbers correlated inversely (RR = 0.7233, 95% CI: 0.4776, 0.9637). This study clarifies ASF dynamics and influencing factors in China, highlighting the need to strengthen veterinary services and balance economic development with biosecurity, offering a global reference for infectious disease management.

Indexed as

African swine fever (ASF)Bayesian spatiotemporal modelGross domestic product (GDP)influencing factorsprevention and controlrelative risk (RR)veterinary resources

Identifiers

PMID41089301
PMCPMC12517988

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.