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

**Authors:** Juan Li, Bingxin Nie, Shubo Li, Junhui Zhang, Lu Gao

PMC · DOI: 10.1155/vmi/9954801 · 2025-10-06

## TL;DR

This study analyzed the spread of African swine fever in China from 2018 to 2019, identifying key factors and high-risk regions to help prevent future outbreaks.

## Contribution

The study introduces a Bayesian spatiotemporal model to analyze ASF spread and risk factors in China.

## Key findings

- High-risk clusters were identified in Liaoning, Heilongjiang, and Beijing.
- GDP per capita positively correlated with ASF risk, while veterinarian numbers inversely correlated.
- The study emphasizes the importance of veterinary services and biosecurity for disease control.

## 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.

## Linked entities

- **Diseases:** African swine fever (MONDO:0025377)

## Full-text entities

- **Diseases:** viral disease (MESH:D014777), ASF (MESH:D000357), infectious disease (MESH:D003141)
- **Species:** Sus scrofa (pig, species) [taxon 9823]

## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12517988/full.md

---
Source: https://tomesphere.com/paper/PMC12517988