{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:10:10Z","timestamp":1780470610143,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Program of Henan Province","award":["262102111011"],"award-info":[{"award-number":["262102111011"]}]},{"name":"Modern Agricultural Industry Technology System in Henan Province","award":["HARS2212G3"],"award-info":[{"award-number":["HARS2212G3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>African swine fever (ASF) poses a persistent and escalating threat to global swine production. To comprehensively characterize its global spatiotemporal dynamics from 1996 to 2025, we developed an integrated framework combining multi-distance spatial analysis and advanced time series forecasting, utilizing a dataset of 57,253 outbreak records. Our findings reveal a clear divergence in transmission patterns: wild boar accounted for approximately 70% of outbreaks and predominantly sustained transmission in Eastern Europe, whereas domestic pig outbreaks were largely concentrated in Southeast Asia. A pronounced epidemiological shift occurred between 2017 and 2020, during which ASF spread transitioned from a predominantly north\u2013south axis linking Africa and the Caucasus to a broad east\u2013west expansion across Eurasia, coinciding with rapid dissemination throughout Asia. In the Northern Hemisphere, ASF outbreaks exhibited a bimodal seasonal pattern, with peaks observed in January\u2013March and July\u2013August. Comparative forecasting analyses demonstrated that machine learning approaches consistently outperformed both traditional statistical and deep learning models. Among these, the random forest algorithm achieved the highest predictive accuracy, surpassing SARIMA, Prophet, XGBoost, and GRU. Collectively, these findings underscore the pivotal role of wild boar in maintaining global ASF transmission and highlight the necessity of integrated surveillance at wildlife\u2013livestock interfaces. Furthermore, they support the application of machine learning-based approaches for improving early warning systems and enhancing the effectiveness of global ASF control strategies.<\/jats:p>","DOI":"10.3390\/v18060618","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:17:31Z","timestamp":1780467451000},"page":"618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Global Spatiotemporal Dynamics of African Swine Fever: An Integrated Multi-Scale Spatial and Time-Series Analysis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4101-5922","authenticated-orcid":false,"given":"Renfeng","family":"Li","sequence":"first","affiliation":[{"name":"College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China"},{"name":"Henan Provincial Key Laboratory of Zoonoses of Companion and Wild Animals, Henan Institute of Science and Technology, Xinxiang 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunshi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyan","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biological Engineering, Xinxiang University, Xinxiang 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxuan","family":"He","sequence":"additional","affiliation":[{"name":"College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China"},{"name":"Henan Provincial Key Laboratory of Zoonoses of Companion and Wild Animals, Henan Institute of Science and Technology, Xinxiang 453003, China"},{"name":"National Research Center for Wildlife-Borne Diseases, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Z., Chen, W., Qiu, Z., Li, Y., Fan, J., Wu, K., Li, X., Zhao, M., Ding, H., and Fan, S. 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