{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:41:54Z","timestamp":1792597314578,"version":"4.5.0"},"reference-count":222,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Vector-Borne and Zoonotic Diseases"],"published-print":{"date-parts":[[2026,12,1]]},"abstract":"<jats:p>\n                    The escalating threat of viral pandemics, dramatically illustrated by the COVID-19 crisis, has exposed the critical shortcomings of conventional reactive virology in addressing rapidly evolving pathogens. This review introduces predictive virology (PV) as an artificial intelligence (AI)-driven discipline within broader epidemic intelligence and public health surveillance that uses advanced computational tools to forecast viral threats and accelerate countermeasure design. The current review systematically examines how AI-driven approaches (\n                    <jats:italic toggle=\"yes\">e.g.<\/jats:italic>\n                    , machine learning and deep learning) are reshaping virology by integrating vast genomic datasets, multimodal surveillance signals, and advanced computational models to anticipate viral emergence and evolution before widespread transmission occurs. Core pillars of PV discussed include zero-shot mutational fitness and antigenic escape prediction using large protein language models; multimodal early-warning systems that fuse wastewater monitoring, digital epidemiology, mobility data, and social media; neural differential equation-based transmission modeling; generative AI for\n                    <jats:italic toggle=\"yes\">de novo<\/jats:italic>\n                    design of broad-spectrum antivirals and vaccines; and ecological risk assessment of zoonotic spillovers. In retrospective benchmarks against deep mutational scanning experiments and real-world epidemiological outcomes (SARS-CoV-2 variants, influenza, and other outbreaks), several AI-powered tools have demonstrated performance comparable to or exceeding traditional methods, although prospective validation at scale remains limited. Despite remarkable progress, significant challenges persist, including data bias, overfitting to historical patterns, lack of prospective validation, and limited generalizability across settings. In addition, there are concerns about mechanistic interpretability, equitable global data integration, and responsible deployment. This review also critically addresses the ethical, governance, and equity implications of deploying predictive capabilities at a global scale. By consolidating cutting-edge AI methodologies with virological insights and acknowledging current limitations, this work provides a comprehensive framework for transitioning virology from a reactive to a truly predictive discipline, ultimately strengthening global health security and pandemic preparedness.\n                  <\/jats:p>","DOI":"10.1177\/15303667261469037","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:57:32Z","timestamp":1784213852000},"page":"733-755","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Artificial Intelligence and Machine Learning in Predictive Virology: Forecasting, Tracking, and Combating Viral Threats"],"prefix":"10.1177","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-6750","authenticated-orcid":false,"given":"Mohamed A.","family":"Farrag","sequence":"first","affiliation":[{"name":"Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/hec.4218"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.12688\/wellcomeopenres.16006.2"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07487-w"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.9734\/ijtdh\/2025\/v46i101694"},{"key":"e_1_3_3_6_1","first-page":"98","article-title":"Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections","volume":"6","author":"Ala\u2019raj M","year":"2021","unstructured":"Ala\u2019raj M, , Majdalawieh M, , Nizamuddin N. 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