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Review
. 2025 Jun;36(2):241-262.
doi: 10.1007/s13337-025-00920-2. Epub 2025 Apr 28.

Comprehensive review and early detection strategies for severe fever with thrombocytopenia: insights from epidemiology, diagnostics, and evolving research with machine learning

Affiliations
Review

Comprehensive review and early detection strategies for severe fever with thrombocytopenia: insights from epidemiology, diagnostics, and evolving research with machine learning

Abhishek S Rao et al. Virusdisease. 2025 Jun.

Abstract

Severe Fever with Thrombocytopenia Syndrome (SFTS) is a neglected tick-borne viral infection caused by Bandavirus dabieense (formerly known as Huaiyangshan banyangvirus and Dabie bandavirus), with the potential for endemic and pandemic transmission through ticks to humans, animals to humans, and human-to-human routes. Low platelet counts in patients (< 150,000 platelets/mm3) lead to severe complications, with a global case fatality ratio of 20-30%. This review aims to summarize Bandavirus dabieense, focusing on its epidemiology, global prevalence, and the impact of geographical and demographic factors on transmission. We conducted a systematic review and bibliometric analysis using the PRISMA framework and Scopus database. The study explores transmission routes of Bandavirus dabieense, diagnostic methods, treatments, and challenges, with an emphasis on machine learning (ML) for diagnosis and awareness initiatives. The results reveal global Bandavirus dabieense prevalence, transmission modes, diagnostic advancements, vector controls, and early detection hurdles. Bibliometric analysis identifies knowledge gaps and emphasizes the study's relevance. The review underscores the importance of understanding and addressing Bandavirus dabieense, advocating for proactive measures such as ML for diagnosis, recommendations for future work, and effective elimination strategies. Heightened awareness and comprehensive research are vital to mitigate the public health impact and reduce mortality associated with Bandavirus dabieense.

Keywords: Acute febrile illness; Healthcare; Machine learning; Mortality prediction; Thrombocytopenia.

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Conflict of interest statement

Conflict of interestThe authors declare that there is no conflict of interest.

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