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

Comprehensive review and early detection strategies for severe fever with thrombocytopenia: insights from epidemiology, diagnostics, and evolving research with machine learning - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Virusdisease . 2025 Apr 28;36(2):241–262. doi: 10.1007/s13337-025-00920-2 Comprehensive review and early detection strategies for severe fever with thrombocytopenia: insights from epidemiology, diagnostics, and evolving research with machine learning Abhishek S Rao 1, 2, ✉ , B H Karthik Pai 1, 2 , K Adithi 3 , Rathika Shenoy 4 , Sneha Nayak 5 Author information Article notes Copyright and License information 1 Department of Information Science & Engineering, Nitte (Deemed to be University), NMAM Institute of Technology, Nitte, Karnataka India 2 Department of Computer Science & Engineering, Canara Engineering College, Benjanapadavu, Karnataka India 3 Department of General Medicine, Nitte (Deemed to be University), K S Hegde Medical Academy, Mangaluru, Karnataka India 4 Department of Clinical Medicine, American University of Antigua, University Park, Antigua and Barbuda 5 Department of Biotechnology Engineering, Nitte (Deemed to be University), NMAM Institute of Technology, Nitte, Udupi, Karnataka India ✉ Corresponding author. Received 2024 Feb 2; Accepted 2025 Mar 2; Issue date 2025 Jun. © The Author(s), under exclusive licence to Indian Virological Society 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12474768  PMID: 41018215 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/mm 3 ) 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 . Graphical abstract Keywords: Acute febrile illness, Thrombocytopenia, Mortality prediction, Healthcare, Machine learning Introduction Severe Fever with Thrombocytopenia Syndrome (SFTS) is a rapidly evolving tick-borne infectious disease characterized by a substantial case-fatality rate. Notably, no vaccine for this disease has been developed thus far [ 1 ]. It is caused by a novel virus, Bandavirus dabieense , previously known as Huaiyangshan banyangvirus and Dabie bandavirus , classified in the genus Phlebovirus and family Bunyaviridae. Fever is usually considered to be an indicator that indicates an infection or an illness in our body. However, identifying the real cause of infection in many febrile infants and adults is a challenging task in the case of asymptomatic patients [ 2 ]. Thrombocytopenia is a medical condition in which a patient will have a low blood platelet count (< 150,000 platelets/ mm 3 ). Comprehensive clinical and laboratory assessments were conducted on patients presenting with thrombocytopenia to determine its underlying causes. These results underscore the presence of numerous undisclosed infections as significant contributors to thrombocytopenia [ 3 ]. Thus, there is a broad variance diagnosis for patients presenting with fever and a low platelet count. Despite investigations, the etiology may not be determined, and the treatment is empirical with increased mortality. Investigations have confirmed the genomic evolution of Bandavirus dabieense using genome sequencing techniques [ 4 ]. In patients with Bandavirus dabieense , the inclusion criteria were a short period of fever (< 14 days) and low platelet count (< 150,000 platelets/ mm 3 ). However, the above details per day are not sufficient, and further details related to demographic information (age, sex, place, duration of illness at the time of admission (days), duration of hospital stay (days), requirement for ICU admission, and outcome at discharge), and data related to vital signs at admission during the first 48 h are needed [ 5 ]. This may include details related to airway and breathing, circulation, respiration, cardiovascular system, renal, hematological, gastrointestinal, hepatic, nervous system, and comorbidities, which may be required for mortality prediction based on the weights assigned to each parameter. Comorbidity details, such as chronic liver disease, hypertension, diabetes mellitus, and other details regarding alcohol dependence, heavy smoking, and immunosuppressant medication may also be critical in predicting survival or mortality. Laboratory details that may influence outcomes include various organ parameters, such as liver enzymes, renal function, and coagulopathy. Since laboratory tests do not always lead to a confirmed diagnosis, and treatment is often empirical, the use of artificial intelligence (AI) models for early disease prediction could be valuable. A variety of statistical methods, including naive Bayes and logistic regression models, are used to forecast the likelihood of death, and no clinical score model is yet available for prognosis prediction [ 6 ]. Bandavirus dabieense was predominantly reported in certain regions of East Asia, particularly in rural areas where individuals had more exposure to ticks. The disease’s prevalence was relatively low compared to other infectious diseases, but it raised concerns due to its high mortality rate, which varied from region to region. Bandavirus dabieense has been classified by the World Health Organization (WHO) as a priority pathogen requiring immediate attention due to its fatality rate of up to 30%, posing a serious threat to public health [ 7 ]. Using epidemiological data, including demographic, geographic, and meteorological characteristics, few studies have estimated the monthly incidence and number of Bandavirus dabieense cases in the geographic area of Korea [ 8 ]. Machine learning (ML) approaches were used to create a parsimonious model in the form of a decision tree to assess the severity of Bandavirus dabieense using the most frequently observed symptoms and laboratory values at admission. The decision tree classification strategy based on laboratory parameters was found to be more effective than models based on clinical and demographic factors [ 9 ]. Figure 1 illustrates the PRISMA model, which serves as a systematic framework for streamlining the exhaustive processes of identifying, screening, and selecting pertinent research articles [ 9 ]. This approach empowers researchers to distill and present the latest and most pertinent findings on this emerging infectious disease. By adhering to PRISMA guidelines, the study aimed to ensure transparency, rigor, and reproducibility in the review process, thereby contributing to a better understanding of the disease’s epidemiology, clinical manifestations, and potential interventions. Fig. 1. Open in a new tab PRISMA flowchart [ 10 ] The review’s primary objective is to provide a concise summary of Bandavirus dabieense , covering its epidemiology, global prevalence, and the significant influence of geographical and demographic factors on its transmission. It offers a comprehensive examination of the various routes of Bandavirus dabieense transmission, advanced diagnostic methods, available clinical treatments, and the key obstacles related to early detection. The review underscores a particular focus on the utilization of ML methods in the context of diagnosing and managing Bandavirus dabieense , with an additional emphasis on the significance of incorporating awareness-creation efforts for elimination strategies. Epidemiology Ticks, acting as viral carriers, are recognized as the main source of Bandavirus dabieense . Wild animals and livestock, serving as intermediaries or potential amplifiers, play a role in the transmission of Bandavirus dabieense [ 11 ]. The period from April to October is recognized as the endemic season for Bandavirus dabieense transmission, with the disease reaching its peak between May and July. This peak coincides with the hot and humid conditions, which provide an optimal environment for the multiplication of the virus [ 1 ]. Diverse environmental elements contribute to favorable conditions for the growth and reproduction of ticks. These factors encompass a humid climate [ 12 ], wooded or mountainous regions [ 13 ], increased human outdoor activities [ 14 ], and increased interactions with ticks in hills and fields, thereby elevating the risk of infection [ 15 ]. Global prevalence of Bandavirus dabieense The global prevalence of Bandavirus dabieense has been a cause for concern, particularly in East Asia, where the disease was first identified. While the prevalence of Bandavirus dabieense varies from region to region, it has been reported in several countries, including China, South Korea, Japan, and Vietnam. The global prevalence of Bandavirus dabieense highlights the importance of continued surveillance, research, and public health measures to understand and mitigate the impact of this disease. Efforts to prevent tick bites, improve diagnostic capabilities, and raise awareness about Bandavirus dabieense are crucial in controlling its spread and reducing its impact on public health. Hence, this study analyses studies that focus on the impact of Bandavirus dabieense-related disease in adults and the use of various machine-learning techniques that would ease disease prediction at early stages. Bandavirus dabieense patients may also show certain patterns such as topographical, seasonal, demographic, laboratory, therapy, or other variations in their data, which could provide perceptions of the underlying cause of their illness. Inclusion and exclusion criteria Clear articulation of the inclusion and exclusion criteria is essential to facilitate the reader’s understanding. Inclusion criteria define the characteristics of participants considered for the study, while exclusion criteria specify the characteristics that disqualify individuals from the study. For the context of Bandavirus dabieense , the inclusion criteria pertain to individuals diagnosed with confirmed Bandavirus dabieense , while the exclusion criteria encompass individuals with pre-existing comorbidities and those with incomplete or insufficient medical records as given in Table 1 . Table 1. Inclusion and exclusion criteria for Bandavirus dabieense related diseases: critical factors and key analysis Study area Prediction using No. of cases Inclusion criteria Exclusion criteria Critical factors Analysis AUC References Hubei and Henan provinces, China Clinical symptoms and laboratory parameters 179 1. Acute fever (temperature of 38 °C or higher) 1. Coinfection by other viruses 1. Elevated level of neurologic symptoms 1. Pearson chi-square and Fisher’s exact tests 0.964 [ 16 ] 2. Respiratory symptoms 2. Thrombocytopenia (platelet count <100 × 109/L) 2. History of other serious chronic diseases 2. Mann–Whitney U test 3. Bandavirus dabieense by RT-PCR 3. Viral load 4. A lower % of monocyte 3. Multivariable logistic regression analysis Central and northeastern China Clinical symptoms and laboratory parameters 429 1. Acute fever (temperature >37.5 °C for over 24 h) 1. laboratory-confirmed other pathogen infection 1. Low lymphocyte percentage 1. Pearson chi-square and Fisher’s exact tests Low lymphocyte %: 0.944 [ 17 ] 2. Early-stage encephalopathy 2. Thrombocytopenia (platelet count <100×109 /L) 2. Mann–Whitney U test 3. Elevated concentration of serum lactate dehydrogenase and Blood urea nitrogen 3. Multivariable Cox analysis 2. History of acute or chronic blood system diseases 3. Laboratory-confirmed Bandavirus dabieense infection by detection of viral RNA (fluorescent probe PCR) 4. Virus-specific IgM antibody in the peripheral blood Jiangsu Province, China Demographic factors Clinical presentations Physical examination and laboratory parameters 142 1. Acute fever of > 38°C with thrombocytopenia (platelet (PLT) count <100×109/L) 1. Age 1. Chi-square test 0.927 [ 18 ] 2. Activated partial thromboplastin time value 2. Mann-Whitney U-tests 2.Laboratory-confirmed Bandavirus dabieense infection using RT-PCR 3. Binary logistic regression analyses 3. Blood urea nitrogen level on admission Anhui Province, China Fasting blood glucose levels 77 1. No FBG data available at admission Elevated levels of fasting blood glucose correlated to hypercoagulability, inflammation, and lower platelet 1. Chi-square or Mann-Whitney U test [ 19 ] 2. Multivariate statistical analysis 2.Having previously diagnosed with diabetes. Yantai Province, China Demographic and clinical characteristics 321 Acute fever with thrombocytopenia with multi-organ dysfunction 1. Age 1. Chi-square test 0.919 [ 20 ] 2. Quick sequential organ failure assessment (qSOFA score) 2. Mann-Whitney U-tests 3. Multivariate logistic regression model 3. Systemic inflammatory response syndrome (SIRS score) 4. Aspartate aminotransferase (AST) Open in a new tab Examination of the Scopus database with the keyword “Severe fever with thrombocytopenia syndrome” between 1960 and 2022 shows a rapid rise in the number of papers published over the years with 16,337 documents (Fig. 2 A), while research on the country-wise study and its severity is gaining momentum as visualized from the search for “ Bandavirus dabieense papers country-wise (Fig. 2 B). The USA topped the list with 4856 documents and India stood 5th with 1065 documents, understanding the need for further research in this area. However, search results on “Severe fever with thrombocytopenia syndrome severity” between 1971 and 2022, show 5541 documents (Fig. 3 A), and “Severe fever with thrombocytopenia syndrome death” between 1964 and 2022 yielded 4756 documents (Fig. 3 B). This proves that better healthcare facilities with proper diagnosis could prevent mortality, which could be further improved by the inclusion of AI and ML for the rapid diagnosis of this disease. Fig. 2. Open in a new tab Papers published on “ Bandavirus dabieense ” between 1960 to 2022 ( A ); and on “ Bandavirus dabieense -related papers by country” ( B ), based on a recent search in the Scopus Database Fig. 3. Open in a new tab Papers published on “ Bandavirus dabieense severity” between 1971 and 2022 ( A ); and on “ Bandavirus dabieense -related mortality” between 1964 and 2022 ( B ), based on a recent search in the Scopus database Despite the limited number of ML models available in the past considering ground conditions, symptoms about the area and the impact of geographical location on the disease progression analysis is critical. The next section highlights the importance of this in the present context. Geographical distribution of Bandavirus dabieense After its initial discovery in 2010 across six prominent Asian countries, including China, Japan, Korea, Vietnam, Myanmar, and Thailand, Bandavirus dabieense has exhibited a remarkable capacity for global dissemination. This widespread transmission of Bandavirus dabieense cases can be attributed to the virus’s unique ability to endure in a variety of animal and human hosts, thus enabling its spread to a broader population [ 4 ]. Bandavirus dabieense has been classified by the WHO as a priority pathogen that requires immediate attention because it has a fatality rate of up to 30%, posing a serious threat to public health [ 21 ]. Detailed clinical and laboratory evaluation of patients with low platelet count to study the etilogy have revealed an upsurge in fever with thrombocytopenia cases in Northern Kerala. The research finding throws light on the fact that many unidentified infections cause thrombocytopenia [ 22 ]. Current research on Bandavirus dabieense is predominantly concentrated in endemic regions such as East Asia, particularly China, Korea, and Japan. This focus has been due to the higher incidence of the disease in these areas, where the primary tick vector is prevalent. While valuable insights have been gained in these regions, the concentration of research limits the understanding of Bandavirus dabieense potential spread outside of these areas. Global factors like climate change may influence the spread of Bandavirus dabieense . Warmer temperatures and shifting rainfall patterns could expand the habitats of tick vectors, potentially introducing the disease into regions that have not experienced it before. Southeast Asia, Europe, and the Americas may become increasingly vulnerable as environmental changes alter the distribution of ticks that transmit the virus [ 23 ]. The lack of research in non-endemic regions hinders the ability to assess the virus’s transmission potential outside of established hotspots. These regions may present unique challenges due to different environmental, ecological, and healthcare conditions, and without studies focusing on these areas, it is difficult to understand how the disease might evolve or spread. Expanding global surveillance to include non-endemic regions is critical. Monitoring these areas for signs of emerging cases and changes in vector populations can help detect early outbreaks and identify areas at risk. Active surveillance programs should be designed to gather data on environmental changes and tick behavior, which are crucial for understanding the broader risks of Bandavirus dabieense [ 24 ]. Cross-regional collaborations are essential for improving the understanding of Bandavirus dabieense transmission across diverse regions. Sharing research, data, and expertise between endemic and non-endemic regions can enhance knowledge and enable the development of adaptable strategies for diagnosis, prevention, and control of the disease. These collaborations are vital to address the potential global spread of Bandavirus dabieense [ 25 ]. Some details of Bandavirus dabieense cases in various geographical areas are shown in Table 2 . Table 2. Overview of Bandavirus dabieense cases in Asian countries Characteristics Xiyang, China Nagasaki Prefecture, Japan Zhejiang Province, Eastern China Oita, Japan Jeju, China NESID, Japan Year of surveillance 2011–2020 2013–2020 2011–2020 2013–2021 2013–2022 2013–2017 Data collection period Apr 2011–Oct 2020 Jan 2013-Dec 2020 Jan 2011–Dec 2020 Apr 2013–Mar 2021 Mar 2013–Aug 2022 Mar 2013–Oct 2017 No of patients 1149 23 hospitalized patients 188 hospitalized cases including 40 deaths 494 91 303 Case fatality ratio (%) 32.9 21.3 19 10.9 27 References [ 30 ] [ 31 ] [ 32 ] [ 33 ] [ 34 ] [ 35 ] Open in a new tab Demographic characteristics Demographic characteristics are known to have an impact on the severity of Bandavirus dabieense among individuals. For instance, older age group individuals are more prone to the complexity and severity of Bandavirus dabieense may be due to lower immunity or pre-existing co-morbidities [ 26 ]. Low serum FT3 and age were the independent factors for mortality in Bandavirus dabieense patients as seen by the developed risk score model. Also, the level of FT3 was much lower in patients having neuropsychiatric issues which accounted for significant mortality cases among the selected 207 cases [ 27 ]. A higher case fatality ratio (CFR) of 17.7% was seen in males compared to females (13.7%) in a study conducted in China. Males greater than 60 years of age had a reduced percentage of Immune cells like CD4 cells, and B lymphocytes and had an increased count of natural killer cells than individuals less than 60 years of age. This accounts for the fact that age and sex are significant contributors to Bandavirus dabieense and need to be considered as significant parameters for its effective management [ 28 ]. Creating awareness about Bandavirus dabieense in the endemic population through intervention programs using questionnaires, and pre-and post-assessment forms has helped to significantly increase the Knowledge attitude practices scores (KAP scores) [ 29 ]. Therefore, it is evident that demographic factors like age, sex, immunity levels, educational levels, and ethnicity would significantly contribute to the disease. The use of gold standard laboratory confirmatory tests like real-time reverse transcriptase polymerase chain reaction (RT-PCR) followed by virus isolation have been tried in the Hubei Province Center for Disease Prevention and Control, China in early 2012 [ 36 ]. However, newer techniques like electron microscopy [ 37 ], and lateral flow immunoassay (LFIA) strip have been recently explored for detecting Bandavirus dabieense [ 38 ], and Nucleocapsid-based ELISA test has been explored for Bandavirus dabieense specific IgM antibodies detection [ 39 ]. Reported transmission routes of Bandavirus dabieense Bandavirus dabieense is known to be transmitted through three main routes: ticks to humans, animals to humans, and human-to-human transmission. Among these routes, tick-to-human and human-to-human transmission are the most reported. The zoonotic transmission of Bandavirus dabieense to humans by the H. longicornis tick species is well-documented in the literature. However, there have been reports of other tick species worldwide, and their presence establishes them as confirmed vectors for Bandavirus dabieense transmission. An overview of various tick species as vectors for Bandavirus dabieense transmission along with the hosts and diagnostic options used for its detection is presented in Table 3 . Table 3. Overview of various tick species as vectors for Bandavirus dabieense transmission Country Vector Host Diagnosis References Hubei Province, China Haemaphysalis flava Lab Infected Rabbit *Blood test *RT-PCR [ 46 ] Republic of Korea Haemaphysalis longicornis *RT-PCR *Indirect immunofluorescent antibody assay (IFA) *Imunoblotting *Morphological identification: TEM [ 47 ] Hainan Island, China Rhipicephalus sanguineus Rabbit RT-PCR [ 48 ] Shandong Province, China Haemaphysalis concinna Goat PCR assay [ 49 ] Hainan Island, China Rhipicephalus sanguineus Rhipicephalus microplus Canine and bovine PCR assay [ 50 ] Hokkaido, a northern island of Japan Amblyomma testudinarium Brown bear PCR/RT-PCR [ 51 ] Xinjiang, China Dermacentor nuttalli Hyalomma asiaticum *Blood test *Elisa *Negative staining electron microscopy [ 52 ] Miyazaki Prefecture, Japan Amblyomma testudinarium Haemaphysalis flava Haemaphysalis formosensis Haemaphysalis hystricis Haemaphysalis megaspinosa RT-PCR [ 53 ] Open in a new tab Both domestic and wild animals have been identified as potential amplifiers for the spread of the Bandavirus dabieense . Recent meta-analyses conducted in China have confirmed the presence of anti- Bandavirus dabieense antibodies, specific to Bandavirus dabieense (IgG or IgM), in various animal hosts. However, a systematic assessment of seroprevalence has not been conducted thus far [ 40 ]. In total, anti- Bandavirus dabieense antibodies (IgG or IgM) were detected in 15 different animal species. Notably, the pooled seroprevalence of anti- Bandavirus dabieense antibodies was found to be 45.70% in goats and sheep, 36.70% in cattle, 29.50% in dogs, 9.60% in chickens, 3.20% in rodents, and 3.20% in pigs [ 40 ]. Besides, the seroprevalence of Bandavirus dabieense through molecular techniques is confirmed in poultry [ 41 ] and horses [ 35 ] in the republic of Korea. Prevalence of the virus is also reported in ruminants and wildlife in Taiwan [ 14 ] and medium-sized wild mammals in Miyazaki, Japan [ 42 ]. Therefore, it is established that animals indeed serve as reservoirs for Bandavirus dabieense [ 43 ], and further research is imperative to better understand the overlooked role of certain animal vectors in the transmission of the virus. Apart from the zoonotic transmission and animal-to-human transmission, transmission of Bandavirus dabieense through human-to-human contact is significantly reported [ 44 ]. The likelihood of this can be attributed to factors such as close contact with infected individuals in home care settings, as well as exposure in hospital environments during disease management and postmortem investigations of mortality cases. These circumstances elevate the risk of Bandavirus dabieense transmission [ 44 ]. Sexual transmission of Bandavirus dabieense is also a rare possibility as reports have confirmed the presence of Bandavirus dabieense in the semen of infected patients [ 45 ]. The available routes of Bandavirus dabieense transmission are presented in Fig. 4 . Fig. 4. Open in a new tab Available routes of Bandavirus dabieense transmission Ground reality in South Asian Countries Bandavirus dabieense , a rapidly emerging infectious disease first seen in China has also been recognized in other Southeast Asian countries like Japan and India. The rising number of cases, the disease’s spreading across a vast geographical area, and the consistently elevated case fatality rate (CFR) have sparked significant apprehension throughout East Asia [ 54 ]. Similarly, Bandavirus dabieense syndrome was reported in South Korea between 2013 and 2015 by the Korea Center for Disease Control and Prevention (KCDC). By evaluating the KCDC surveillance sheets and patient medical records, a total of 172 cases were reported from Jeju Island (hilly terrain). This confirmed the occurrence of ticks in these areas which was the main reason for the endemic outbreak. The case fatality ratio was as high as 32.6% with disease progression like the China case which included fever followed by multi-organ failure, confusion, elevated C reactive proteins and death [ 55 ]. A study conducted in a tertiary care hospital in Karnataka, India, as part of an MD dissertation jointly undertaken by Kathmandu University (Nepal) and Manipal University (India), established specific inclusion and exclusion criteria. The inclusion criteria encompassed individuals over 18 years of age with a fever and a platelet count below 100,000/mm 3 . Conversely, patients treated for fever in the outpatient department were excluded from the study due to the absence of significant details. Among the 471 individuals studied, 118 were diagnosed with dengue, 43 with leptospirosis through Elisa testing, 104 with malaria via rapid diagnostic tests, and 60 were identified as having scrub typhus based on immunofluorescence assays. Approximately 146 cases remained unidentified, although these patients experienced fever and thrombocytopenia. This underscores a direct correlation between platelet counts at admission and disease severity, emphasizing the importance of estimating platelet counts upon admission. Thrombocytopenia emerges as a potential prognostic factor for various acute febrile illnesses. Accurate disease identification is paramount for devising appropriate treatment strategies, as it can influence patient outcomes significantly. Bandavirus dabieense infections have been observed in regions outside their usual endemic areas, including Vietnam. To verify the presence of Bandavirus dabieense viral infection, serum samples were obtained from patients exhibiting symptoms from October 2017 to March 2018 at Hue University Hospital. These samples were subjected to RNA extraction and subsequent real-time RT-PCR studies. Testudinarium ticks are predominant vectors seen in these regions and migratory birds could be potential carriers for these virus-bearing ticks. This further confirms the spread of Bandavirus dabieense infection from multiple transmission routes [ 56 ]. Vector control and its role in containing Bandavirus dabieense Vector control remains the single most effective method for containing and preventing the further escalation of Bandavirus dabieense , particularly in endemic areas. Since Bandavirus dabieense is primarily transmitted by ticks, controlling the vector population plays a vital role in reducing human exposure to the virus. The ticks responsible for spreading Bandavirus dabieense thrive in specific ecological conditions, such as humid, forested, or rural areas. Therefore, targeting these areas for vector control interventions can significantly reduce the incidence of the disease. There are several strategies for vector control, including environmental management, chemical control, and biological control. Environmental management involves removing or reducing tick habitats by clearing vegetation, managing animal populations, and controlling tick refuges in forested areas. Chemical control includes the use of insecticides or acaricides to reduce tick populations. However, this method must be used cautiously to avoid resistance and minimize environmental impact. Biological control strategies are also emerging, such as introducing natural predators of ticks to reduce their numbers in the ecosystem [ 57 ]. In addition to controlling the vector population, personal protective measures, such as using tick repellents, wearing protective clothing, and regular tick checks, are also essential in preventing tick bites [ 58 ]. Public health education and awareness campaigns are necessary to encourage local populations to adopt these preventive measures, especially in areas with high tick exposure [ 59 ]. Furthermore, integrated vector management (IVM), which combines different control strategies tailored to local environmental conditions, can provide a more sustainable and effective approach. This comprehensive method involves regular monitoring of tick populations, the use of insecticides when necessary, and the promotion of public awareness campaigns. Future research and interventions must focus on improving vector control methods in endemic areas and incorporate community participation to achieve better outcomes in preventing the spread of Bandavirus dabieense . Without effective vector control, efforts to control Bandavirus dabieense and prevent its further escalation will be limited, regardless of advances in diagnostic and treatment methods [ 60 ]. Therefore, integrating vector control into the broader disease management and prevention strategies is crucial to reduce the incidence and spread of Bandavirus dabieense . Advanced methods of Bandavirus dabieense diagnosis Apart from the routine clinical and biochemical tests done in the lab to check the symptoms of Bandavirus dabieense by clinicians, use of advanced techniques like PCR and its variants [ 37 ], Reverse transcription recombinant polymerase amplification [ 61 ], CRISPR/Cas12a Technology combined with recombinant polymerase amplification [ 62 ], LAMP assay [ 63 ], ELISA [ 39 ], lateral flow assay [ 38 ], CRISPR/Cas13a microfluidics-enabled digital platform [ 64 ] and electron microscopy [ 37 ] have been explored for the early detection of Bandavirus dabieense virus. However, it is important to recognize that these advanced diagnostic technologies are often out of reach for populations living in resource-poor areas, particularly in South Asian countries like Vietnam, Thailand, mainland China, and India, where Bandavirus dabieense incidence is high [ 65 ]. The availability of advanced molecular diagnostic techniques such as CRISPR and PCR requires expensive equipment, specialized reagents, and skilled personnel, which are typically unavailable in low-resource environments [ 66 ]. In these regions, the lack of infrastructure, limited access to medical care, low health literacy, and insufficient healthcare expenditure contribute to a significant gap in the availability of these advanced diagnostic tools. The primary focus in most techniques involves the extraction of RNA, which is followed by employing various molecular diagnostic methods to quantify Bandavirus dabieense. These methods encompass the examination of samples such as blood, serum, and cerebrospinal fluids [ 67 ]. However, certain challenges exist in this area, including the high cost of RNA isolation and amplification kits, the need for skilled professionals to conduct molecular diagnostic assays and the complex nature of diagnostic procedures. These factors present significant barriers to implementing these methods in low-income regions [ 68 ]. The socio-economic limitations in these areas emphasize the need for innovative approaches to diagnostics that are cost-effective, require minimal training, and are suitable for point-of-care use. To address these challenges, research efforts must focus on the development of affordable, user-friendly, and portable diagnostic tools that can be easily deployed in areas with limited resources. One promising approach is the use of CRISPR-based diagnostics, such as SHERLOCK (Specific High-Sensitivity Enzymatic Reporter UnLOCKing), which can detect viral RNA with high specificity and sensitivity without the need for expensive equipment. This method uses a simple paper-based platform that can be adapted for use in remote and low-resource areas [ 69 ]. Additionally, isothermal amplification methods, such as LAMP (Loop-mediated Isothermal Amplification), are gaining attention as they eliminate the need for costly thermal cyclers and provide faster results. These methods are highly adaptable to point-of-care settings and can be utilized with simple, portable devices [ 70 ]. Furthermore, there is growing interest in leveraging mobile health (mHealth) solutions for diagnostics. Smartphone-based biosensors and diagnostic tools can significantly improve accessibility in remote areas. Recent advancements in mobile-based diagnostics allow the integration of portable sensors with smartphones, enabling real-time data processing and transmission for rapid diagnosis. These solutions could be a game-changer in areas with limited access to healthcare facilities [ 71 ]. The development of these simplified, affordable technologies could play a crucial role in improving the diagnosis and management of Bandavirus dabieense in underserved regions. By combining innovative diagnostic techniques with mobile health technology, we can enhance early detection, reduce diagnostic delays, and ultimately improve patient outcomes in regions with limited healthcare infrastructure. Research should continue to prioritize such low-cost, accessible solutions while addressing the socio-economic and infrastructural barriers that currently hinder the effective use of advanced diagnostics. An overview of the techniques employed by different researchers for detecting the Bandavirus dabieense virus is provided in Table 4 . Table 4. Overview of various diagnostic techniques used for Bandavirus dabieense detection Detection Approach Sample used Method/ Mechanism Reference Bandavirus dabieense Electron microscopy DH82 cells infected with Bandavirus dabieense Homogenized patients’ white blood cells inoculated into DH82 cell lines [ 37 ] Bandavirus dabieense genome Nucleic acid detection: RT-PCR Clinical samples: Blood/ serum/cerebrospinal fluid Targets nucleocapsid protein in Bandavirus dabieense genome [ 37 ] Bandavirus dabieense -specific IgM antibodies Immunological test: Nucleocapsid-based ELISA Serum/plasma * Bandavirus dabieense -specific IgM antibodies: 3 days post symptom onset * Bandavirus dabieense -specific IgG antibodies: 6 days post symptom onset [ 39 ] Bandavirus dabieense lateral flow immunoassay (LFIA) strip-based detection Clinical serum samples *Rapid nucleic acid amplification system *Catalytic hairpin assembly (CHA) in conjunction with a lateral flow immunoassay (LFIA) strip-based detection [ 38 ] Bandavirus dabieense CRISPR/Cas13a microfluidics-enabled digital platform Blood/Serum Quantitative detection of viral nucleic acid [ 64 ] Bandavirus dabieense RT-LAMP assay Serum *HB29 viruses grown in Vero cells * Bandavirus dabieense titer determined using focus-forming assay *Primers and a QProbe targeting a conserved region of the L segment of Bandavirus dabieense [ 63 ] Bandavirus dabieense RT-RPA Serum *Isothermal detection of Bandavirus dabieense RNA *Point of care detection: result in 15 min at 39 °C [ 61 ] Open in a new tab Available clinical treatments Bandavirus dabieense has been designated by the World Health Organization as a disease warranting urgent attention [ 45 ]. Unfortunately, there is currently a lack of effective treatments for Bandavirus dabieense . However, several conservative measures, such as maintaining hydration, administering antipyretic medications, providing transfusions, and utilizing inotropic agents, are recognized for their ability to alleviate symptoms like diarrhea, fever, bleeding, and shock. Patients with Bandavirus dabieense are often afflicted by sepsis, which can potentially lead to complications like multiorgan failure and even mortality. Therefore, early detection and prompt intervention are crucial in addressing this challenging health issue [ 72 ]. Available clinical treatment options for Bandavirus dabieense are presented in Fig. 5 . Fig. 5. Open in a new tab Available clinical treatment options for Bandavirus dabieense The use of Antivirals like Ribavirin, Favipiravir, Peramivir and Nitazoxanide has shown to have an inhibitory effect against Bandavirus dabieense replication thus helping in the treatment [ 73 ]. Steroid therapy on the other hand should be used with caution as it can lead to further complications in severe cases of Bandavirus dabieense . Hence, patients with mild conditions or Apache II scores less than 14 are suggested for this treatment [ 73 ]. APACHE II score is a comprehensive indicator of the severity of a patient’s illness, considering their present physiological metrics, age, and medical history. This scoring system aids in evaluating patients to gauge the appropriate extent of diagnostic and therapeutic interventions [ 74 ]. When interpreting the APACHE II score, it ranges from a minimum of 0 to a maximum of 71. A higher score is indicative of a greater risk of mortality during hospitalization. One of the key advantages of the APACHE II score is its applicability throughout a patient’s hospital stay, allowing for the continuous assessment of the patient’s response to treatment. In a retrospective multicenter clinical cohort study that focused on steroid treatment for Bandavirus dabieense management, the survival time was 18.4 days for the early steroidal group, however, the time increased to 22.4 and 27.3 days for the late steroidal group and non-steroidal group respectively which signifies the requirement of steroid treatment at an early stage of disease progression [ 75 ]. However, as patients typically receive more than just steroid therapy, it becomes challenging to precisely gauge the effectiveness of this treatment. Consequently, a prospective study is required to elucidate the specific contribution of steroid therapy in reducing mortality associated with Bandavirus dabieense . The use of intravenous immunoglobulin (IVIG) therapy is a valuable strategy for patients experiencing neurological complications in the context of Bandavirus dabieense . An insightful single-centre retrospective study conducted on a limited sample size suggested that administering IVIG at higher dosages for an extended duration may lead to improved prognoses. The study’s findings demonstrated a notable increase in white blood cell (WBC) and platelet counts following IVIG treatment, with statistical significance (p-value < 0.001), which signifies the positive impact of these parameters on individuals affected by Bandavirus dabieense with neurological complications [ 76 ]. A single-centre study in Korea explored the use of therapeutic plasma exchange (TPE) therapy as an adjunctive treatment for 10 patients afflicted with Bandavirus dabieense . These 10 patients exhibited diminished white blood cell (WBC) and platelet counts, as well as reduced serum albumin levels, but conversely had elevated levels of AST, ALT, LDH, and CK. The study findings revealed that when TPE therapy was administered over an average of three days with one to two-day intervals, there was a noticeable increase in WBC levels. However, platelet counts remained largely unchanged, while levels of AST, ALT, and LDH surged by a factor of 2 to 6 in patients who experienced recovery which proves the clinical significance of TPE therapy for Bandavirus dabieense management [ 77 ]. Challenges in early detection of Bandavirus dabieense Within the realm of tick-borne diseases, identifying Bandavirus dabieense and scrub typhus presents a unique challenge owing to their overlapping epidemiology, similar risk factors, and shared clinical symptoms. In South Korea, researchers have explored a novel approach that involves amplifying nucleic acids under isothermal conditions and subsequently detecting them using a bio-optical sensor, known as iNAD. They compared this approach with traditional RT-PCR and found that iNAD exhibited a sensitivity of 100% for detecting both Bandavirus dabieense and scrub typhus, in contrast to RT-PCR, which demonstrated sensitivities of 75% ( Bandavirus dabieense ) and 67% (scrub typhus). However, RT-PCR displayed higher clinical specificity (95% for both diseases) compared to iNAD. It’s worth noting that although the clinical specificity was higher with RT-PCR, the differences in values between iNAD and RT-PCR did not reach statistical significance. Furthermore, the current approach offered the advantage of faster results (20–30 min) and the ability to perform both DNA and RNA analyses under isothermal conditions, making it particularly advantageous [ 78 ]. Future research efforts aimed at developing sensitive and specific point-of-care detection kits for Bandavirus dabieense , which can be used under isothermal conditions at the household level, would greatly aid in addressing the challenges associated with Bandavirus dabieense disease management, particularly the issue of delayed diagnosis. Ensuring effective patient care necessitates the consistent and reliable delivery of results. It is of utmost importance that standardized detection methods for Bandavirus dabieense are consistently employed across all diagnostic laboratories. In the absence of such standardization, variations in test outcomes from the same samples could significantly undermine accuracy and dependability. To address this issue, the introduction of automation in detection methods offers several benefits, including increased efficiency, enhanced precision, cost reduction, decreased manual errors, and the capacity to process a substantial number of samples in a short timeframe, thereby aiding in the early detection of Bandavirus dabieense in samples [ 37 ]. Additionally, ongoing financial support is imperative for advancing research in innovative diagnostic approaches, such as next-generation sequencing and biomarker identification, to ensure sensitive detection of Bandavirus dabieense . Recognizing the importance of adopting a "one health" approach, which considers the health of humans, animals, and the environment, is crucial to preventing future pandemics and fostering the creation of a resilient society [ 79 ]. These comprehensive approaches collectively contribute to the effective containment and prevention of the spread of the Bandavirus dabieense virus. By harnessing the power of advanced algorithms and predictive modelling, ML can analyze a wide range of patient data, including clinical symptoms, vital signs, laboratory results, and demographic information, to identify patterns and risk factors associated with Bandavirus dabieense . These algorithms can learn from historical data, enabling them to recognize subtle indicators of the disease that might be missed by human clinicians. Moreover, ML can continuously monitor and analyze data in real-time, making it capable of flagging potential Bandavirus dabieense cases as soon as symptoms and risk factors manifest, facilitating early diagnosis and timely intervention. Applications of machine learning in diagnosing and managing Bandavirus dabieense In the field of diagnosing and managing severe fever with Thrombocytopenia, the applications of ML have yielded mixed results. This study meticulously evaluated the inclusion and exclusion criteria to ensure the quality and relevance of the datasets used for training and validation. The results demonstrated that ML techniques, such as classification algorithms and deep learning models, can effectively differentiate between various etiologies, including Bandavirus dabieense , malaria, scrub typhus, leptospirosis, bacterial sepsis, dengue, spotted fever, Q fever, typhoid fever, and more. However, it is essential to note that Bandavirus dabieense does not exhibit unique hallmark clinical signs or symptoms compared to other vector-borne tropical viral diseases. Thrombocytopenia and fever, for instance, are common to many arboviral diseases, which significantly challenges the predictive value of ML in distinguishing Bandavirus dabieense [ 80 ]. These algorithms exhibited a high degree of accuracy in identifying specific pathogens associated with Bandavirus dabieense , enabling early and accurate diagnoses. Despite this, the accuracy of ML models must be interpreted with caution due to the overlapping clinical presentations among similar diseases. ML models are often trained on specific datasets, which may not generalize well across different populations, further limiting their applicability. Moreover, the inclusion of demographic factors, such as age, gender, and geographical location, further improved the predictive power of these models. A modified regression ML model was devised in South Korea to enhance the accuracy of Bandavirus dabieense transmission estimation. This model integrated classification outcomes, particularly focusing on significant epidemiological and geographical factors such as climate, temperature, relative humidity (RH), and the number of mountain visitors. By considering these factors, the model successfully pinpointed high-risk areas for Bandavirus dabieense transmission. Consequently, it provided valuable insights for establishing guidelines that could help mountain visitors prevent Bandavirus dabieense transmission [ 81 ]. However, it is important to recognize that these models are inherently limited by the quality of the data used and cannot replace traditional epidemiological surveillance. They should instead be viewed as supplementary tools to aid public health efforts rather than definitive predictive instruments. Efforts have been made to employ ML techniques in identifying the worldwide locations with a high incidence of Bandavirus dabieense transmission. This analysis revealed that eight key factors contributed significantly to the effectiveness of the study: elevation, the likelihood of tick presence, two temperature-related variables, two precipitation-related variables, the presence of mammals in the area, and the percentage of water body coverage. Despite the apparent precision in identifying potential hotspots, the inherent variability in ecological and environmental factors makes it difficult to ensure consistent accuracy. Furthermore, changes in vector behavior, human movement, and other unforeseen factors further complicate predictive modeling efforts [ 82 ]. Additionally, the development of mobile health (mHealth) applications and wearable devices can facilitate real-time data collection, enabling early detection and intervention for patients at risk of severe complications. However, it is critical to acknowledge that the utility of these applications heavily depends on robust data-sharing frameworks, consistent data input, and widespread access to such technologies factors that remain challenging in resource-limited environments. Furthermore, the integration of genomics and proteomics data into ML models could enhance our understanding of host–pathogen interactions, leading to more personalized and effective treatment strategies. An overview of recent advances in ML for transmission prediction/ mortality prediction of Bandavirus dabieense is presented in Table 5 . Table 5. An overview of recent advances in machine learning for transmission prediction/ mortality prediction of Bandavirus dabieense Country No of patients Period Parameters Model Prediction Results Reference Jiangsu, China 327 2010–2022 *Clinical presentation *Demographic information *Laboratory parameters Reservoir computing with boosted topology (RC–BT) algorithm Encephalitis and mortality Model accuracy: 0.897 sensitivity:0.913 area under curve: 0.899 negative predictive value: 0.946 [ 83 ] Sichuan University, China 1354 2016–2018 *Acute physiology and chronic health evaluation *Sequential organ failure assessment Random forest (RF), gradient boosting (GB), logistic regression (LR), XGBoost, multi-layer perceptron (MLP), support vector machine (SVM), K-nearest neighbor (KNN) Hospital Acquired Thrombocytopenia after Operation in ICU Gradient boosting : AUC: 0.834, Sensitivity: 79.3%, Specificity: 73.7% Random forest : AUC: 0.828, Sensitivity: 73.85, Specificity: 79.1% [ 84 ] Shandong Province, China 416 2010–2013 *Clinical diagnosis criteria *Laboratory confirmation criteria *Ecological factors MaxEnt ecological niche model Potential risk areas of Bandavirus dabieense Case fatality rate: 13.52% AUC: 0.9 [ 85 ] South Korea 913 2016–2019 Epidemiological data Classification model in machine learning (CMML) modified regression model in machine learning (Modified-RMML) Severe fever with thrombocytopenia syndrome transmission Gradient boosting regression (GBR) model and Bootstrap truncated regression (BTR) model AUC: 0.959–0.991 [ 81 ] Open in a new tab Bold represent the name of the machine learning algorithm *This highlight key categories Future directions and recommendations for applying machine learning in diagnosing and managing Bandavirus dabieense Despite its current limitations, ML has significant potential to enhance the diagnosis, management, and prevention of Bandavirus dabieense infections. To fully unlock this potential, addressing key challenges is essential. One crucial aspect is the development of high-quality, diverse datasets. Collaborative efforts should focus on gathering data from different regions and healthcare systems to ensure models accurately represent various clinical presentations, ecological variables, and patient demographics. This approach will improve the generalizability and accuracy of ML models in predicting disease outcomes. However, a gap in the literature exists due to the scarcity of long-term studies on Bandavirus dabieense , which hinders the understanding of the long-term health outcomes for survivors and the effectiveness of control measures [ 86 ]. Future research should prioritize longitudinal studies that track patient outcomes over time, assessing disease progression, recovery, and long-term complications. ML techniques could be employed to analyze long-term patient data, offering valuable insights into disease management and the effectiveness of various control strategies. Incorporating multi-modal data is equally important. Clinical, laboratory, genomic, proteomic, and ecological data must be integrated into ML models to increase their predictive power. Genomic and proteomic data can provide valuable insights into host–pathogen interactions, while climate and vector behavior data can enhance predictions of disease transmission. By integrating such diverse data sources, ML models can more effectively differentiate Bandavirus dabieense from other febrile illnesses with overlapping symptoms, leading to more accurate diagnoses. However, a significant challenge lies in the fact that many arboviruses share common clinical features, such as fever and thrombocytopenia, which makes diagnosis based solely on symptoms highly challenging. Further complicating this is the possibility of co-infections with multiple arboviruses, which can extend the spectrum of symptoms and highly confound the diagnosis. These situations, where patients present with overlapping signs of two or more infections, can pose a serious challenge for machine learning models. In these special cases, traditional ML approaches may struggle to correctly identify the underlying pathogen(s), highlighting the limitations of using automated systems without expert clinical interpretation. Rather than solely depending on ML algorithms, combining traditional epidemiological methods with ML techniques offers a promising path forward. Hybrid models can use epidemiological surveillance as a foundation and apply ML for advanced pattern recognition and anomaly detection. This combined approach can enhance predictive accuracy while maintaining model interpretability, which is essential for clinicians making critical decisions about patient care. Integrating long-term surveillance data with ML can also contribute to a more holistic understanding of disease patterns and help identify trends that are not apparent in short-term studies. Another important area for future work involves improving the interpretability of ML models. Many current models operate as “black boxes,” producing predictions without clear explanations. To gain the trust of healthcare providers, future models should emphasize explainable AI (XAI) approaches that provide transparent reasoning for their predictions. This will allow clinicians to better understand and rely on the model’s outputs in diagnosing or managing Bandavirus dabieense . In regions with limited resources, scalability and cost-effectiveness are vital. ML models must be designed to function efficiently on existing technological infrastructure, using minimal computational power. Researchers should focus on developing lightweight models capable of delivering accurate results without requiring extensive hardware or data-processing capabilities. Ethical considerations must also play a central role in future research. Ensuring patient privacy, data security, and adherence to informed consent protocols is critical. Establishing robust data governance frameworks that prioritize transparency and patient rights will be essential. Techniques such as federated learning, which allows models to be trained on decentralized data sources without transferring sensitive information, can help balance the need for data diversity with privacy concerns. Given the scarcity of long-term studies, future research should emphasize longitudinal studies that track patient outcomes and disease progression over time to enhance predictive accuracy. Adaptive learning algorithms, which can update as new data becomes available, are critical to maintaining the relevance of ML models. Collaborative efforts involving clinicians, epidemiologists, data scientists, and public health experts will be necessary to develop practical and effective models. An important avenue for future research is the investigation of co-infections with multiple arboviruses. Co-infections can significantly complicate the clinical presentation of Bandavirus dabieense , as symptoms from other arboviruses may overlap or extend the disease spectrum, making the diagnosis more challenging. ML models must account for such complexities to improve their diagnostic accuracy in these special situations. Incorporating co-infection data, along with more comprehensive symptom profiles, into the training datasets will be essential to improve the reliability of ML-based predictions. The ability of models to distinguish between single infections and co-infections could be critical for more accurate diagnosis and treatment planning. The real-world performance of ML models should be evaluated through clinical trials and pilot studies to determine their utility across different environments. Privacy-preserving techniques, including federated learning, should be explored further to enhance data diversity while upholding ethical standards. By addressing these challenges, future research can harness the full potential of ML, ultimately improving patient outcomes and advancing public health strategies in combating Bandavirus dabieense . Role of elimination strategies in awareness creation Treatment for Bandavirus dabieense typically involves supportive care such as hydration, fever control, and pain management. Therefore, access to quality healthcare facilities with the necessary resources and expertise is essential for preventing complications and improving patient outcomes. In developing countries like India, offering comprehensive healthcare to patients requires public health tools for early disease prediction and mortality surveillance. However, achieving this goal faces systemic challenges in health informatics systems, such as integrating data effectively, strengthening leadership and governance for monitoring and surveillance, ensuring health financing options, and making long-term investments in integrated health systems. To lower the prevalence of Bandavirus dabieense in patients, it is important to educate people about the risk factors associated with Bandavirus dabieense , such as how to safeguard themselves from tick bites and when to seek medical help if symptoms arise. However, in regions with limited healthcare infrastructure, the effectiveness of awareness campaigns is often hindered by significant challenges. A shortage of trained healthcare professionals, poor health literacy, and limited access to reliable communication channels impede the widespread dissemination of critical information. These factors make it difficult to implement large-scale, effective awareness programs [ 87 ]. In recent years, significant progress has been made in the development of vaccines for Bandavirus dabieense . While no vaccine has yet been widely approved for use, several vaccine candidates, ranging from inactivated vaccines to mRNA-based formulations, are under investigation [ 88 ]. Inactivated vaccines, which use killed viruses to stimulate an immune response, have shown promising results in pre-clinical trials and are being explored in ongoing clinical studies. On the other hand, mRNA vaccines, which have gained global attention due to their success in COVID-19 prevention, are also being evaluated for Bandavirus dabieense . These newer vaccine technologies offer the potential for faster production, scalability, and adaptability to future strains [ 89 ]. The development and distribution of such vaccines could become one of the most effective tools in the global health response to Bandavirus dabieense , offering a preventive measure to reduce disease transmission and prevent outbreaks. Though many studies have focused on elimination strategies, major elimination efforts for Bandavirus dabieense and associated diseases have seen a setback due to systemic challenges and inadequate vaccine coverage and access [ 41 ]. Addressing these barriers requires a more inclusive approach to awareness campaigns. Solutions such as utilizing mobile health (mHealth) technologies, empowering community-based health workers, and collaborating with local governments and NGOs could create more cost-effective and sustainable awareness programs tailored to resource-constrained locations [ 90 ]. mHealth technologies, in particular, offer a promising avenue to disseminate critical health information, enabling the use of mobile phones and digital platforms to reach remote communities. Additionally, training community health workers and strengthening local government involvement can enhance the spread of awareness at the grassroots level, increasing engagement and providing more reliable communication channels. Therefore, there is a crucial need for solid evidence bases for accurate rapid diagnostics developed using cost-effective algorithms to provide better healthcare [ 42 ]. Furthermore, alongside rapid diagnostic solutions, integrating vaccines as part of comprehensive disease control strategies will be essential. Vaccines play a critical role in reducing the burden of infectious diseases, and their inclusion in the public health armamentarium for Bandavirus dabieense would be pivotal in curbing its spread and preventing severe outcomes. This could be achieved by strengthening partnerships between technology developers and public health experts to provide robust healthcare solutions that are critical in reducing mortality and saving lives. A brief overview of various strategies and the importance of collaborative partnerships between technology developers and public health experts in building robust healthcare solutions are shown in Fig. 6 . Fig. 6. Open in a new tab Overview of elimination strategies and collaborative partnerships in building robust healthcare solutions Conclusion Bandavirus dabieense presents a significant global health concern due to its potential for endemic and pandemic transmission through various routes. This neglected tick-borne viral infection has shown its capacity to exploit multiple avenues of transmission, including ticks to humans, animals to humans, and even human-to-human spread. The consequences of Bandavirus dabieense are severe, often resulting in fever and dangerously low platelet counts, which can lead to life-threatening complications, such as multi-organ failure, sepsis, and encephalitis, with a global case fatality ratio of 20–30%. This review has provided a comprehensive insight into the epidemiology and global prevalence of Bandavirus dabieense , emphasizing the critical role played by geographical and demographic factors in its transmission dynamics. Furthermore, we have discussed advanced diagnostic methods, available clinical treatments, and the challenges associated with early detection of Bandavirus dabieense . ML techniques have been highlighted as a promising avenue for improving diagnosis and management while emphasizing the importance of awareness campaigns and elimination strategies. Our review serves as a valuable resource for healthcare professionals, researchers, and policymakers, offering insights that can aid in the development of early detection strategies and the optimization of patient care. In the face of Bandavirus dabieense potential to cause significant morbidity and mortality, our review not only highlights the existing challenges but also offers a glimpse into the promising future of Bandavirus dabieense management with the integration of ML techniques. Together, these insights can contribute to more effective strategies for combating Bandavirus dabieense and improving patient outcomes. Funding There was no source of funding for this work. Data availability Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. Declarations Conflict of interest The authors declare that there is no conflict of interest. Ethics approval Not Applicable. Consent to publish images The authors have a subscription to the Biorender tool and have permission to publish in the Journal. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. He Z, Wang B, Li Y, Du Y, Ma H, Li X, et al. Severe fever with thrombocytopenia syndrome: a systematic review and meta-analysis of epidemiology, clinical signs, routine laboratory diagnosis, risk factors, and outcomes. BMC Infect Dis [Internet]. 2020 Aug 5 [cited 2022 Oct 17];20(1):1–12. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05303-0 [ DOI ] [ PMC free article ] [ PubMed ] 2. Liu W, Dai K, Wang T, Zhang H, Wu J, Liu W, et al. Severe fever with thrombocytopenia syndrome incidence could be associated with ecotone between forest and cultivated land in rural settings of central China. Ticks Tick Borne Dis. 2023;14(2): 102085. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Fang L, Yu S, Tian X, Fu W, Su L, Chen Z, et al. Severe fever with thrombocytopenia syndrome virus replicates in platelets and enhances platelet activation. J Thromb Haemost. 2023;21(5):1336–51. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Lee K, Seok JH, Kim H, Park S, Lee S, Bae J-Y, et al. Genome-informed investigation of the molecular evolution and genetic reassortment of severe fever with thrombocytopenia syndrome virus. PLoS Negl Trop Dis [Internet]. 2023 Sep 15 [cited 2023 Sep 29];17(9):e0011630. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0011630 [ DOI ] [ PMC free article ] [ PubMed ] 5. Liu Z, Zhang R, Zhou W, Ma R, Han L, Zhao Z, et al. High levels of C-reactive protein-to-albumin ratio (CAR) are associated with a poor prognosis in patients with severe fever with thrombocytopenia syndrome in early stage. J Med Virol [Internet]. 2022 Nov 1 [cited 2023 Sep 22];94(11):5375–84. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/jmv.27972 [ DOI ] [ PMC free article ] [ PubMed ] 6. Xie J, Su M, Dang Y, Zhao L. Prognostic value of serum ferritin for patients with severe fever with thrombocytopenia syndrome: a single-center retrospective cohort study. Infect Dis Ther [Internet]. 2023 Mar 1 [cited 2023 Sep 22];12(3):979–88. Available from: https://link.springer.com/article/10.1007/s40121-023-00784-3 [ DOI ] [ PMC free article ] [ PubMed ] 7. Li J, Li S, Yang L, Cao P, Lu J. Severe fever with thrombocytopenia syndrome virus: a highly lethal bunyavirus. Crit Rev Microbiol [Internet]. 2021 [cited 2023 Oct 25];47(1):112–25. Available from: https://www.tandfonline.com/doi/abs/10.1080/1040841X.2020.1847037 [ DOI ] [ PubMed ] 8. Park SW, Ryou J, Choi WY, Han MG, Lee WJ. Epidemiological and Clinical Features of Severe Fever with Thrombocytopenia Syndrome during an Outbreak in South Korea, 2013–2015. Am J Trop Med Hyg [Internet]. 2016 Dec 12 [cited 2023 Oct 25];95(6):1358. Available from: /pmc/articles/PMC5154450/ [ DOI ] [ PMC free article ] [ PubMed ] 9. Wang B, He Z, Yi Z, Yuan C, Suo W, Pei S, et al. Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome. PLoS One [Internet]. 2021 Jul 1 [cited 2023 Oct 25];16(7):e0255033. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255033 [ DOI ] [ PMC free article ] [ PubMed ] 10. Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev [Internet]. 2022 Jun 1 [cited 2023 Oct 31];18(2):e1230. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/cl2.1230 [ DOI ] [ PMC free article ] [ PubMed ] 11. Liu Y, Guo L, Wang G, Gao F, Tu Z, Xu D, et al. DNA virome of ticks in the Northeast and Hubei provinces of China reveals diverse single-stranded circular DNA viruses. Parasites and Vectors [Internet]. 2023 Dec 1 [cited 2023 Sep 29];16(1):1–8. Available from: https://link.springer.com/articles/10.1186/s13071-023-05684-6 [ DOI ] [ PMC free article ] [ PubMed ] 12. Bai Y, Li Y, Liu W, Li J, Tian F, Liu L, et al. Analysis of the diversity of tick-borne viruses at the border areas in Liaoning Province, China. Front Microbiol. 2023;2(14):1179156. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Li J, Huang X, Ding F, Li Y, Li D, Li Q. Epidemiological Progress of Severe Fever with Thrombocytopenia Syndrome. Prog China Epidemiol Vol 1 [Internet]. 2023 Jan 1 [cited 2023 Sep 29];1:139–50. Available from: https://link.springer.com/chapter/10.1007/978-981-19-2199-5_7 14. Kuan CY, Lin TL, Ou SC, Chuang S Te, Chan JPW, Maeda K, et al. The First Nationwide Surveillance of Severe Fever with Thrombocytopenia Syndrome in Ruminants and Wildlife in Taiwan. Viruses 2023, Vol 15, Page 441 [Internet]. 2023 Feb 5 [cited 2023 Sep 29];15(2):441. Available from: https://www.mdpi.com/1999-4915/15/2/441/htm [ DOI ] [ PMC free article ] [ PubMed ] 15. Shah T, Li Q, Wang B, Baloch Z, Xia X. Geographical distribution and pathogenesis of ticks and tick-borne viral diseases. Front Microbiol. 2023;24(14):1185829. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Xiong S, Zhang W, Li M, Xiong Y, Li M, Wang H, et al. A simple and practical score model for predicting the mortality of severe fever with thrombocytopenia syndrome patients. Medicine (Baltimore) [Internet]. 2016 [cited 2022 Oct 24];95(52). Available from: /pmc/articles/PMC5207567/ [ DOI ] [ PMC free article ] [ PubMed ] 17. Wang L, Wan G, Shen Y, Zhao Z, Lin L, Zhang W, et al. A nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome at the early stage—A multicenter study in China. PLoS Negl Trop Dis [Internet]. 2019 [cited 2022 Oct 24];13(11):e0007829. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0007829 [ DOI ] [ PMC free article ] [ PubMed ] 18. Jia B, Yan X, Chen Y, Wang G, Liu Y, Xu B, et al. A scoring model for predicting prognosis of patients with severe fever with thrombocytopenia syndrome. PLoS Negl Trop Dis [Internet]. 2017 Sep 21 [cited 2022 Oct 24];11(9):e0005909. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005909 [ DOI ] [ PMC free article ] [ PubMed ] 19. Zhang Y, Zhang Y, Xu Y, Huang Y. The associations between fasting blood glucose levels and mortality of SFTS in patients. BMC Infect Dis [Internet]. 2021 Dec 1 [cited 2022 Oct 24];21(1):1–8. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06463-3 [ DOI ] [ PMC free article ] [ PubMed ] 20. Wang L, Zou Z, Ding K, Hou C. Predictive risk score model for severe fever with thrombocytopenia syndrome mortality based on qSOFA and SIRS scoring system. BMC Infect Dis [Internet]. 2020 Aug 12 [cited 2022 Oct 24];20(1):1–9. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05299-7 [ DOI ] [ PMC free article ] [ PubMed ] 21. Qian J, Wei J, Ren L, Liu Y, Feng L. Sex differences in incidence and fatality of severe fever with thrombocytopenia syndrome: A comparative study based on national surveillance data of China. J Med Virol [Internet]. 2023 Mar 1 [cited 2023 Sep 29];95(3):e28632. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/jmv.28632 [ DOI ] [ PubMed ] 22. N. MF, B. KB, Valliyot B, Balakrishnan S. Study of acute febrile illness with thrombocytopenia in a tertiary care centre. Int J Res Med Sci [Internet]. 2018 Jan 24 [cited 2022 Oct 17];6(2):455–8. Available from: https://www.msjonline.org/index.php/ijrms/article/view/4451 23. Bouchard C, Dibernardo A, Koffi J, Wood H, Leighton PA, Lindsay LR. Climate change and infectious diseases: the challenges: N increased risk of tick-borne diseases with climate and environmental changes. Can Commun Dis Rep. 2019;45(4):83. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Jang H, Casel MA, Jang SG, Choi JH, Gil J, Rollon R, Cheun SY, Kim YI, Song MS, Choi YK. Seasonal dynamics of Haemaphysalis tick species as SFTSV vectors in South Korea. Microbiology Spectrum. 2024;30:e00489-e524. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Bardosh KL, Ryan SJ, Ebi K, Welburn S, Singer B. Addressing vulnerability, building resilience: community-based adaptation to vector-borne diseases in the context of global change. Infect Dis Poverty. 2017;6:1–21. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Sun J, Lu L, Wu H, Yang J, Ren J, Liu Q. The changing epidemiological characteristics of severe fever with thrombocytopenia syndrome in China, 2011–2016. Sci Reports 2017 71 [Internet]. 2017 Aug 23 [cited 2023 Oct 7];7(1):1–7. Available from: https://www.nature.com/articles/s41598-017-08042-6 [ DOI ] [ PMC free article ] [ PubMed ] 27. Wang L, Liu Y, Yu H, Ding K, Zou Z. Low serum free triiodothyronineis level predicts worse outcome of patients with severe fever with thrombocytopenia syndrome. BMC Infect Dis [Internet]. 2022 Dec 1 [cited 2023 Oct 16];22(1):1–8. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07367-6 [ DOI ] [ PMC free article ] [ PubMed ] 28. Zhao J, Bin LuQ, Li H, Yuan Y, Cui N, Yuan C, et al. Sex differences in case fatality rate of patients with severe fever with thrombocytopenia syndrome. Front Microbiol. 2021;12:738808. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Lyu Y, Hu CY, Sun L, Qin W, Xu PP, Sun J, et al. Impact of an intervention programme on knowledge, attitudes and practices of population regarding severe fever with thrombocytopenia syndrome in endemic areas of Lu’an, China. Epidemiol Infect [Internet]. 2018 Jan 1 [cited 2023 Oct 20];146(1):125–36. Available from: https://www.cambridge.org/core/journals/epidemiology-and-infection/article/impact-of-an-intervention-programme-on-knowledge-attitudes-and-practices-of-population-regarding-severe-fever-with-thrombocytopenia-syndrome-in-endemic-areas-of-luan-china/F3F2E93234749B290AE479FD0AA5A7C0 [ DOI ] [ PMC free article ] [ PubMed ] 30. Dai ZN, Peng XF, Li JC, Zhao J, Wu YX, Yang X, et al. Effect of genomic variations in severe fever with thrombocytopenia syndrome virus on the disease lethality. Emerg Microbes Infect [Internet]. 2022 Dec 31 [cited 2023 Sep 26];11(1):1672–82. Available from: https://www.tandfonline.com/doi/abs/10.1080/22221751.2022.2081617 [ DOI ] [ PMC free article ] [ PubMed ] 31. Nakada N, Yamamoto K, Tanaka M, Ashizawa H, Yoshida M, Umemura A, et al. Clinical Differentiation of Severe Fever with Thrombocytopenia Syndrome from Japanese Spotted Fever. Viruses [Internet]. 2022 Aug 1 [cited 2023 Sep 26];14(8):1807. Available from: https://www.mdpi.com/1999-4915/14/8/1807/htm [ DOI ] [ PMC free article ] [ PubMed ] 32. Zu Z, Hu Y, Zheng X, Chen C, Zhao Y, Jin Y, et al. A ten-year assessment of the epidemiological features and fatal risk factors of hospitalised severe fever with thrombocytopenia syndrome in Eastern China. Epidemiol Infect [Internet]. 2022 Jun 21 [cited 2023 Sep 26];150:e131. Available from: https://www.cambridge.org/core/journals/epidemiology-and-infection/article/tenyear-assessment-of-the-epidemiological-features-and-fatal-risk-factors-of-hospitalised-severe-fever-with-thrombocytopenia-syndrome-in-eastern-china/5E54719E0C2EAFDC1F163132BBE14F96 [ DOI ] [ PMC free article ] [ PubMed ] 33. Shuto H, Komiya K, Usagawa Y, Yamasue M, Fushimi K, Hiramatsu K, et al. Corticosteroid Therapy for Patients With Severe Fever With Thrombocytopenia Syndrome: A Nationwide Propensity Score–Matched Study in Japan. Open Forum Infect Dis [Internet]. 2023 Aug 1 [cited 2023 Sep 26];10(8). Available from: 10.1093/ofid/ofad418 [ DOI ] [ PMC free article ] [ PubMed ] 34. Kim JY, Yoo JR, Kim M, Oh H, Heo ST. Epidemiologic and Clinical Characteristics of Patients with Severe Fever with Thrombocytopenia Syndrome at Tertiary Hospital in Jeju for 10 years. Infect Chemother [Internet]. 2023 Sep 1 [cited 2023 Sep 26];55(3):377–87. Available from: 10.3947/ic.2023.0035 [ DOI ] [ PMC free article ] [ PubMed ] 35. Kobayashi Y, Kato H, Yamagishi T, Shimada T, Matsui T, Yoshikawa T, et al. Severe Fever with Thrombocytopenia Syndrome, Japan, 2013–2017. Emerg Infect Dis [Internet]. 2020 Apr 1 [cited 2024 Jan 23];26(4):692. Available from: /pmc/articles/PMC7101122/ [ DOI ] [ PMC free article ] [ PubMed ] 36. Chen H, Hu K, Zou J, Xiao J. A cluster of cases of human-to-human transmission caused by severe fever with thrombocytopenia syndrome bunyavirus. Int J Infect Dis. 2013;17(3):e206–8. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Ai L, Wang W, Teng Z. Advancements in the Worldwide Detection of Severe Fever with Thrombocytopenia Syndrome Virus Infection from 2009 to 2023. China CDC Wkly [Internet]. 2023 Aug 8 [cited 2023 Sep 15];5(31):687. Available from: /pmc/articles/PMC10427339/ [ DOI ] [ PMC free article ] [ PubMed ] 38. Chen L, Ma M, Zou M, Zhao L, Ou M, Geng Y, et al. Rapid and portable bunyavirus SFTSV RNA testing utilizing catalytic hairpin assembly coupled with lateral flow immunoassay. Long SW, editor. Microbiol Spectr [Internet]. 2023 Sep 8 [cited 2023 Sep 21]; Available from: https://journals.asm.org/doi/10.1128/spectrum.02144-23 [ DOI ] [ PMC free article ] [ PubMed ] 39. Tran XC, Kim SH, Lee J-E, Kim S-H, Kang SY, Binh ND, et al. Serological Evidence of Severe Fever with Thrombocytopenia Syndrome Virus and IgM Positivity Were Identified in Healthy Residents in Vietnam. mdpi.comXC Tran, SH Kim, JE Lee, SH Kim, SY Kang, ND Binh, PV Duc, PTK Phuong, NTP ThaoViruses, 2022•mdpi.com [Internet]. 2022 [cited 2023 Sep 15]; Available from: https://www.mdpi.com/1999-4915/14/10/2280 [ DOI ] [ PMC free article ] [ PubMed ] 40. Chen C, Li P, Li KF, Wang HL, Dai YX, Cheng X, et al. Animals as amplification hosts in the spread of severe fever with thrombocytopenia syndrome virus: a systematic review and meta-analysis. Int J Infect Dis. 2019;1(79):77–84. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Rim JM, Han SW, Cho YK, Kang JG, Choi KS, Chae JS. Serologic and molecular prevalence of severe fever with thrombocytopenia syndrome virus among poultry in the Republic of Korea. https://home.liebertpub.com/vbz [Internet]. 2023 Nov 28 [cited 2024 Jan 23];23(12):662–9. Available from: https://www.liebertpub.com/doi/10.1089/vbz.2023.0028 [ DOI ] [ PubMed ] 42. Kaneko C, Mekata H, Umeki K, Sudaryatma PE, Irie T, Yamada K, et al. Seroprevalence of severe fever with thrombocytopenia syndrome virus in medium-sized wild mammals in Miyazaki, Japan. Ticks Tick Borne Dis. 2023;14(2): 102115. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Fukushi S. Seroprevalence and risk factors of severe fever with thrombocytopenia syndrome. Sev Fever with Thrombocytopenia Syndr [Internet]. 2019 Jan 1 [cited 2023 Oct 20];109–20. Available from: https://link.springer.com/chapter/10.1007/978-981-13-9562-8_10 44. Hu L, Li J, Zhang H, Bian T, Pan J, Xu X, et al. Predisposing factors for person-to-person transmission of severe fever with thrombocytopenia syndrome bunyavirus. J Hosp Infect. 2022;1(123):174–8. [ DOI ] [ PubMed ] [ Google Scholar ] 45. Li JC, Zhao J, Li H, Fang LQ, Liu W. Epidemiology, clinical characteristics, and treatment of severe fever with thrombocytopenia syndrome. Infect Med. 2022;1(1):40–9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Fang LZ, Xiao X, Lei SC, Liu JW, Yu XJ. Haemaphysalis flava ticks as a competent vector of severe fever with thrombocytopenia syndrome virus. Ticks Tick Borne Dis. 2023;14(2): 102100. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Yun SM, Song BG, Choi W, Roh JY, Lee YJ, Park W Il, et al. First Isolation of Severe Fever with Thrombocytopenia Syndrome Virus from Haemaphysalis longicornis Ticks Collected in Severe Fever with Thrombocytopenia Syndrome Outbreak Areas in the Republic of Korea. Vector-Borne Zoonotic Dis [Internet]. 2016 Jan 1 [cited 2023 Oct 20];16(1):66–70. Available from: https://www.liebertpub.com/doi/10.1089/vbz.2015.1832 [ DOI ] [ PMC free article ] [ PubMed ] 48. Yuan C, Zou Z. Infection and transovarial transmission of severe fever with thrombocytopenia syndrome virus in Rhipicephalus sanguineus on Hainan Island, China. 2023 [cited 2023 Oct 20]; Available from: https://onlinelibrary.wiley.com/doi/10.1111/1749-4877.12716 [ DOI ] [ PubMed ] 49. Meng K, Sun W, Cheng Z, Guo H, Liu J, Chai T. First detection of severe fever with thrombocytopenia syndrome virus in the tick species Haemaphysalis concinna in Shandong Province, China. Parasitol Res [Internet]. 2015 Dec 1 [cited 2023 Oct 20];114(12):4703–7. Available from: https://link.springer.com/article/10.1007/s00436-015-4718-2 [ DOI ] [ PubMed ] 50. Wang G, Tian X, Peng R, Huang Y, Li Y, Li Z, et al. Genomic and phylogenetic profiling of RNA of tick-borne arboviruses in Hainan Island. China Microbes Infect. 2023;14: 105218. [ DOI ] [ PubMed ] [ Google Scholar ] 51. Nakao R, Shinjo K, Sakiyama T, Ogata S, Kusakisako K, Kinoshita G, et al. Amblyomma testudinarium infestation on a brown bear (Ursus arctos yesoensis) captured in Hokkaido, a northern island of Japan. Parasitol Int. 2021;1(80): 102209. [ DOI ] [ PubMed ] [ Google Scholar ] 52. Zhu L, Yin F, Moming A, Zhang J, Wang B, Gao L, et al. First case of laboratory-confirmed severe fever with thrombocytopenia syndrome disease revealed the risk of SFTSV infection in Xinjiang, China. Emerg Microbes Infect [Internet]. 2019 Jan 1 [cited 2023 Oct 20];8(1):1122–5. Available from: https://www.tandfonline.com/doi/abs/10.1080/22221751.2019.1645573 [ DOI ] [ PMC free article ] [ PubMed ] 53. Sato Y, Mekata H, Sudaryatma PE, Kirino Y, Yamamoto S, Ando S, et al. Isolation of Severe Fever with Thrombocytopenia Syndrome Virus from Various Tick Species in Area with Human Severe Fever with Thrombocytopenia Syndrome Cases. https://home.liebertpub.com/vbz [Internet]. 2021 Apr 27 [cited 2023 Oct 20];21(5):378–84. Available from: https://www.liebertpub.com/doi/10.1089/vbz.2020.2720 [ DOI ] [ PubMed ] 54. Yu X jie. Risk factors for death in severe fever with thrombocytopenia syndrome. Lancet Infect Dis [Internet]. 2018 Oct 1 [cited 2023 Oct 25];18(10):1056–7. Available from: http://www.thelancet.com/article/S1473309918303128/fulltext [ DOI ] [ PubMed ] 55. Choi SJ, Park SW, Bae IG, Kim SH, Ryu SY, Kim HA, et al. Severe Fever with Thrombocytopenia Syndrome in South Korea, 2013–2015. PLoS Negl Trop Dis [Internet]. 2016 Dec 29 [cited 2023 Oct 25];10(12):e0005264. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005264 [ DOI ] [ PMC free article ] [ PubMed ] 56. Tran XC, Yun Y, An L Van, Kim SH, Thao NTP, Man PKC, et al. Endemic Severe Fever with Thrombocytopenia Syndrome, Vietnam. Emerg Infect Dis [Internet]. 2019 May 1 [cited 2023 Oct 25];25(5):1029. Available from: /pmc/articles/PMC6478219/ [ DOI ] [ PMC free article ] [ PubMed ] 57. Wilson AL, Courtenay O, Kelly-Hope LA, Scott TW, Takken W, Torr SJ, Lindsay SW. The importance of vector control for the control and elimination of vector-borne diseases. PLoS Negl Trop Dis. 2020;14(1): e0007831. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Eisen L. Personal protection measures to prevent tick bites in the United States: knowledge gaps, challenges, and opportunities. Ticks Tick-Borne Dis. 2022;13(4): 101944. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. De Meneghi D, Stachurski F, Adakal H. Experiences in tick control by acaricide in the traditional cattle sector in Zambia and Burkina Faso: possible environmental and public health implications. Front Public Health. 2016;9(4):239. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Hewson R. Understanding viral haemorrhagic fevers: virus diversity, vector ecology, and public health strategies. Pathogens. 2024;13(10):909. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Zhou J, Wang Q, Zhu L, Li S, Li W, Fu Y, et al. Development and evaluation of a rapid detection assay for severe fever with thrombocytopenia syndrome virus based on reverse-transcription recombinase polymerase amplification. Mol Cell Probes. 2020;1(52): 101580. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Huang M, Liu S, Xu Y, Li A, Wu W, Liang M, et al. CRISPR/Cas12a technology combined with RPA for rapid and portable SFTSV detection. Front Microbiol. 2022;25(13): 754995. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Sano S, Fukushi S, Yamada S, Harada S, Kinoshita H, Sugimoto S, et al. Development of an RT-LAMP Assay for the Rapid Detection of SFTS Virus. Viruses [Internet]. 2021 Apr 1 [cited 2023 Oct 23];13(4). Available from: https://pubmed.ncbi.nlm.nih.gov/33923720/ [ DOI ] [ PMC free article ] [ PubMed ] 64. Zuo L, Miao J, He D, Fang Z, Zhang X, Sun C, et al. Development and characterization of a digital CRISPR/Cas13a based assay for rapid and sensitive diagnosis of severe fever with thrombocytopenia syndrome virus. Sens Actuators B Chem. 2023;1(388): 133789. [ Google Scholar ] 65. Sawabe K, Wangdi T, Somboon P, Khamsing V, Iwagami M, Tan SH, Igori K, Pandey BD, Morita K, Sun J, Faizah AN. Special Topics from Asian Countries. InMedical Entomology in Asia 2024 Aug 9 (pp. 369–494). Singapore: Springer Nature Singapore. 66. Chakraborty S. Democratizing nucleic acid-based molecular diagnostic tests for infectious diseases at resource-limited settings–from point of care to extreme point of care. Sens Diagn. 2024;3(4):536–61. [ Google Scholar ] 67. Kwon JS, Kim JY, Jang CY, Son JY, Kim W, Kim T, Park SY, Kim MC, Park SY, Cha HH, Jang HM. Effect of Severe Fever With Thrombocytopenia Syndrome Virus Genotype on Disease Severity, Viral Load, and Cytokines in South Korea. InOpen Forum Infectious Diseases 2024 Sep (Vol. 11, No. 9, p. ofae508). US: Oxford University Press. [ DOI ] [ PMC free article ] [ PubMed ] 68. De Paz HD, Brotons P, Muñoz-Almagro C. Molecular isothermal techniques for combating infectious diseases: towards low-cost point-of-care diagnostics. Expert Rev Mol Diagn. 2014;14(7):827–43. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Zahra A, Shahid A, Shamim A, Khan SH, Arshad MI. The SHERLOCK platform: an insight into advances in viral disease diagnosis. Mol Biotechnol. 2023;65(5):699–714. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Soroka M, Wasowicz B, Rymaszewska A. Loop-mediated isothermal amplification (LAMP): the better sibling of PCR? Cells. 2021;10(8):1931. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Anikwe CV, Nweke HF, Ikegwu AC, Egwuonwu CA, Onu FU, Alo UR, Teh YW. Mobile and wearable sensors for data-driven health monitoring system: state-of-the-art and future prospect. Expert Syst Appl. 2022;15(202): 117362. [ Google Scholar ] 72. Yokomizo K, Tomozane M, Sano C, Ohta R. Clinical presentation and mortality of severe fever with thrombocytopenia syndrome in Japan: a systematic review of case reports. Int J Environ Res Public Heal 2022, Vol 19, p. 2271 [Internet]. 2022 Feb 17 [cited 2023 Oct 27];19(4):2271. Available from: https://www.mdpi.com/1660-4601/19/4/2271/htm [ DOI ] [ PMC free article ] [ PubMed ] 73. Seo JW, Kim D, Yun N, Kim DM. Clinical update of severe fever with thrombocytopenia syndrome. Viruses 2021, Vol 13, p. 1213 [Internet]. 2021 Jun 23 [cited 2023 Oct 27];13(7):1213. Available from: https://www.mdpi.com/1999-4915/13/7/1213/htm [ DOI ] [ PMC free article ] [ PubMed ] 74. Xu Y, Yang X, Qu C, Jia J. Clinical characteristics of severe fever with thrombocytopenia syndrome (SFTS) sepsis and non-SFTS sepsis. Mol Immunol. 2023;1(164):1–6. [ DOI ] [ PubMed ] [ Google Scholar ] 75. Jung SI, Kim YE, Yun NR, Kim CM, Kim DM, Han MA, et al. Effects of steroid therapy in patients with severe fever with Thrombocytopenia syndrome: A multicenter clinical cohort study. PLoS Negl Trop Dis [Internet]. 2021 Feb 1 [cited 2023 Oct 27];15(2). Available from: /pmc/articles/PMC7928499/ [ DOI ] [ PMC free article ] [ PubMed ] 76. Liu Y, Tong H, He F, Zhai Y, Wu C, Wang J, et al. Effect of intravenous immunoglobulin therapy on the prognosis of patients with severe fever with thrombocytopenia syndrome and neurological complications. Front Immunol. 2023;22(14):1118039. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. Yoo E-H, Lee A-J, Kim S-G, Jeon C-H. Therapeutic plasma exchange in patients with severe fever with thrombocytopenia syndrome: a single institution experience. Korean J Blood Transfus [Internet]. 2021 Aug 31 [cited 2023 Oct 27];32(2):83–90. Available from: https://www.kjbt.org/journal/view.html?doi=10.17945/kjbt.2021.32.2.83 78. Kim JY, Koo B, Jin CE, Kim MC, Chong YP, Lee SO, et al. Rapid diagnosis of tick-borne illnesses by use of one-step isothermal nucleic acid amplification and bio-optical sensor detection. Clin Chem [Internet]. 2018 Mar 1 [cited 2023 Oct 25];64(3):556–65. Available from: 10.1373/clinchem.2017.280230 [ DOI ] [ PubMed ] 79. Andoh K, Hidano A, Sakamoto Y, Sawai K, Arai N, Suda Y, et al. Current research and future directions for realizing the ideal one-health approach: a summary of key-informant interviews in Japan and a literature review. One Heal. 2023;1(16): 100468. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Li J, Han Y, Xing Y, Li S, Kong L, Zhang Y, Zhang L, Liu N, Wang Q, Wang S, Lu S. Concurrent measurement of dynamic changes in viral load, serum enzymes, T cell subsets, and cytokines in patients with severe fever with thrombocytopenia syndrome. PLoS ONE. 2014;9(3): e91679. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 81. Cho G, Lee S, Lee H. Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea. Sci Reports 2021 111 [Internet]. 2021 Nov 8 [cited 2023 Oct 26];11(1):1–10. Available from: https://www.nature.com/articles/s41598-021-01361-9 [ DOI ] [ PMC free article ] [ PubMed ] 82. Miao D, Dai K, Zhao GP, Li X Lou, Shi WQ, Zhang JS, et al. Mapping the global potential transmission hotspots for severe fever with thrombocytopenia syndrome by machine learning methods. Emerg Microbes Infect [Internet]. 2020 [cited 2023 Oct 26];9(1):817–26. Available from: https://www.tandfonline.com/doi/abs/10.1080/22221751.2020.1748521 [ DOI ] [ PMC free article ] [ PubMed ] 83. Zheng H, Geng Y, Gu C, Li M, Mao M, Wan Y, et al. A reservoir computing with boosted topology model to predict encephalitis and mortality for patients with severe fever with thrombocytopenia syndrome: a retrospective multicenter study. Infect Dis Ther [Internet]. 2023 May 1 [cited 2023 Oct 26];12(5):1379–91. Available from: https://link.springer.com/article/10.1007/s40121-023-00808-y [ DOI ] [ PMC free article ] [ PubMed ] 84. Cheng Y, Chen C, Yang J, Yang H, Fu M, Zhong X, et al. Using machine learning algorithms to predict hospital acquired thrombocytopenia after operation in the intensive care unit: A retrospective cohort study. Diagnostics [Internet]. 2021 Sep 1 [cited 2023 Oct 27];11(9):1614. Available from: https://www.mdpi.com/2075-4418/11/9/1614/htm [ DOI ] [ PMC free article ] [ PubMed ] 85. Du Z, Wang Z, Liu Y, Wang H, Xue F, Liu Y. Ecological niche modeling for predicting the potential risk areas of severe fever with thrombocytopenia syndrome. Int J Infect Dis [Internet]. 2014 Sep 1 [cited 2023 Oct 27];26:1–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1201971214015008 [ DOI ] [ PubMed ] 86. Hu L, Kong Q, Liu Y, Li J, Bian T, Ma X, Ye Y, Li J. Time course of severe fever with thrombocytopenia syndrome virus and antibodies in patients by long-term follow-up study, China. Front Microbiol. 2021;12(12): 744037. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 87. Raadsen M, Du Toit J, Langerak T, van Bussel B, van Gorp E, Goeijenbier M. Thrombocytopenia in virus infections. J Clin Med. 2021;10(4):877. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 88. Kowalzik F, Schreiner D, Jensen C, Teschner D, Gehring S, Zepp F. mRNA-based vaccines. Vaccines. 2021;9(4):390. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 89. Kim JY, Jeon K, Hong JJ, Park SI, Cho H, Park HJ, Kwak HW, Park HJ, Bang YJ, Lee YS, Bae SH. Heterologous vaccination utilizing viral vector and protein platforms confers complete protection against SFTSV. Sci Rep. 2023;13(1):8189. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 90. Khatri RB, Endalamaw A, Erku D, Wolka E, Nigatu F, Zewdie A, Assefa Y. Enablers and barriers of community health programs for improved equity and universal coverage of primary health care services: a scoping review. BMC Prim Care. 2024;25(1):385. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. Articles from VirusDisease are provided here courtesy of Springer ACTIONS View on publisher site PDF (1.6 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Explainable machine learning-based preliminary screening for viral encephalitis by blood routine analysis.#345232
• Blood Collection Study From COVID-19 Convalescents Previously Hospitalized to Identify Immunogenic Viral Epitopes#875824
• Genomic and phylogenetic characterization of severe fever with thrombocytopenia syndrome virus in companion animals in Korea, 2023–2024#258671
• MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models#927566
• Posterior Circulation Ischemic Stroke Occurring After ChAdOx1 nCoV-19/AZD1222 Vaccination Without Evidence of Vaccine-Induced Immune Thrombotic Thrombocytopenia: A Case Report and Focused Narrative Review.#409303