Systematic review of Lyme disease in Turkey

Önal, Uğur; Erdem, Hüseyin Aytaç; Önal, Ayşe Uyan; Sıpahi, Oğuz Reşat

doi:10.1177/0049475519843387

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…Lyme disease is considered endemic in Albania, North Macedonia, Bulgaria, and Turkey. The reported incidence of Borreliosis in Bulgaria is about 6/100,000 of the population, whereas in Turkey, 75 cases of Lyme disease have been reported [ 25 , 26 , 27 , 28 ]. The principal tick vector for Borrelia species in Europe, I. ricinus , is prevalent in Greece and potently infesting wild resident birds (mostly small passerines).…”

Section: Discussionmentioning

confidence: 99%

Aseptic Meningitis Linked to Borrelia afzelii Seroconversion in Northeastern Greece: An Emerging Infectious Disease Contested in the Region

Kouroupis,

Terzaki,

Moscha

et al. 2024

TropicalMed

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Borreliosis (Lyme disease) is a zoonosis, mediated to humans and small mammals through specific vectors (ticks), with increasing global incidence. It is associated with a variety of clinical manifestations and can, if not promptly recognized and left untreated, lead to significant disability. In Europe, the main Borrelia species causing disease in humans are Borrelia burgdorferi s.s., Borrelia afzelii, Borrelia garinii, and Borrelia spielmanii. The Ixodes ricinus tick is their principal vector. Although Lyme disease is considered endemic in the Balkan region and Turkey, and all three main Lyme pathogens have been detected in ticks collected in these countries, autochthonous Lyme disease remains controversial in Greece. We report a case of aseptic meningitis associated with antibody seroconversion against Borrelia afzelii in a young female patient from the prefecture of Thasos without any relevant travel history. The patient presented with fever and severe headache, and the cerebrospinal fluid examination showed lymphocytic pleocytosis. Serum analysis was positive for specific IgG antibodies against Borrelia afzelii. In the absence of typical erythema migrans, serological evidence of infection is required for diagnosis. Although atypical in terms of clinical presentation, the seasonality and geographical location of potential disease transmission in the reported patient should raise awareness among clinicians for a still controversial and potentially underreported emerging infectious disease in Greece.

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Section: Discussionmentioning

confidence: 99%

Aseptic Meningitis Linked to Borrelia afzelii Seroconversion in Northeastern Greece: An Emerging Infectious Disease Contested in the Region

Kouroupis,

Terzaki,

Moscha

et al. 2024

TropicalMed

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“…Lyme disease pathogens, including B. garinii and B. afzelii, have also been detected in ticks collected in the Marmara Region of Turkey [ [12] , [13] , [14] ]. Additionally, Lyme disease has been known to be endemic in Albania, North Macedonia, Bulgaria and Turkey [ [15] , [16] , [17] ]; the reported incidence of Lyme borreliosis in Bulgaria is about 6/100,000 of the population [ 18 ], whereas in Turkey 75 cases of Lyme disease have been reported [ 19 ] ( Fig. 2 ).…”

Section: Discussionmentioning

confidence: 99%

Serological evidence of possible Borrelia afzelii lyme disease in Greece

Karageorgou

Koutantou

Papadogiannaki

et al. 2022

New Microbes and New Infections

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“…In Europe, Lyme disease is mainly found in the central regions of the continent and in Scandinavian countries, and it is also found in Russia [6,9,10]. The occurrence of Lyme disease is very recent in Asia and has been reported in India, Turkey, China, Korea, Nepal, Taiwan, and Japan [11][12][13][14][15][16]. Owing to the current wide geographical spread of this disease, the early detection of potential Lyme disease cases will remain a public health concern in the forthcoming decades [17].…”

Section: Introductionmentioning

confidence: 99%

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Laison,

Hamza Ibrahim,

Boligarla

et al. 2023

J Med Internet Res

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Background Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease–related tweets were rare in countries from Africa and Asia. The most reported Lyme disease–related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. Conclusions The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

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Systematic review of Lyme disease in Turkey

Cited by 8 publications

References 38 publications

Aseptic Meningitis Linked to Borrelia afzelii Seroconversion in Northeastern Greece: An Emerging Infectious Disease Contested in the Region

Aseptic Meningitis Linked to Borrelia afzelii Seroconversion in Northeastern Greece: An Emerging Infectious Disease Contested in the Region

Serological evidence of possible Borrelia afzelii lyme disease in Greece

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

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