First report of Lyme disease in Nepal

Pun, Sher Bahadur; Agrawal, Sumit; Jha, Santoshananda; Bhandari, Lila Nath; Chalise, Bimal Sharma; Mishra, Abadhesh; Shah, Ritesh

doi:10.1099/jmmcr.0.005128

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Rickettsia honei was reported in one human patient infested with ticks [166]. Lyme disease (caused by the tick-transmitted spirochaete Borrelia burgdorferi) was reported for the first time in 2018 in Kaski District [167] and a subsequent case was reported in a patient from Gulmi District [168]. Canines transmit vector-borne diseases at the wild-domestic interface.…”

Section: Nepalmentioning

confidence: 99%

Perception of Ticks and Tick-Borne Diseases Worldwide

de la Fuente,

Estrada-Peña,

Rafael

et al. 2023

Pathogens

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In this comprehensive review study, we addressed the challenge posed by ticks and tick-borne diseases (TBDs) with growing incidence affecting human and animal health worldwide. Data and perspectives were collected from different countries and regions worldwide, including America, Europe, Africa, Asia, and Oceania. The results updated the current situation with ticks and TBD and how it is perceived by society with information bias and gaps. The study reinforces the importance of multidisciplinary and international collaborations to advance in the surveillance, communication and proposed future directions to address these challenges.

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Section: Nepalmentioning

confidence: 99%

Perception of Ticks and Tick-Borne Diseases Worldwide

de la Fuente,

Estrada-Peña,

Rafael

et al. 2023

Pathogens

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“…(typhus and spotted fevers) and Bartonella spp. (cat scratch disease and trench fever) commonly manifest as undifferentiated fever with headaches and malaise [ 5 , 6 , 7 , 8 ]. Some of these diseases can be complicated and fatal if they are not promptly treated [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Molecular Surveillance for Vector-Borne Bacteria in Rodents and Tree Shrews of Peninsular Malaysia Oil Palm Plantations

et al. 2023

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Many human clinical cases attributed to vector-borne pathogens are underreported in Malaysia, especially in rural localities where healthcare infrastructures are lacking. Here, 217 small mammals, consisting of rodents and tree shrews, were trapped in oil palm plantations in the Peninsular Malaysia states of Johor and Perak. Species identification was performed using morphological and DNA barcoding analyses, and 203 small mammals were included in the detection of selected vector-borne bacteria. The DNA extracted from the spleens was examined for Orientia tsutsugamushi, Borrelia spp., Bartonella spp. and Rickettsia spp. using established PCR assays. The small mammals collected in this study included Rattus tanezumi R3 mitotype (n = 113), Rattus argentiventer (n = 24), Rattus tiomanicus (n = 22), Rattus exulans (n = 17), Rattus tanezumi sensu stricto (n = 1) and Tupaia glis (n = 40). Orientia tsutsugamushi, Borrelia spp. and Bartonella phoceensis were detected in the small mammals with the respective detection rates of 12.3%, 5.9% and 4.9%. Rickettsia spp., however, was not detected. This study encountered the presence of both Lyme disease and relapsing fever-related borreliae in small mammals collected from the oil palm plantation study sites. All three microorganisms (Orientia tsutsugamushi, Borrelia spp. and Bartonella phoceensis) were detected in the R. tanezumi R3 mitotype, suggesting that the species is a competent host for multiple microorganisms. Further investigations are warranted to elucidate the relationships between the ectoparasites, the small mammals and the respective pathogens.

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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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First report of Lyme disease in Nepal

Cited by 7 publications

References 10 publications

Perception of Ticks and Tick-Borne Diseases Worldwide

Perception of Ticks and Tick-Borne Diseases Worldwide

Molecular Surveillance for Vector-Borne Bacteria in Rodents and Tree Shrews of Peninsular Malaysia Oil Palm Plantations

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

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