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Enregistrement W4391326630 · doi:10.4103/apjtm.apjtm_2_24

Mapping COVID-19 in India: Southern states at the forefront of new JN.1 variant

2024· article· en· W4391326630 sur OpenAlexaboutno aff
Rabin Debnath, Arshdeep Singh, Kushal Seni, Anjali Sharma, Viney Chawla, Pooja A. Chawla

Notice bibliographique

RevueAsian Pacific Journal of Tropical Medicine · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyGeographyMedicineOutbreakInternal medicine

Résumé

récupéré en direct d'OpenAlex

A new variant, JN.1, stemming from the omicron subvariant BA.2.86, garnered the attention of the World Health Organization (WHO) as a "variant of interest." Despite its rapid global spread, especially in the US, Canada, France, Singapore, Sweden[1], and the UK, JN.1 is considered to pose minimal danger. Current vaccinations are believed to remain effective against it. The WHO underscores the importance of maintaining immunization records amid co-occurring respiratory illnesses, and epidemiologists recommend monitoring hospitalizations, particularly in areas with low vaccination rates. Despite concerns, experts anticipate JN.l's impact to be less severe than that of the omicron variant[2]. The southern states of India are more affected by the current COVID-19 outbreak than the northern states. As we have seen, Kerala, a southern state in India, reported the first case of COVID-19 back in 2020. As a result, it has established a recurring pattern, with the southern Indian states being the most affected as compared to the northern states. India reported 4 091 active COVID-19 cases and five fatalities on December 29, 2023. Based on data from the Union Health Ministry, the state-wise distribution showed that Kerala had the most cases (2 522), followed by Karnataka (568), Maharashtra (369), and Tamil Nadu (156). December 29, 2023 witnessed the largest single-day spike in COVID-19 infections, with 654 new cases reported all over India. Karnataka and Maharashtra had 96 and 50 new cases, respectively[3]. Figure 1A shows cases of COVID-19 in India which is dominated by the southern states which include Kerala, Karnataka and Tamil Nadu as compared to the northern states.Figure 1.: Representation of cases of (A) COVID-19 in 2023[ 3 ]; (B) JN. 1 subvariant of COVID-19 in India[ 5 ].On December 8, 2023, at Karakulam, Thiruvananthapuram, Kerala, a positive RT-PCR sample revealed the first case of JN.1. "No cause for panic (over JN.1 subvariant)," stated Chief Dr. NK Arora of the Indian SARS-CoV-2 Genomics Consortium (INSACOG), a network of laboratories that tracks genomic variants of the COVID-19 virus. Upper respiratory symptoms were induced by a minor variation known as JN.1. The symptoms include fever, runny nose, sore throat, headaches, and, in some cases, minor gastrointestinal issues. Within four to five days, he said, the symptoms were getting better[4]. Till date, there were 157 cases of the JN.1 sub-variant nationwide, according to INSACOG, with Kerala accounting for (78), Gujarat (34), Goa (18), Karnataka (8), Maharashtra (7), Rajasthan (5), Tamil Nadu (4), Telangana (2), and Delhi (1) cases. Notably, the JN.1 sub-variant was found in nine states and Union territories. The graph below shows the cases of COVID-19 JN.1 sub-variant cases in India, indicating that the southern states of India have took the major hit from this variant as compared to the northern states (Figure 1B). An increase of 702 COVID-19 cases occurred in India on December 28, 2023, raising the overall number of current cases to 4 097. Six fatalities were also reported during this time. Since January 2020, India has seen a total of 45 010 944 COVID-19 cases, resulting in 533 346 deaths[5]. Karnataka reported 103 new cases and one fatality on December 27, 2023, increasing the state's total number of cases to 479. Bengaluru contributed 80 of the new cases with eight others coming from Mandya. Among others, there were three from Ballari and Mysuru. In the past 24 hours, 87 individuals have been released from treatment, according to the health department[6]. On December 29, 2023, following an extended period without new infections, Manipur reported a new case of COVID-19. The affected person is a resident of Senapati district's Paomata. He or she took an aircraft from Delhi to Dimapur and then a road trip from Dimapur to Senapati. Since samples have been sent for genome sequencing to find out additional information, the precise virus variation is yet unknown. To stop any possible viral spread, authorities are keeping a careful eye on the situation. With the advent of sub-variant JN.1 and multiple states reporting new cases, the rise in COVID-19 cases has raised new concerns for the nation[7]. According to a report in 2021, southern states of India, experienced a relatively higher number of COVID-19 cases due to factors such as its high population density, robust testing and reporting infrastructure, effective contact tracing, international connectivity leading to potential virus introduction, urbanization, well-developed healthcare facilities, and the implementation of public health measures[8]. These factors collectively contributed to a more accurate identification and reporting of cases, along with the state's proactive approach in managing and containing the spread of the virus. So, this can be the reason why the southern state of India are more affected both by COVID-19 and its sub-variant JN. 1 virus as compared to the northern states. There is always a possibility that this new variant could spread to the northern regions of India. Particularly, the southern states have been the epicentres of several viral epidemics, including the JN.1 virus, the tomato virus, and monkey pox. Therefore, steps should be taken to prevent the virus from spreading to other states of India. Conflict of interest statement The authors declare that there are no conflicts of interest. Funding The authors received no extramural funding for the study. Authors' contributions All the authors have equal contribution. Publisher's note The Publisher of the Journal remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Edited by Zhang Q, Pan Y, Lei Y

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,673
Score d'incertitude au seuil0,439

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,340
Écart entre enseignants0,304 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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