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Record 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 on OpenAlexaboutno aff
Rabin Debnath, Arshdeep Singh, Kushal Seni, Anjali Sharma, Viney Chawla, Pooja A. Chawla

Bibliographic record

VenueAsian Pacific Journal of Tropical Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyGeographyMedicineOutbreakInternal medicine

Abstract

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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

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.340
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractyes

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