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Record W4399652640 · doi:10.2196/53331

South Asia’s COVID-19 History and Surveillance: Updated Epidemiological Assessment

2024· article· en· W4399652640 on OpenAlexvenueno aff
Lori Ann Post, Alan Soetikno, Scott A Wu, Claudia Hawkins, Maryann Mason, Egon A. Ozer, Robert L. Murphy, Sarah B Welch, Yingxuan Liu, Robert J Havey, Charles B. Moss, Chad J. Achenbach, Alexander Lundberg

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Epidemiology2019-20 coronavirus outbreakPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Environmental healthMedicineGeographyVirologyOutbreakInfectious disease (medical specialty)DiseasePathology

Abstract

fetched live from OpenAlex

BACKGROUND: This study updates our findings from the COVID-19 pandemic surveillance we first conducted in South Asia in 2020 with 2 additional years of data for the region. We assess whether COVID-19 had transitioned from pandemic to endemic at the point the World Health Organization (WHO) ended the public health emergency status for COVID-19 on May 5, 2023. OBJECTIVE: First, we aim to measure whether there was an expansion or contraction in the pandemic in South Asia around the WHO declaration. Second, we use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we aim to provide historical context for the course of the pandemic in South Asia. METHODS: In addition to updating the traditional surveillance data and dynamic panel estimates from our original study, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed or transmission rate per 100,000 population was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. RESULTS: Speed for the region had remained below the outbreak threshold for over a year by the time of the WHO declaration. Acceleration and jerk were also low and stable. While the 1-day persistence coefficients remained statistically significant and positive (1.168), the 7-day persistence coefficient was negative (-0.185), suggesting limited cluster effects in which cases on a given day predict cases 7 days forward. Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, did not indicate any overall change in the persistence measure around the time of the WHO declaration. From December of 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of speed equal to 10 was statistically insignificant across the entire pandemic. CONCLUSIONS: While COVID-19 continued to circulate in South Asia, the rate of transmission had remained below the outbreak threshold for well over a year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had ended by the time of the WHO declaration. Prevention policies should be a focus ahead of future pandemics. On that point, policy should emphasize an epidemiological task force with widespread testing and a contact-tracing system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.008
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.271
GPT teacher head0.459
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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