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Record W4405172472 · doi:10.1186/s44263-024-00105-x

Unveiling pandemic patterns: a detailed analysis of transmission and severity parameters across four COVID-19 waves in Bogotá, Colombia

2024· article· en· W4405172472 on OpenAlexfundno aff
David Santiago Quevedo, Diego de Miguel‐Pérez, Maria Alejandra Cabrera Polanía, Juan David Serrano Medina, Felipe Segundo Abril-Bermúdez, Diane Moyano Romero, Diana Sofía Ríos Oliveros, M. Mayorga, Charles A. Whittaker, Zulma M. Cucunubá

Bibliographic record

VenueBMC Global and Public Health · 2024
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
FundersInternational Development Research CentreWellcome Trust
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Transmission (telecommunications)GeographyVirologyMedicineComputer scienceOutbreakTelecommunicationsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Despite a wealth of data from high-income countries, there is limited information on the distinct epidemiological patterns observed in diverse, densely populated regions within Latin America. This retrospective analysis of COVID-19's four major waves in Bogotá, Colombia, evaluates 1.77 million cases in detail. METHODS: , while variant-specific transmission advantages were estimated using multinomial logistic regression models. Disease severity was assessed through a suite of indicators: Hospitalisation Case Ratio (HCR), intensive care unit case ratio (ICU-CR), case fatality ratio (CFR), hospitalisation fatality ratio (HFR), and ICU fatality ratio (ICU-FR). Additionally, we analysed the distribution of hospitalisations, ICU admissions, and fatalities by age group and wave. We employed a Bayesian hierarchical model to capture epidemiological delays-such as onset-to-death, hospitalisation, and ICU admission durations to estimate hospital and ICU stay durations. RESULTS: , with peaks exceeding 2.5 during the ancestral and Omicron waves. Over the course of the pandemic, we observed a 78% reduction in CFR, underscoring shifts in clinical severity. The third wave, associated with the Mu variant, recorded the highest case and death counts, alongside a decreased CFR, an elevated HFR, and a shift in the most affected age group towards younger populations. In contrast, the fourth wave, driven by the Omicron variant, exhibited the highest reproduction number and the lowest overall severity. This wave was characterised by a significant increase in pediatric hospitalisations. The study reveals a continued decline in the mean durations of hospital and ICU stays across the four waves, with hospital stays decreasing from 10.84 to 7.85 days and ICU stays dropping from 16.2 to 12.4 days. CONCLUSIONS: This study reveals significant shifts in transmission and severity metrics-including mortality, hospitalisation, and ICU rates and stays-across age groups during Bogotá's four COVID-19 waves. These insights underscore the value of retrospective analyses to understand the pandemic's varied impact and inform public health strategies in diverse urban settings.

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 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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.334
GPT teacher head0.473
Teacher spread0.139 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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