Editorial: SARS-CoV-2: virology, epidemiology, diagnosis, pathogenesis, and control
Bibliographic record
Abstract
The rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the most severe public health challenge since the 1918 Spanish influenza pandemic 1 . Consequently, the crisis caused by the coronavirus disease 2019 (COVID-19) has massively impacted global public health, which has rapidly called for public health authorities and scientists to improve our knowledge about this condition 2 . In this Research Topic, we serve as Editors and our primary goal was to gather knowledge about the virology of SARS-CoV-2 and different aspects of COVID-19, including epidemiology, pathogenesis, diagnosis, and control. Below, we provide a brief context of the published studies, including 19 original articles, three narrative reviews, and one systematic review.During the course of the COVID-19 pandemic, the emergence of SARS-CoV-2 variants has been associated with evasion of immunity from natural infection and vaccinations, reduced susceptibility to therapies, increased transmissibility, risk of reinfection, and disease severity 1,[3][4][5][6][7] , resulting in a tremendous challenge to controlling the pandemic phase 8 . Within this perspective, the rapid spread of SARS-CoV-2 variants worldwide has been associated with a series of waves, as reported in many countries worldwide. To understand this impact in Santiago (Chile), Acuña-Castillo et al. showed that the highest rate of reinfections described during the fourth and fifth COVID-19 waves in Santiago was primarily driven by the omicron variant, where the interval between initial infection and reinfection was found to be 372 days. In the context of the epidemiology of COVID-19 cases, Zhang et al. Accurated COVID-19 diagnosis and testing have shown to be key for disease control, especially before vaccines were widely available 2,12 . However, diagnostics also play important role in other contexts. Thus, Acuña-Castillo did and ecological study on COVID-19 reinfection in Chile using RT-PCR data information from over 300 thousand individuals tested between 2020 and 2022.They found that the highest rate of reinfections took place during the fourth and fifth COVID-19 waves and was primarily driven by the Omicron variant. The a reinfection rate was 1.52 per 100,000 inhabitants and the interval between initial infection and reinfection was found to be close to one year.Serological tests were of limited value for clinical decision-making and implementation of patient isolation and quarantine 12 . However, SARS-CoV-2 specific antibodies are major players in the immune defense against COVID-19.To address this, Kaufman and coalleagues conducted a retrospective study to estimate the association between SARS-CoV-2 spike-protein targeted antibody levels and clinically outcomes in a cohort of almost 200 thousand patients.Individuals with detectable SARS-CoV-2 antibody levels were less prone to be infected by SARS-CoV-2 and had lower risk of developing serious disease upon infection.In a rapidly moving field of study, several articles have evaluated the effectiveness of available vaccines against SARS-CoV-2 variants, especially within the emergence of omicron VOC. To address this question, Song et al. Despite the passage of four years since the beginning of the pandemic, there are still many gaps that we need to address about this devastating disease that will certainly be recorded as one of the greatest public health problems in the history of humanity. The COVID-19 pandemic has highlighted both our ability to respond and our resilience to face biothreats of this magnitude. Most importantly, the lessons acquired during the COVID-19 pandemic will be essential for dealing with future public health threats, particularly for the response against new pathogens. Through this Research Topic, we contributed to the advancement of knowledge related to COVID-19 in several aspects, including epidemiology, genomic surveillance, diagnosis, pathogenesis, and control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".