Evidence map›Paper›PMID 38298537›Full record

ArticleFrontiers in microbiology2023

Trend of viral load during the first, second, and third wave of COVID-19 in the Indian Himalayan region: an observational study of the Uttarakhand state.

Shailender Negi, Diksha, Deepjyoti Kalita, Neeraj Ranakoti, Ashish Negi, Diksha Kandwal, Shailesh Kumar Gupta, Yogendra Pratap Mathuria

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shailender NegiViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
DikshaViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
Deepjyoti KalitaDepartment of Microbiology, All India Institute of Medical Sciences, Guwahati, India.
Neeraj RanakotiViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
Ashish NegiViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
Diksha KandwalViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
Shailesh Kumar GuptaViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.
Yogendra Pratap MathuriaViral Research and Diagnostic Laboratory (VRDL), All India Institute of Medical Sciences, Rishikesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

India had faced three waves throughout the Coronavirus disease 2019 (COVID-19) pandemic, which had already impacted economic lives and affected the healthcare setting and infrastructure. The widespread impacts have inspired researchers to look for clinical indicators of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prognosis. Cyclic threshold values have been used to correlate the viral load in COVID-19 patients and for viral transmission. In light of this correlation, a retrospective study was conducted to assess the trend of viral load in clinical and demographic profiles across the three waves. Data of a total of 11,125 COVID-19-positive patients were obtained, which had a Ct value of <35. We stratified Ct values as follows: under 25 (high viral load), 25-30 (moderate viral load), and over 30 (low viral load). We found a significantly high proportion of patients with high viral load during the second wave. A significantly high viral load across the symptomatic and vaccinated populations was found in all three waves, whereas a significantly high viral load across age groups was found only in the first wave. With the widespread availability of real-time PCR and the limited use of genomic surveillance, the Ct value and viral load could be a suitable tool for population-level monitoring and forecasting.

Indexed as

COVID-19cycle thresholdSARS-CoV-2trendviral load

Identifiers

PMID38298537
PMCPMC10829093

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.