Evidence map›Paper›PMID 36785627›Full record

ArticleDialogues in health2022

Noel George, Jang Bahadur Prasad, Pradyuman Verma

Open access · goldAbstract read
In one paragraph

Article in Dialogues in health, 2022. 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
0.3field-weighted citation impact, top 53% of its field
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, 2 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Noel GeorgeDepartment of Epidemiology and Biostatistics, KLE University, Belgaum- 590010, Karnataka, India.
Jang Bahadur PrasadDepartment of Epidemiology and Biostatistics, KLE University, Belgaum- 590010, Karnataka, India.
Pradyuman VermaDepartment of Epidemiology and Biostatistics, KLE University, Belgaum- 590010, Karnataka, India.
KLE University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19 has resurfaced in India, where it is rapidly spreading and wreaking havoc in rural areas. An effort has been undertaken to assess the levels and patterns of COVID-19 active cases in the southern states of India. To trace and reason out anomalous trends in the COVID-19 curve so that particular actions such as lockdown, de-lockdown, and healthcare improvisation can be implemented at the appropriate time. Methods: The data has retrieved from the government websites through a platform called Kaggle. The entire duration of COVID - 19 were classified into three compartments: Phase one, Resting phase, and Phase two. The Case Fatality Rate in south Indian states was analysed corresponding to the phases, and a compartmental model for COVID-19 dynamics in the region was proposed. Results: The quadratic regression model was fitted and found to be the best model for the phases except for the resting phase. Phase one was comparatively less fitted when compared to phase two. In most of the south Indian states, the active cases in phase one were almost more than four times that of phase two. The average CFR value in phase one was lower than the subsequent phase in all of the southern Indian states. In phase one, Telangana, Karnataka, and Tamil Nadu had the highest CFR (4.77,4.22, and 3.71, respectively), whereas Lakshadweep and Kerala had the lowest CFR (0.27 and 0.71, respectively). In the resting phase, the CFR stabilized in all states and reached a value between 0.2 to 2. The trend was similar in phase two also, CFR of Lakshadweep, Kerala, Telangana, and Andhra Pradesh (0.143, 0.416,0.553, 0.803) were very low, while the CFR of Andaman and Nicobar Islands, Karnataka, and Tamil Nadu (1.237, 1.306, 1.490) were very high. Conclusion: The first and second phases of the COVID-19 virus in south Indian states had different characteristics. A District-level working group with the autonomy to respond to rapidly changing local situations must be empowered to tackle the next phase. The upcoming phases could be more peaked in less time and could be a hectic situation for the health care system.

Indexed as

Active CasesCase Fatality RateCOVID-19PhasesSouth India

Identifiers

PMID36785627
PMCPMC9135483
OpenAlexW4281990468

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.