Evidence map›Paper›PMID 33786017›Full record

ArticleJournal of public affairs2021

The incubation period of coronavirus disease (COVID-19): A tremendous public health threat-Forecasting from publicly available case data in India.

Rinshu Dwivedi, Ramesh Athe, Kavi Mahesh, Praveen Kumar Modem

Abstract read
In one paragraph

Article in Journal of public affairs, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

4 authors.

Rinshu DwivediIndian Institute of Information Technology Trichy Tamil Nadu India.ORCID https://orcid.org/0000-0001-5912-2764
Ramesh AtheIndian Institute of Information Technology Dharwad Karnataka India.ORCID https://orcid.org/0000-0002-2915-5095
Kavi MaheshIndian Institute of Information Technology Dharwad Karnataka India.
Praveen Kumar ModemIndian Institute of Information Technology Dharwad Karnataka India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The World Health Organization (WHO) declared the Coronavirus Disease (COVID-19) a pandemic due to the huge upsurge in the number of reported cases worldwide. The COVID-19 pandemic in India has become a public health threat, and if we go by the number of confirmed cases then the situation seems to be a matter of grave concern. According to real-time data, the numbers of confirmed cases are growing exponentially. No doubt, substantial public health interventions both at the national and state levels are implemented immediately by the Government of India; there is a need for improved preparedness plans and mitigation strategies along with accurate forecasting. The present study aims to forecast the COVID-19 outbreak infected cases in India. The data have been obtained from https://www.covid19india.org, https://www.worldometers.info/coronavirus, and ICMR reported publicly available information about COVID-19 confirmation cases. We have used the double exponential smoothing method for forecasting the trends in terms of confirmed, active, recovered and death cases from COVID-19 for emergency preparedness and future predictions. Findings reveal that the estimated value of point forecast is just 8.22% of the total number of confirmed cases reported on a daily basis across the country. It was observed that the deaths were lower for the states and union territories with a higher detection rate. It is suggested that by keeping in view the limited healthcare resources in the country, accurate forecasting, early detection, and avoidance of acute care for the majority of infected cases is indispensable.

Indexed as

coronavirus disease (COVID‐19)emergency‐preparednessforecastingIndiapublic‐health

Identifiers

PMID33786017
PMCPMC7995164

What OpenQuestion holds

Textmetadata
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.