Evidence map›Paper›PMID 34877691›Full record

ArticleJournal of medical virology2022

Modeling and forecasting the total number of cases and deaths due to pandemic.

Nasrullah Khan, Asma Arshad, Muhammad Azam, Ali Hussein Al-Marshadi, Muhammad Aslam

Open access · greenAbstract read
In one paragraph

Article in Journal of medical virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

1 citing paper in PubMed, 13 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 2 countries.

Nasrullah KhanDepartment of Statistics, College of Veterinary and Animal Sciences, Jhang, University of Veterinary and Animal Sciences Lahore, Lahore, Pakistan.
Asma ArshadDepartment of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.
Muhammad AzamDepartment of Statistics and Computer Science, University of Veterinary and Animal Sciences Lahore, Lahore, Pakistan.
Ali Hussein Al-MarshadiDepartment of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
Muhammad AslamDepartment of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.ORCID http://orcid.org/0000-0003-0644-1950
King Abdulaziz University · SAUniversity of Veterinary and Animal Sciences · PKNational College of Business Administration and Economics · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R

Indexed as

Epidemiological ModelsPandemicsAlgorithmsCOVID-19ForecastingHumansModels, StatisticalMortalityPrevalenceSARS-CoV-2COVID-19 pandemicmean absolute percentage errorpolynomial regressionroot mean square errorR-square

Identifiers

PMID34877691
PMCPMC9015266
OpenAlexW4200066017

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.