Evidence map›Paper›PMID 33876598›Full record

ArticleJournal of epidemiology and global health2021

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States.

Olusola S Makinde, Abiodun M Adeola, Gbenga J Abiodun, Olubukola O Olusola-Makinde, Aceves Alejandro

Abstract readComparative Study
In one paragraph

Article in Journal of epidemiology and global health, 2021. 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
–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

1 citing paper in PubMed.

  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.

Olusola S MakindeDepartment of Statistics, Federal University of Technology, P.M.B. 704, Akure, Nigeria.
Abiodun M AdeolaResearch and Development Department, South African Weather Service, Private Bag X097, Pretoria 0001, South Africa.
Gbenga J AbiodunDepartment of Mathematics, Southern Methodist University, Dallas, TX 75275, USA.
Olubukola O Olusola-MakindeDepartment of Microbiology, Federal University of Technology, P.M.B. 704, Akure, Nigeria.
Aceves AlejandroDepartment of Mathematics, Southern Methodist University, Dallas, TX 75275, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA' COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (

Indexed as

Communicable Disease ControlForecastingModels, TheoreticalCOVID-19HumansIncidenceSARS-CoV-2United StatesARIMACOVID-19INGARCHlockdown policynegative binomialUSA

Identifiers

PMID33876598
PMCPMC8242119

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.