Evidence map›Paper›PMID 36636154›Full record

ArticleAIMS public health2022

Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories.

Gabriel Owusu, Han Yu, Hong Huang

Abstract read
In one paragraph

Article in AIMS public 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
–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

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

3 authors.

Gabriel OwusuDepartment of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO 80639, USA.
Han YuDepartment of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO 80639, USA.
Hong HuangSchool of Information, University of South Florida, Tampa, FL, 33620, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.

Indexed as

co-occurrenceCOVID-19hierarchical modelslatent log-linear Poisson processtemporal dynamics

Identifiers

PMID36636154
PMCPMC9807409

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.