Evidence map›Paper›PMID 38435296›Full record

ArticleFrontiers in public health2024

Scenario analysis of COVID-19 dynamical variations by different social environmental factors: a case study in Xinjiang.

Ruonan Fu, Wanli Liu, Senlu Wang, Jun Zhao, Qianqian Cui, Zengyun Hu, Ling Zhang, Fenghan Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2024. 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
0.4field-weighted citation impact, top 49% 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

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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, 1 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

8 authors at 5 institutions in 1 country.

Ruonan Fu *School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
Wanli Liu *Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
Senlu WangSchool of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
Jun ZhaoCenter of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
Qianqian CuiSchool of Mathematics and Statistics, Ningxia University, Yingchuan, Ningxia, China.
Zengyun HuSchool of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ling ZhangSchool of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fenghan WangDaizhuang Hospital, Jining, Shandong, China.
Xinjiang Uygur Autonomous Region Disease Prevention and Control Center · CNChinese Academy of Sciences · CNNingxia University · CNShanghai Mental Health Center · CNXinjiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the rapid advancement of the One Health approach, the transmission of human infectious diseases is generally related to environmental and animal health. Coronavirus disease (COVID-19) has been largely impacted by environmental factors regionally and globally and has significantly disrupted human society, especially in low-income regions that border many countries. However, few research studies have explored the impact of environmental factors on disease transmission in these regions. Methods: We used the Xinjiang Uygur Autonomous Region as the study area to investigate the impact of environmental factors on COVID-19 variation using a dynamic disease model. Given the special control and prevention strategies against COVID-19 in Xinjiang, the focus was on social and environmental factors, including population mobility, quarantine rates, and return rates. The model performance was evaluated using the statistical metrics of correlation coefficient (CC), normalized absolute error (NAE), root mean square error (RMSE), and distance between the simulation and observation (DISO) indices. Scenario analyses of COVID-19 in Xinjiang encompassed three aspects: different population mobilities, quarantine rates, and return rates. Results: The results suggest that the established dynamic disease model can accurately simulate and predict COVID-19 variations with high accuracy. This model had a CC value of 0.96 and a DISO value of less than 0.35. According to the scenario analysis results, population mobilities have a large impact on COVID-19 variations, with quarantine rates having a stronger impact than return rates. Conclusion: These results provide scientific insight into the control and prevention of COVID-19 in Xinjiang, considering the influence of social and environmental factors on COVID-19 variation. The control and prevention strategies for COVID-19 examined in this study may also be useful for the control of other infectious diseases, especially in low-income regions that are bordered by many countries.

Indexed as

Communicable DiseasesCOVID-19One HealthAnimalsComputer SimulationHumansPovertyCOVID-19 pandemicscenarios analysissimulation and predictionsocial environmental factorsXinjiang Uygur Autonomous Region

Identifiers

PMID38435296
PMCPMC10906079
OpenAlexW4391883824

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.