Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022.

Ting Zeng, Zhidong Teng, Ramziya Rifhat, Xiaodong Wang, Lei Wang, Kai Wang

Abstract read

In one paragraph

Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

An SEIHR model with age group and social contact for analysis of Fuzhou COVID-19 large wave.Infectious Disease Modelling · 2024
Article
Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore.Frontiers in public health · 2023
Article

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5 · Who and what money

Authors and funding

6 authors.

Ting ZengCollege of Public Health, Xinjiang Medical University, Urumqi, 830017, PR China.

Zhidong TengDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, PR China.

Ramziya RifhatDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, PR China.

Xiaodong WangDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, PR China.

Lei WangDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, PR China.

Kai WangDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures is proposed. Firstly, the existence and uniqueness of the global positive solution is obtained. Secondly, threshold criteria for the stochastic extinction and persistence in the mean with probability one are established. Moreover, a sufficient condition for the existence of unique ergodic stationary distribution for any positive solution is also established. Finally, numerical simulations are carried out in combination with real COVID-19 data from Urumqi, China and the theoretical results are verified.

Indexed as

Extinction persistence in the meanNumerical simulationStationary distributionStochastic COVID-19 model

Identifiers

PMID37035468

DOI10.1016/j.idm.2023.03.004

PMCPMC10069993

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