Evidence map›Paper›PMID 38385019›Full record

ArticleInfectious Disease Modelling2024

Valuation and comparison of the actual and optimal control strategy in an emerging infectious disease: Implication from a COVID-19 transmission model.

Lili Liu, Xi Wang, Ou Liu, Yazhi Li, Zhen Jin, Sanyi Tang, Xia Wang

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2024. 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

7 authors.

Lili LiuShanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Xi WangShanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Ou LiuSchool of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China.
Yazhi LiSchool of Mathematics and Statistics, Qiannan Normal University for Nationalities, Guizhou, Duyun, 558000, China.
Zhen JinShanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Sanyi TangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, China.
Xia WangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To effectively combat emerging infectious diseases like COVID-19, it is crucial to adopt strict prevention and control measures promptly to effectively contain the spread of the epidemic. In this paper, we propose a transmission model to investigate the influence of two control strategies: reducing contact numbers and improving medical resources. We examine these strategies in terms of constant control and time-varying control. Through sensitivity analysis on two reproduction numbers of the model with constant control, we demonstrate that reducing contact numbers is more effective than improving medical resources. Furthermore, these two constant controls significantly influence the peak values and timing of infections. Specifically, intensifying control measures can reduce peak values, albeit at the expense of delaying the peak time. In the model with time-varying control, we initially explore the corresponding optimal control problem and derive the characteristic expression of optimal control. Subsequently, we utilize real data from January 10th to April 12th, 2020, in Wuhan city as a case study to perform parameter estimation by using our proposed improved algorithm. Our findings illustrate that implementing optimal control measures can effectively reduce infections and deaths, and shorten the duration of the epidemic. Then, we numerically explore that implementing control measures promptly and increasing intensity to reduce contact numbers can make actual control be more closer to optimized control. Finally, we utilize the real data from October 31st to November 18th, 2021, in Hebei province as a second case study to validate the feasibility of our proposed suggestions.

Indexed as

Actual controlEmerging epidemicOptimal controlSensitivity analysisTransmission model

Identifiers

PMID38385019
PMCPMC10879675

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.