Evidence map›Paper›PMID 33874998›Full record

ArticleInfectious diseases of poverty2021

Effectiveness of potential antiviral treatments in COVID-19 transmission control: a modelling study.

Sheng-Nan Lin, Jia Rui, Qiu-Ping Chen, Bin Zhao, Shan-Shan Yu, Zhuo-Yang Li, Ze-Yu Zhao, Yao Wang, Yuan-Zhao Zhu, Jing-Wen Xu and 15 more

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Article
  7. Review
  8. Article
  9. Article
  10. ComputingInfectious Disease Modelling · 2022
    Article
  11. Article
  12. Article
  13. Promising Antiviral Activity ofPharmaceuticals (Basel, Switzerland) · 2021
    Article
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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

25 authors.

Sheng-Nan Lin *State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Jia Rui *State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Qiu-Ping Chen *Medical Insurance Office, Xiang'an Hospital of Xiamen University, Xiamen, Fujian, People's Republic of China.
Bin ZhaoClinical Medical Laboratory, Xiang'an Hospital of Xiamen University, Xiamen, Fujian, People's Republic of China.
Shan-Shan YuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Zhuo-Yang LiState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Ze-Yu ZhaoState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Yao WangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Yuan-Zhao ZhuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Jing-Wen XuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Meng YangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Xing-Chun LiuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Tian-Long YangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Li LuoState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Bin DengState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Jie-Feng HuangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Chan LiuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Pei-Hua LiState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Wei-Kang LiuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Fang XieState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Yong ChenDepartment of Stomatology, School of Medicine, Xiamen University, Xiamen, Fujian, People's Republic of China.
Yan-Hua SuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China.
Ben-Hua ZhaoState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China. benhuazhao@163.com.
Yi-Chen ChiangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China. jyj@xmu.edu.cn.
Tian-Mu ChenState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an, Xiamen, Fujian, People's Republic of China. 13698665@qq.com.ORCID http://orcid.org/0000-0003-0710-5086

Funding

Bill and Melinda Gates Foundation INV-005834the Science and Technology Program of Fujian Province 2020Y0002the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program 3502Z2020YJ03
6 · The paper itself

Abstract

backgroundNovel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Although public health countermeasures effectively controlled the epidemic in China, non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally. Vaccination is mainly used to prevent COVID-19, and most current antiviral treatment evaluations focus on clinical efficacy. Therefore, we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.

methodsWe collected COVID-19 data of Wuhan City from published literature and established a database (from 2 December 2019 to 16 March 2020). We developed an age-specific model to evaluate the effectiveness of antiviral treatment in patients with COVID-19. Efficacy was divided into three types: (1) viral activity reduction, reflected as transmission rate decrease [reduction was set as v (0-0.8) to simulate hypothetical antiviral treatments]; (2) reduction in the duration time from symptom onset to patient recovery/removal, reflected as a 1/γ decrease (reduction was set as 1-3 days to simulate hypothetical or real-life antiviral treatments, and the time of asymptomatic was reduced by the same proportion); (3) fatality rate reduction in severely ill patients (f

resultsComparing the simulation results of combination and single medication therapy s, all four age groups showed better results with combination medication. When 1/γ = 2 and v = 0.4, age group 2 had the highest TAR reduction rate (98.48%, 56.01-0.85%). When 1/γ = 2, z = 0.3, and v = 0.1, age group 1 had the highest reduction rate of f (83.08%, 0.71-0.12%).

conclusionsAntiviral treatments are more effective in COVID-19 transmission control than in mortality reduction. Overall, antiviral treatments were more effective in younger age groups, while older age groups showed higher COVID-19 prevalence and mortality. Therefore, physicians should pay more attention to prevention of viral spread and patients deaths when providing antiviral treatments to patients of older age groups.

Indexed as

AdolescentAgedAge FactorsAntiviral AgentsChinaCOVID-19HumansInfectious Disease Incubation PeriodMiddle AgedModels, StatisticalSARS-CoV-2Young AdultAntiviral AgentsAge groupAntiviral treatmentCOVID-19Transmission model

Identifiers

PMID33874998
PMCPMC8054260

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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.