Evidence map›Paper›PMID 38500506›Full record

ArticleFrontiers in cellular and infection microbiology2024

Influenza and COVID-19 co-infection and vaccine effectiveness against severe cases: a mathematical modeling study.

Jingyi Liang, Yangqianxi Wang, Zhijie Lin, Wei He, Jiaxi Sun, Qianyin Li, Mingyi Zhang, Zichen Chang, Yinqiu Guo, Wenting Zeng and 4 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% 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

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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
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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

14 authors at 4 institutions in 2 countries.

Jingyi LiangDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China.
Yangqianxi WangDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China.
Zhijie LinDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China.
Wei HeDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China.
Jiaxi SunGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Qianyin LiState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Mingyi ZhangGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Zichen ChangGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Yinqiu GuoGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Wenting ZengGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Tie LiuGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Zhiqi ZengGuangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Zifeng YangRespiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.
Chitin HonDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China.
Guangzhou Medical University · CNMacau University of Science and Technology · MOFirst Affiliated Hospital of Guangzhou Medical University · CNState Key Laboratory of Respiratory Disease · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Influenza A virus have a distinctive ability to exacerbate SARS-CoV-2 infection proven by in vitro studies. Furthermore, clinical evidence suggests that co-infection with COVID-19 and influenza not only increases mortality but also prolongs the hospitalization of patients. COVID-19 is in a small-scale recurrent epidemic, increasing the likelihood of co-epidemic with seasonal influenza. The impact of co-infection with influenza virus and SARS-CoV-2 on the population remains unstudied. Method: Here, we developed an age-specific compartmental model to simulate the co-circulation of COVID-19 and influenza and estimate the number of co-infected patients under different scenarios of prevalent virus type and vaccine coverage. To decrease the risk of the population developing severity, we investigated the minimum coverage required for the COVID-19 vaccine in conjunction with the influenza vaccine, particularly during co-epidemic seasons. Result: Compared to the single epidemic, the transmission of the SARS-CoV-2 exhibits a lower trend and a delayed peak when co-epidemic with influenza. Number of co-infection cases is higher when SARS-CoV-2 co-epidemic with Influenza A virus than that with Influenza B virus. The number of co-infected cases increases as SARS-CoV-2 becomes more transmissible. As the proportion of individuals vaccinated with the COVID-19 vaccine and influenza vaccines increases, the peak number of co-infected severe illnesses and the number of severe illness cases decreases and the peak time is delayed, especially for those >60 years old. Conclusion: To minimize the number of severe illnesses arising from co-infection of influenza and COVID-19, in conjunction vaccinations in the population are important, especially priority for the elderly.

Indexed as

CoinfectionCOVID-19Influenza A virusInfluenza, HumanInfluenza VaccinesAgedCOVID-19 VaccinesHumansMiddle AgedSARS-CoV-2VaccinationVaccine EfficacyCOVID-19 VaccinesInfluenza Vaccinesco-infectioncompartmental modelinfluenzaSARS-CoV-2vaccination

Identifiers

PMID38500506
PMCPMC10945002
OpenAlexW4392372574

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.