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Meta-Analysis
. 2025 Jan 31;25(1):145.
doi: 10.1186/s12879-025-10521-5.

Co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis

Monireh Golpour  1 , Hossein Jalali  2 , Reza Alizadeh-Navaei  3 , Masoumeh Rezaei Talarposhti  4   5 , Tahoora Mousavi  6 , Ali Asghar Nadi Ghara  7
Affiliations
Meta-Analysis

Co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis

Monireh Golpour et al. BMC Infect Dis. .

Abstract

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) is a public health problem and may result in co-infection with other pathogens such as influenza virus. This review investigates the co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19.

Methods: This meta- analysis included 38 primary studies investigating co-infection of SARS-CoV-2 with influenza in confirmed cases of COVID-19. The global online databases were used to identify relevant studies published between December 2019 and July 2024. Data analysis was performed using STATA Ver. 17 software, and standard errors of prevalence were calculated using the binomial distribution formula. Heterogeneity of study results was evaluated using the I-square and Q index, and publication bias was examined using the Begg's and Egger's tests, as well as funnel plot. A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. In addition, sensitivity analyses were performed to check the impact of each primary study on the overall estimate.

Result: The analysis found that the prevalence of influenza in co-infected patients at 95% confidence interval using a random effect model was 14% (95% CI: 8-20%). Significant heterogeneity was observed in the random-effects model for influenza A, 11% (95% CI: 5-18%) and B, 4% (95% CI: 2-7%) in co-infected patients. The highest prevalence of influenza A/B (21%), influenza A (17%) and influenza B (20%) was shown in Asia and Europe respectively. Subgroup analysis by study year showed that the co-prevalence of COVID-19 and influenza A/B was similar in the pre-2021 and post-2021 time periods, at 14% (95% CI: 5-23%) for pre-2021 and 6-22% for 2021 and post-2021. Also, the overall prevalence of influenza A and B in COVID-19 patients is 11% and 4%, and there was no significant difference between the time periods before and after 2021. Meta-regression with a random-effects model showed that the variables location, year group, and total patients showed only 2.71% of very high heterogeneity (I² = 99.92%), and none of these variables had a significant effect on the co-prevalence of COVID-19 and influenza A/B (p > 0.05). Also, meta-regression results showed that these variables had no significant effect on influenza A and B prevalence (p > 0.05) and showed only a small proportion of the very high heterogeneity (I² = 99.72%), (I² = 68.78%). In our study, Egger's test indicated that there was publication bias or small study effects in this meta-analysis (p = 0.0000).

Conclusion: The combination of SARS-CoV-2 with influenza and other respiratory viruses requires the best treatment protocols to reduce the severity of the disease. In this approach, high vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic.

Keywords: COVID-19; Co-infection; Coronavirus; Influenza virus A; Influenza virus B; Respiratory syndrome coronavirus 2; SARS-CoV-2.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Statement: A preprint has previously been published [Reza Alizadeh-Navaei_ et.al 2021, and 2024]” and references to the preprint included in the reference list. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The search strategy and flow diagram for databases, screening and selection of primary studies in the systematic review and meta-analysis
Fig. 2
Fig. 2
Forest plot diagram for estimation of total prevalence of influenza A/B in COVID-19 patients according to each primary studies and overall estimate with a 95% confidence interval
Fig. 3
Fig. 3
Overall results of forest plot subgroup analysis for influenza A/B prevalence by geographic region (Asia, Africa, America and Europe)
Fig. 4
Fig. 4
Overall Forest Plot results for influenza A/B prevalence by time period (before and after 2021)
Fig. 5
Fig. 5
Forest plot diagram for estimation of influenza A/B prevalence sensitivity analysis according to each primary study and overall estimate with a 95% confidence interval
Fig. 6
Fig. 6
Funnel plot to investigate publication bias in prevalence of influenza A/B in COVID-19 patients
Fig. 7
Fig. 7
Forest plot diagram for estimation of influenza A prevalence in COVID-19 patients according to each primary studies and overall estimate with a 95% confidence interval
Fig. 8
Fig. 8
Overall results of forest plot subgroup analysis for influenza A prevalence by geographic region (Asia, Africa, America and Europe)
Fig. 9
Fig. 9
Overall forest plot results for influenza A prevalence by time period (before and after 2021)
Fig. 10
Fig. 10
Funnel plot to investigate publication bias in prevalence of influenza A in COVID-19 patients
Fig. 11
Fig. 11
Forest plot diagram for estimation of influenza B prevalence in COVID-19 patients according to each primary studies and overall estimate with a 95% confidence interval
Fig. 12
Fig. 12
Overall results of forest plot subgroup analysis for influenza B prevalence by geographic region (Asia, Africa, America and Europe)
Fig. 13
Fig. 13
Overall forest plot results for influenza B prevalence by time period (before and after 2021)
Fig. 14
Fig. 14
Funnel plot to investigate publication bias in prevalence of influenza B in COVID-19 patients
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