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Epidemiological characteristics of respiratory tract infections during and after the pandemic of COVID-19 from 2021 - 2023 in Shenzhen, southern China

BMC Public Health volume 25, Article number: 1724 (2025) Cite this article

Abstract

Objective

It is now understood that the COVID-19 pandemic and its associated containment measures have affected the epidemiology of other respiratory viruses. This study aimed to investigate respiratory pathogen infections in Shenzhen during and after the COVID-19 pandemic.

Methods

A retrospective analysis was conducted on test data from 24,814 patients at Shenzhen Third People’s Hospital between January 2021 and December 2023. The analysis focused on changes in detection rates, epidemiological characteristics, and clinical features of respiratory pathogens, including three viruses and eight bacteria.

Results

The overall positivity rate for respiratory viruses increased after the COVID-19 epidemic (P < 0.05), whereas no significant difference was detected in the overall positivity rate of most respiratory bacteria. Notably, the detection rates of influenza A and B increased after the COVID-19 epidemic, with influenza A showing the most significant increase from 4.5 to 10.8% (P < 0.05). Conversely, the detection rates of PAE and MRSA decreased significantly (P < 0.05), whereas those of HIN and SMA increased significantly (P < 0.05). The seasonal patterns of influenza A changed markedly, with a shift in peak occurrence and extended periods of high positivity. The age distribution of infections also shifted, with adults showing higher detection rates after the pandemic than school-aged children and elderly individuals did.

Conclusion

The removal of non-pharmaceutical interventions following the COVID-19 pandemic has significantly affected the epidemiological and seasonal patterns of certain respiratory pathogens in Shenzhen. These findings highlight the need for continuous surveillance of multiple respiratory pathogens and adaptive public health strategies in the post-pandemic era.

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Background

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly around the world since December 2019, leading to a public health emergency declared by the World Health Organization (WHO) [1, 2]. In response to the ongoing COVID-19 pandemic, many countries have implemented a wide range of measures known as non-pharmaceutical interventions (NPIs). China is one of the countries that implemented more stringent measures, particularly during the first year of the pandemic. These measures included immediate detection and full inclusion of suspected cases through multiple rounds of mass screening tests for entire cities; upgrading of viral testing capacity for rapid diagnosis; establishment and construction of temporary hospitals to isolate cases; and strict tracking and quarantine of close contacts via global positioning system (GPS) technology and electronic health cards [3]. A number of other NPIs (e.g., mask requirements, screening of incoming travelers) have been implemented on an ongoing basis. There is substantial evidence that the robust public health response in China is highly effective in controlling the spread of SARS-CoV-2 [4,5,6,7].

On the other hand, non-pharmaceutical measures such as mask wearing, social distancing and travel restrictions have also affected the spread of other respiratory infections [8,9,10]. Worldwide, the burden of respiratory infections, such as influenza and RSV, markedly decreased between 2020 and 2021 but rebounded in 2021 after the gradual relaxation and lifting of NPIs [11, 12]. Healthcare systems may need to prepare for future outbreaks of non-COVID-19 infections as NPIs are relaxed [13]. China implemented restrictive measures from January 2021 until December 2022, which is a longer period than many other countries did, and all the measures were relaxed from 2023 onward. Currently, there are limited data on the epidemiological characteristics of respiratory disease following the COVID-19 pandemic in China.

In this retrospective study, we evaluated the respiratory pathogen testing data of all patients who visited our hospital between 2021 and 2023. The aim of this study was to assess the pattern of respiratory pathogen changes during the COVID-19 pandemic and after the relaxation of COVID-19 restriction measures in Shenzhen, southern China.

Methods

This retrospective study collected testing data from January 2021 to December 2023 from the Shenzhen Third People’s Hospital. Nasopharyngeal swabs from patients were used as experimental samples and were collected by nurses. The experiments were conducted within the laboratories of the Laboratory Department of the Third People’s Hospital of Shenzhen. Test results were recorded and extracted from the laboratory’s testing system, and samples with missing information on test results were excluded. The patients’ essential clinical information was recorded and extracted from the hospital’s electronic medical record system. This was a retrospective study, and all patient data were reported anonymously. The study was approved by the Ethics Committee of Shenzhen Third People’s Hospital.

During our study period (January 2021 to December 2023), China implemented several phases of COVID-19 control measures. From 2021 to 2022, Shenzhen operated under the ‘dynamic zero-COVID’ strategy, which included key population screening, key venue management, mass vaccination campaigns, and strict quarantine measures. On 11 December 2022, Shenzhen stopped epidemic checks for incoming and returning persons, and indoor venues no longer required proof of negative nucleic acid tests. Finally, on 8 January 2023, Class A prevention and control measures for COVID-19 were lifted nationwide, marking the end of the Zero COVID policy.

Examination of respiratory pathogens

Three viruses, namely, influenza A, influenza B, and respiratory syncytial viruses, were detected via the GeneXpert® system with Xpert® Xpress Flu/RSV (Cepheid Inc., Sunnyvale, CA, USA; subsequently referred to as the Xpert Flu/RSV assay). Eight bacteria were detected via the CapitalBio® Isothermal Nucleic Acid Amplification Microfluidic Chip Analyzer RTisochip™-W (CapitalBio Technology, Sichuan, China), including Streptococcus pneumoniae (SP), Staphylococcus aureus (SAU), methicillin-resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae (KP), Pseudomonas aeruginosa (PAE), Acinetobacter baumannii (ABA), Stenotrophomonas maltophilia (SMA), and Haemophilus influenzae (HIN). These assays were performed according to the manufacturer’s instructions.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics (version 27.0, IBM Corp., Armonk, NY, USA) and GraphPad Prism (version 9.3.1, GraphPad Software, San Diego, CA, USA). Categorical variables were expressed as frequency counts with percentages (%) and compared across groups using chi-square test. Continuous variables were summarized as median with interquartile range (IQR). Nonparametric analyses were systematically applied due to non-normal distribution of the data: the rank sum test was used for group comparisons of continuous variables, while Spearman’s rank correlation coefficient was calculated to evaluate associations between variables. All statistical tests were two-sided, and p < 0.05 was considered statistically significant.

Results

Study population

Table 1 presents the general and clinical characteristics of the study population. Among the 24,814 patients included in the analyses, 15,005 patients were tested for all three viral pathogens. There were 7019 cases (48.7%) during the COVID-19 pandemic (2021–2022) and 7986 cases after the COVID-19 pandemic (2023). Overall, virus positivity increased after the COVID-19 pandemic (P < 0.05). Except for the adult group, there were statistically significant differences across age groups, genders and types of patients (P < 0.05).

Table 1 General and clinical characteristics of the study population
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A total of 9809 patients were tested for all eight bacterial pathogens. There were 5991 cases (48.7%) during the COVID-19 pandemic (2021–2022) and 3818 cases after the COVID-19 pandemic (2023). There was no significant difference in the overall positivity rate following the COVID-19 pandemic. However, statistically significant differences were observed across all age groups and sexes.

Prevalence of respiratory pathogens during and after the COVID-19 pandemic

Overall, the number of positive detections and the rate of positivity for all three viruses increased after the pandemic, with significant differences between influenza A and influenza B before and after the pandemic (P < 0.05). The detection rate of influenza A increased the most, from 4.5 to 10.8%. There were no significant differences in the RSV detection rates (P < 0.05). For bacteria, the number of positive samples decreased, except for SMA. The detection rates of PAE and MRSA decreased after the COVID-19 pandemic (P < 0.05), whereas those of HIN and SMA increased (P < 0.05) (Fig. 1; Table 2).

Fig. 1
figure 1

Positive cases and rates of respiratory pathogens during the COVID-19 pandemic (2021–2022) and after the COVID-19 pandemic (2023). Three respiratory viruses were included: influenza virus A and B (IVA, IVB) and respiratory syncytial virus (RSV). Eight bacteria were included: Acinetobacter baumannii (ABA), Haemophilus influenzae (HIN), Klebsiella pneumoniae (KP), Pseudomonas aeruginosa (PAE), Staphylococcus aureus (SAU), Stenotrophomonas maltophilia (SMA), Streptococcus pneumoniae (SP) and methicillin-resistant Staphylococcus aureus (MRSA)

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Table 2 Detection rates of respiratory pathogens during the COVID-19 pandemic (2021–2022) and after the COVID-19 pandemic (2023)
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Comparison of sex and age distributions of respiratory pathogens

During the COVID-19 pandemic, the percentages of male and female virus-positive samples were 56.8% and 43.2%, respectively (P < 0.05), whereas the percentages of bacteria-positive samples were 69.6% and 30.4%, respectively (P < 0.05). After the COVID-19 pandemic, the proportion of males among positive samples was also greater than that among females (viruses for 55.2% vs. 44.8%, P < 0.05; bacteria for 63.8% and 36.2%, P < 0.05) (Table S1). During the COVID-19 pandemic (Fig. S1A), MRSA, HIN, KP, IVA and RSV were the main pathogens in males, whereas HIN, RSV, IVA and PAE were the main pathogens in females. After the COVID-19 pandemic (Figure S1B), IVA, HIN and RSV were the main pathogens in both males and females, and males were also highly infected with MRSA.

Overall, with the exception of HIN, the age distributions of individual pathogens differed during and after the COVID-19 pandemic (Table S2). This was particularly evident for IVA and IVB (Figure S2A-B), where the proportion of adults testing positive increased after the COVID-19 pandemic.

Patients were divided into four age groups. Table 3 shows the detection rates of respiratory pathogens in each age group. The overall positive rates during and after COVID-19 peaked at 19.0% and 25.00%, respectively, in the 0–5 year age group. During the COVID-19 pandemic, these rates decreased with increasing age. After the COVID-19 pandemic, the detection rate in adults was 20%, which was higher than that in school-aged children and elderly individuals. During the pandemic (Figure S1C), IVA was common in all age groups. IVB occurred mainly in the 5–17 year age group. RSV infections are common in children under 5 years of age, HIN is most common in adults, and MRSA is common in adults and elderly individuals. After the pandemic (Figure S1D), IVA, IVB and HIN were common in adults. RSV infections occur in children under 5 years of age, and MRSA infections occur in adults and elderly individuals.

Table 3 Age distribution comparison of respiratory pathogen infections between 2021–2022 and 2023
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Seasonal distribution and correlation analysis of respiratory viruses

Figure 2 shows the monthly positive cases, positive rates and total number of tests for IVA, IVB and RSV during the two phases. The IVA positivity rate started to increase in May during the COVID-19 epidemic, peaked in June and then decreased to a very low level after August. In contrast, the trend of IVA positivity changed in 2023, starting in March and peaking in April. In addition, IVA positivity increased from August to December. During the COVID-19 epidemic, IVB positivity increased in February, September and December. After the epidemic, IVB positivity continued to increase, with the highest positivity rate occurring in December. The prevalence of RSV positivity during and after the COVID-19 epidemic followed a similar trend, with two periods of high prevalence in April and September. However, the positivity rate during the epidemic from July to December was significantly higher than that from March to May. Conversely, the highest detection rate after the outbreak occurred in April, followed by September. Correlation analysis revealed changes in the relationship patterns among the three viruses during and after the epidemi (Figure S3). The correlation between IVA and IVB shifted from negative to positive (rho=-0.23 vs. rho = 0.29), while the positive correlation between IVA and RSV became stronger (rho = 0.30 vs. rho = 0.41). The negative correlation between IVB and RSV also intensified (rho=-0.27 vs. rho=-0.44). However, none of these changes reached statistical significance (all p > 0.05).

Fig. 2
figure 2

Seasonal patterns of viruses during the COVID-19 pandemic (2021–2022) and after the COVID-19 pandemic (2023). Three respiratory viruses have been identified: influenza virus A (IVA), influenza virus B (IVB) and respiratory syncytial virus (RSV). From 2021 to 2022, Shenzhen implemented various epidemic prevention and control measures, including key population screening, key venue management, vaccination and quarantine measures. From 11 December 2022, it stopped epidemic checks for incoming and returning people, and indoor venues stopped requiring negative nucleic acid tests. On 8 January 2023, Class A prevention and control measures for COVID-19 were lifted

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Discussion

SARS-CoV-2 is a highly contagious respiratory virus that spreads from person to person through contact with aerosols from infected individuals. Non-pharmaceutical interventions (NPIs) were used worldwide in the early months of the COVID-19 pandemic, which began in 2020 [14]. Many anti-infection interventions have slowed the COVID-19 pandemic, with enormous health benefits [15]. However, these policies also affect the prevention and treatment of acute and chronic diseases [16]. The public health measures introduced during the COVID-19 pandemic had a major impact on the prevalence of respiratory viral infections other than COVID-19. The burden of respiratory infections such as influenza, RSV and even the common cold was significantly reduced. Influenza transmission has declined sharply [17, 18], or outbreaks have occurred only in the off-season [19]. RSV circulation was also significantly affected, with very low detection during the usual autumn‒winter peaks or a delay in the appearance of the RSV peak [20, 21].

With relaxation of NPIs, rebound or outbreaks of diseases such as RSV and rhinovirus have been observed in children, possibly due to reduced community immunity [22, 23]. Our study in Shenzhen provides empirical evidence to support these predictions and offers insights into the changing landscape of respiratory infections in the post-pandemic era. We observed that hospitalizations significantly increased compared with the years during the outbreak. This increase likely reflects the rapid exposure of susceptible populations to various respiratory pathogens as control measures are relaxed. The observed increase in viral positivity rates, particularly for influenza A, is consistent with the expected rebound effect following NPI relaxation. This increase can be attributed to several factors, including reduced population immunity due to limited exposure during the pandemic, interruption of the vaccination programme and potential viral interference effects. Similar trends have been observed in other parts of the world, with studies reporting significant increases in respiratory virus detection following the relaxation of COVID-19 restrictions [24, 25].

Interestingly, our study revealed that most bacterial respiratory infections did not significantly differ in overall positivity rates during and after the pandemic, contrasting with viral pathogens. This lack of dramatic change might be explained by the commensal nature of many bacterial pathogens [26]. Unlike respiratory viruses, many bacterial pathogens are part of the normal human microbiota [27, 28]. The constant presence of these bacteria in the human microbiome might provide a reservoir for potential infections, regardless of social distancing measures [29]. However, we did observe changes in specific bacteria. The decrease in PAE and MRSA after the pandemic could be attributed to improved hygiene measures and reduced hospital admissions [30]. The increase in HIN and SMA might reflect shifts in the respiratory tract microbiome, possibly due to viral-bacterial interactions [31]. Studies have shown that COVID-19 vaccination can influence respiratory microbiome composition [32, 33], potentially contributing to the observed changes in bacterial detection rates.

Regarding age distribution, while the overall positive rate peaked in the 0–5 year age group both during and after the pandemic, we observed higher detection rates in adults compared to school-aged and elderly people post-pandemic. Adults may have resumed social activities more quickly, increasing their exposure risk, and different age groups may have varied in their adherence to NPIs. Notably, some studies reported age distribution trends that revealed that school-aged people [34] or elderly people [25] were at high risk for respiratory infection in their populations after the pandemic, which differed from our findings. These differences highlight the importance of regional factors in shaping postpandemic respiratory infection patterns and underscore the need for broader, multicenter studies.

For sex differences, positive cases were consistently greater in males than females, both during and after the pandemic. These differences may be due to immune system variations, sex hormones, physiological factors, and sociocultural behaviors [35]. Immune responses between males and females may be a key factor in this disparity, and behavioral factors may also play an important role; for example, males may be less willing to take preventive measures such as wearing masks or maintaining social distance, which may increase their risk of infection. However, it is important to note that not all studies have reported consistent sex differences [34]. This inconsistency highlights the potential role of local factors, such as occupational exposure, health-seeking behavior or cultural practices, in shaping gender-specific infection patterns.

The changes in the seasonal patterns of respiratory viruses observed in our study have important implications for public health planning and resource allocation. The shift in the influenza A peak from June to April and the prolonged period of high positivity from August to December suggest the need for adjusted timing of influenza vaccination campaigns, extended periods of enhanced surveillance and targeted public health messages. During the COVID-19 pandemic, RSV was predominantly observed during the summer months, and after the epidemic, RSV appeared to have shifted and been delayed. This shift in RSV seasonality could be attributed to the disruption of typical transmission patterns by NPIs, leading to an accumulation of susceptible individuals who had not been exposed to RSV during lockdowns [36]. The delayed appearance might also be influenced by viral interference, where the dominance of one respiratory virus can temporarily suppress the circulation of others [37].The persistence of RSV as a significant pathogen in young children highlights the need for continued vigilance and possibly targeted interventions in this vulnerable group. These changes in seasonality are consistent with findings from other regions [38, 39], although the specific timing and duration of peaks may vary geographically.

These findings align with global observations of changing epidemiological patterns following the COVID-19 pandemic. The rebound effect, particularly in viral infections, is consistent with predictions of increased outbreaks as NPIs are lifted. Our study provides valuable local data for informing public health strategies in the post-pandemic period.

However, it is important to acknowledge the limitations of the present study. Firstly, the statistical power for multivariate modelling was fundamentally constrained by the low prevalence of pathogen-positive specimens (detection rate < 15%). For instance, when we attempted preliminary logistic regression, the model fit was poor, as indicated by the Hosmer-Lemeshow test with a p-value less than 0.001. This implies that, even with adjustments such as weighting, proceeding with binary logistic regression would likely result in biased estimates and potentially spurious associations within a multivariate logistic model. Consequently, we were compelled to employ univariate analyses, which have been shown to be more robust in the face of these data constraints and yield unambiguous results concerning the relationships between our variables of interest and the positivity of respiratory pathogens. Secondly, owing to inherent limitations in our data collection methodology, we were unable to perform more detailed temporal stratification for the 2021–2022 epidemic period, particularly regarding specific periods of policy changes in Shenzhen. Thirdly, the three-year study period may not be sufficiently extensive to establish long-term trends, and the findings may be influenced by factors specific to Shenzhen. Future research should implement more refined temporal stratification, extend the observation period, include data from multiple geographical locations, incorporate genomic surveillance, and investigate the impact of vaccination rates and specific public health interventions on pathogen prevalence. Moreover, comparative studies across different regions and healthcare systems would be valuable in identifying global trends and local variations, as well as conducting in-depth analyses of microbiological changes during specific epidemic stages.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. World Health Organization (WHO). WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19. Available online: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 (accessed on 11 March 2020).

  3. Zanin M, Xiao C, Liang T, et al. The public health response to the COVID-19 outbreak in Mainland China: a narrative review. J Thorac Dis. 2020;12(8):4434–49.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lai S, Ruktanonchai NW, Zhou L, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 2020;585(7825):410–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tian H, Liu Y, Li Y, et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020;368(6491):638–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bushman M, Worby C, Chang HH, Kraemer MUG, Hanage WP. Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China. Eur J Epidemiol. 2021;36(4):429–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kraemer MUG, Yang CH, Gutierrez B, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493–7.

    Article  CAS  PubMed  Google Scholar 

  8. Rankin DA, Spieker AJ, Perez A, Stahl AL, Rahman HK, Stewart LS, Schuster JE, Lively JY, Haddadin Z, Probst V, Michaels MG, Williams JV, Boom JA, Sahni LC, Staat MA, Schlaudecker EP, McNeal MM, Harrison CJ, Weinberg GA, Szilagyi PG, Englund JA, Klein EJ, Gerber SI, McMorrow M, Rha B, Chappell JD, Selvarangan R, Midgley CM, Halasa NB. NVSN network investigators. Circulation of rhinoviruses and/or enteroviruses in pediatric patients with acute respiratory illness before and during the COVID-19 pandemic in the US. JAMA Netw Open. 2023;6(2):e2254909.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Davis WW, Mott JA, Olsen SJ. The role of non-pharmaceutical interventions on influenza circulation during the COVID-19 pandemic in nine tropical Asian countries. Influenza Other Respir Viruses. 2022;16(3):568–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Geng MJ, Zhang HY, Yu LJ, et al. Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic. Nat Commun. 2021;12(1):6923. Published 2021 Nov 26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hatter L, Eathorne A, Hills T, Bruce P, Beasley R. Respiratory syncytial virus: paying the immunity debt with interest. Lancet Child Adolesc Health. 2021;5(12):e44–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Maison N, Peck A, Illi S, Meyer-Buehn M, von Mutius E, Hübner J, von Both U. The rising of old foes: impact of lockdown periods on non-SARS-CoV-2 viral respiratory and Gastrointestinal infections. Infection. 2022;50(2):519–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Baker RE, Park SW, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. Proc Natl Acad Sci U S A. 2020;117(48):30547–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shen Y, Powell G, Ganser I, Zheng Q, Grundy C, Okhmatovskaia A, Buckeridge DL. Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic. Sci Data. 2021;24(1):225.

    Article  Google Scholar 

  15. Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, Druckenmiller H, Huang LY, Hultgren A, Krasovich E, Lau P, Lee J, Rolf E, Tseng J, Wu T. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature. 2020;584(7820):262–7.

    Article  CAS  PubMed  Google Scholar 

  16. Müller O, Razum O, Jahn A. Effects of non-pharmaceutical interventions against COVID-19 on the incidence of other diseases. Lancet Reg Health Eur. 2021;6:100139.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Olsen SJ, Winn AK, Budd AP, et al. Changes in influenza and other respiratory virus activity during the COVID-19 pandemic-United States, 2020–2021. Am J Transpl. 2021;21(10):3481–6.

    Article  CAS  Google Scholar 

  18. Chan CP, Wong NS, Leung CC, Lee SS. Positive impact of measures against COVID-19 on reducing influenza in the Northern hemisphere. J Travel Med. 2020;27(8):taaa087.

    Article  PubMed  Google Scholar 

  19. Kim JH, Roh YH, Ahn JG, et al. Respiratory syncytial virus and influenza epidemics disappearance in Korea during the 2020–2021 season of COVID-19. Int J Infect Dis. 2021;110:29–35.

    Article  CAS  PubMed  Google Scholar 

  20. Oh DY, Buda S, Biere B, et al. Trends in respiratory virus circulation following COVID-19-targeted nonpharmaceutical interventions in Germany, January - September 2020: analysis of National surveillance data. Lancet Reg Health Eur. 2021;6:100112.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Delestrain C, Danis K, Hau I, et al. Impact of COVID-19 social distancing on viral infection in France: A delayed outbreak of RSV. Pediatr Pulmonol. 2021;56(12):3669–73.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Foley DA, Yeoh DK, Minney-Smith CA, et al. The interseasonal resurgence of respiratory syncytial virus in Australian children following the reduction of coronavirus disease 2019-Related public health measures. Clin Infect Dis. 2021;73(9):e2829–30.

    Article  CAS  PubMed  Google Scholar 

  23. Yasmin F, Ali SH, Ullah I. Norovirus outbreak amid COVID-19 in the united Kingdom; priorities for achieving control. J Med Virol. 2022;94(3):1232–5.

    Article  CAS  PubMed  Google Scholar 

  24. Abushahin A, Toma H, Alnaimi A, et al. Impact of COVID–19 pandemic restrictions and subsequent relaxation on the prevalence of respiratory virus hospitalizations in children. BMC Pediatr. 2024;24(1):91.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhao P, Zhang Y, Wang J, et al. Epidemiology of respiratory pathogens in patients with acute respiratory infections during the COVID-19 pandemic and after easing of COVID-19 restrictions. Microbiol Spectr. 2024;12(11):e0116124.

    Article  PubMed  Google Scholar 

  26. Man WH, de Steenhuijsen Piters WA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol. 2017;15(5):259–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Brugger SD, Bomar L, Lemon KP. Commensal-Pathogen interactions along the human nasal passages. PLoS Pathog. 2016;12(7):e1005633. Published 2016 Jul 7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. de Steenhuijsen Piters WA, Sanders EA, Bogaert D. The role of the local microbial ecosystem in respiratory health and disease. Philos Trans R Soc Lond B Biol Sci. 2015;370(1675):20140294.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bomar L, Brugger SD, Lemon KP. Bacterial microbiota of the nasal passages across the span of human life. Curr Opin Microbiol. 2018;41:8–14.

    Article  PubMed  Google Scholar 

  30. Donà D, Di Chiara C, Sharland M. Multi-drug-resistant infections in the COVID-19 era: a framework for considering the potential impact. J Hosp Infect. 2020;106(1):198–9.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Edouard S, Million M, Bachar D, et al. The nasopharyngeal microbiota in patients with viral respiratory tract infections is enriched in bacterial pathogens. Eur J Clin Microbiol Infect Dis. 2018;37(9):1725–33.

    Article  CAS  PubMed  Google Scholar 

  32. De Maio F, Posteraro B, Ponziani FR, et al. Nasopharyngeal microbiota profiling of SARS-CoV-2 infected patients. Biol Proced Online. 2020;22:18.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lee KH, Gordon A, Foxman B. The role of respiratory viruses in the etiology of bacterial pneumonia: an ecological perspective. Evol Med Public Health. 2016;2016(1):95–109.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zheng PP, Zhao YN, Wang ZK, et al. Prevalence of respiratory pathogens among hospitalised patients with acute respiratory infection during and after the COVID-19 pandemic in Shijiazhuang, China. Front Cell Infect Microbiol. 2024;14:1486953. Published 2024 Nov 28.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Falahi S, Kenarkoohi A. Sex and gender differences in the outcome of patients with COVID-19. J Med Virol. 2021;93(1):151–2.

    Article  CAS  PubMed  Google Scholar 

  36. Yeoh DK, Foley DA, Minney-Smith CA, et al. Impact of COVID-19 public health measures on detections of influenza and respiratory syncytial virus in children during the 2020 Australian winter. Clin Infect Dis. 2021;72(12):2199–202.

    Article  CAS  PubMed  Google Scholar 

  37. Huang QS, Wood T, Jelley L, et al. Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in new Zealand. Nat Commun. 2021;12(1):1001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lu C, Barr IG, Lambert S et al. Shifts in seasonal influenza patterns in Australia during and after COVID-19: A comprehensive analysis. J Infect Public Health Published Online Dec 16, 2024.

  39. Liu P, Xu M, Lu L, et al. The changing pattern of common respiratory and enteric viruses among outpatient children in Shanghai, China: two years of the COVID-19 pandemic. J Med Virol. 2022;94(10):4696–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

We acknowledge the Guangdong Basic and Applied Basic Research Foundation (grant number 2020A1515010586), Science and Technology Program of Shenzhen (grant numbers KCXFZ20230731100901003, JCYJ20190809144005609 and JCYJ202103241312034), Third People’s Hospital of Shenzhen (grant number G2021014), and Shenzhen Key Laboratory of Biochip (grant number ZDSYS201504301534057).

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Authors and Affiliations

  1. Department of Clinical Laboratory, Shenzhen Third People’s Hospital, Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, No 29 Bulan Rd, Shenzhen, Guangdong, 518112, China

    Qiaoruo Jin, Sheng Yu & Jiuxin Qu

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  1. Qiaoruo Jin
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  2. Sheng Yu
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Contributions

J.Q. conceived the original idea; Q.J. and S.Y. collected the data and performed statistical analysis; Q.J. organized the data and wrote the manuscript.

Corresponding author

Correspondence to Jiuxin Qu.

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Ethical approval

This retrospective study was approved by the Ethics Committee of Shenzhen Third People’s Hospital and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The requirement for informed consent was waived by the Institutional Review Board due to the retrospective nature of the study.

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The authors declare no competing interests.

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Jin, Q., Yu, S. & Qu, J. Epidemiological characteristics of respiratory tract infections during and after the pandemic of COVID-19 from 2021 - 2023 in Shenzhen, southern China. BMC Public Health 25, 1724 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22884-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22884-0

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BMC Public Health

ISSN: 1471-2458

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