Abstract
Background
To analyse the epidemiological characteristics and variation trend of influenza in Zhejiang Province from 2018 to 2023, so as to provide reference for the prevention and control of influenza after COVID-19 pandemic.
Methods
Throat swab samples of influenza-like illness (ILI) cases from 16 local sentinel hospitals were collected every week, and real-time reverse transcription-polymerase chain reaction (RT-PCR) was used to detect and identify the specific types of influenza viruses. Descriptive epidemiological method was used to analyse the surveillance data from sentinel hospitals and network laboratories.
Results
The ILI% were fluctuated between 3.67% and 8.73% from 2018 to 2023 in Zhejiang Province, with a notable increase after COVID-19 pandemic (P < 0.05). The 0–4 years age group had the highest incidence of ILI cases (45.13%), whereas the 5–14 years age group exhibited the highest influenza virus positive rate (26.01%). There were significant differences in temporal distribution and age distribution in ILI% and influenza virus positive rates (P < 0.001). Apart from 2020, the influenza outbreaks in other years displayed a seasonal pattern, with varying predominant strains in different years. During 2020–2022, the correlation between ILI number and the influenza virus positive rate was not statistically significant among 0–4 years age group but it was statistically significant among age groups > 15 years (P < 0.001).
Conclusions
The COVID-19 pandemic has markedly influenced the epidemiological characteristics of influenza. Children under 15 years are the focus group for influenza prevention and control. The current definition of ILI may not be appropriate for influenza surveillance in children under 15 years during the COVID-19 pandemic. Continuous influenza surveillance, pay close attention to the changes of influenza virus strains, actively promote the influenza vaccination of key groups and the combination of COVID-19 surveillance in the influenza sentinel surveillance network will play an important role in realizing accurate influenza prevention and control.
Keywords: Influenza-like illness, COVID-19, Epidemiology, Virology, Influenza surveillance
Background
Influenza was the first disease globally monitored, but it remains difficult to control worldwide. Influenza has repeatedly caused global pandemic, bringing a heavy burden of disease to all countries in the world, and posing a serious threat to the physical and mental health of all mankind for a long time [1, 2]. Annually, approximately one billion individuals worldwide contract seasonal influenza, resulting in three to five million severe cases and 290,000 to 650,000 fatalities due to flu-related respiratory illnesses [3]. China, a nation bearing a high burden of influenza infection, experiences an average of 84,000 to 92,000 excess deaths annually from influenza-associated respiratory diseases, representing 8.2% of all respiratory disease deaths and approximately 13.6% of global influenza mortality [4]. Against the backdrop of the COVID-19 pandemic, the resurgence of influenza could exacerbate the cumulative burden. Research indicates that, compared to single SARS-CoV-2 infections, the risk of requiring mechanical ventilation among hospitalized patients co-infected with influenza virus increased by 4.14 times, while the risk of in-hospital mortality increased by 2.35 times [5]. The co-infections of influenza and SARS-CoV-2 can intensify patient morbidity, extend the duration of illness, elevate patient case fatality rates, and impose significant social and economic burdens [6–8]. Such dual epidemics would also severely strain the emergency medical security systems [9].
Influenza surveillance is carried out globally with reference to the influenza-like illness (ILI) recommended by World Health Organization (WHO), primarily aiming to promptly understand the epidemiological characteristics and dynamic trends of influenza. Currently, the prevalence of influenza among the general population and in hospitalized patients is mainly determined by the monitoring of ILI cases at sentinel hospitals and the severe acute respiratory infections (SARI) in emergency departments. Additionally, part of ILI and SARI samples undergo etiological testing to identify predominant influenza virus strains, providing essential information for the selection of influenza vaccine strains. Nevertheless, varying definitions of ILI significantly influence influenza surveillance outcomes [10, 11]. In the realm of influenza surveillance, the accuracy of ILI monitoring is contingent upon the quality of data collection, and the positive rate of nucleic acid testing is influenced by the quality of sample collection and the sensitivity of detection kits, potentially leading to discrepancies between surveillance results and the true situation. The ILI definition utilized in China diverges from those of the WHO, the European Centre for Disease Prevention and Control, and the US Centers for Disease Control and Prevention. Since the outbreak of the COVID-19 in 2020, there have been notable shifts in global influenza virus activity, prompting a re-evaluation of the efficacy of the current ILI case definition in influenza surveillance.
Through epidemiological investigations and pathogen monitoring, we study analyzed the epidemic characteristics and changing rules of influenza virus infection in Zhejiang Province before and after the COVID-19 pandemic, and explored the relationship between the number of influenza-like cases (ILI) in different age groups across different years and the positive rate of influenza virus nucleic acid detection, so as to provide reference for the effective prevention and control of influenza after COVID-19 pandemic.
Methods
Source of data
ILI data were sourced from the China Influenza Surveillance Information System (CISIS). The influenza surveillance network in Zhejiang Province, as a part of the Chinese influenza surveillance system, was initially launched in 2001. The network has covered 11 collaborating laboratories of local CDCs and 16 sentinel hospitals until 2009. Sentinel hospitals were required to monitor ILI cases in all medical outpatient, medical emergency, fever, and pediatric medical outpatient and emergency departments. ILI is defined as a fever (body temperature ≥ 38 °C) accompanied by cough or sore throat according to the Chinese Influenza Surveillance Technical Guidelines (2017 edition) [12]. These data would be uploaded into CISIS by designated hospital staff in hospitals daily. ILI% is calculated as the ratio of ILI cases to the total number of outpatient and emergency cases. Samples of ILI cases meeting the criteria (ILI cases that have not taken antiviral drugs within 3 days of onset) were collected and sent to designated influenza network laboratories for etiological testing. The types of specimens collected for ILI cases include throat swabs (the main specimens), nasal swabs and nasopharyngeal swabs. The number of specimens collected each week was 10 to 40, reaching an average of 20 per week throughout the year. Specimens were transported to the corresponding influenza surveillance network laboratory within 48 h at 4 °C for real-time reverse transcription-polymerase chain reaction (RT-PCR) and/or viral isolation. Repeated freeze-thaw cycles were avoided. The detected results were input into CISIS by collaborating laboratories. The different stages of the COVID-19 pandemic are announced in accordance with the progression of the epidemic within China, before the COVID-19 pandemic (before January 2020), during the pandemic (January 2020 to the end of 2022), and after the pandemic (the end of 2022 and after).
Virus detection and isolation
Virus RNA extraction was completed by using RNeasy Mini kit (Qiagen, Dusseldorf, Germany), according to the manufacturers’ recommendations. The nucleic acids of the virus were detected by real-time RT-PCR in accordance with the corresponding kit. The PCR reaction system and amplification conditions used for the detection were determined according to the selected kit, and cross-contamination should be avoided during the operation. The identification of influenza virus was carried out by using AgPath-ID Onestep RT-PCR kit (Applied Biosystems, Foster City, CA), the primers and the fluorogenic probes were synthesized in accordance with the Chinese Influenza Surveillance Technical Guidelines (2017 edition) [12].
Specimens tested positive by real-time RT-PCR were propagated in Madin–Darby canine kidney (MDCK) cells by network laboratories of local CDCs. The supernatant was tested by using hemagglutinin (HA) assay with human “O” type red blood cells, and influenza type and subtype was conducted for those with positive hemagglutinin inhibition tests or PCR. The extracted RNA was first tested for the presence of influenza A and B viruses. The nucleic acid showing positive for influenza A/B was further differentiated into subtypes and lineages. Specific test procedures were detailed in China Influenza Surveillance Technical Guidelines (2017 edition). The isolated viruses were submitted to Chinese National Influenza Center (CNIC) for further analysis.
Quality control
All sentinel hospitals and network laboratories strictly adhered to the requirements and standards of the influenza surveillance program. To ensure the quality and level of influenza network surveillance, personnel involved in specimen collection, transportation, and laboratory testing received professional training. The Zhejiang Provincial Center for Disease Control and Prevention annually evaluated the quality of influenza surveillance work in various cities every year.
Data analysis
Statistical analysis was performed using R software (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria). Chi-square test was used to compare ILI% across different years and periods of the COVID-19 pandemic. The positivity of influenza virus’s gene in different years and age groups and the distribution of influenza virus subtypes across different age groups were also compared using Chi-square test. Spearman correlation analysis was used to examine the relationship between the number of ILI cases and the positive rate of nucleic acid detection in different age groups. P < 0.05 was considered statistically significant.
Results
ILI monitoring situation
From 2018 to 2023, a total of 2,540,302 ILI cases were reported in Zhejiang Province, with the lowest number (245,685) in 2020 and the highest (835,570) in 2023. ILI% ranged from 3.67 to 8.73%, with the lowest in 2021 and the highest in 2023, averaging 5.08%. The difference in ILI% across different years was statistically significant (χ² = 341,332.812, P < 0.001), as shown in Table 1. Apart from 2020 to 2021, ILI winter and spring peaks occurred in all other years (December to March of the following year), and a summer peak was observed in 2022 (June to August). In 2023, ILI increased rapidly starting from the 8th week and peaked in the 10th week, higher than that of previous years, as shown in Fig. 1A.
Table 1.
Characteristics of ILI% surveillance in Zhejiang Province, 2018–2023
| Surveillance years | Age(years) | Counts | OPD/ED | ILI% | ||||
|---|---|---|---|---|---|---|---|---|
| 0–4 (%) | 5–14(%) | 15–24(%) | 25–59(%) | ≥ 60(%) | ||||
| 2018 | 203,601(58.48) | 85,469(24.55) | 11,906(3.42) | 38,457(11.05) | 8742(2.51) | 348,175 | 8,997,549 | 3.87 |
| 2019 | 242,548(52.91) | 130,564(28.48) | 17,528(3.82) | 54,343(11.86) | 13,394(2.92) | 458,377 | 9,535,911 | 4.81 |
| 2020 | 121,844(49.59) | 59,329(24.15) | 15,696(6.39) | 37,528(15.27) | 11,288(4.59) | 245,685 | 5,782,923 | 4.25 |
| 2021 | 150,580(52.85) | 71,284(25.02) | 14,381(5.05) | 37,102(13.02) | 11,582(4.06) | 284,929 | 7,772,459 | 3.67 |
| 2022 | 135,394(36.84) | 111,401(30.31) | 28,395(7.73) | 72,435(19.71) | 19,941(5.43) | 367,566 | 8,316,947 | 4.42 |
| 2023 | 292,346(35.41) | 295,293(35.77) | 72,361(8.76) | 143,369(17.37) | 32,201(3.90) | 835,570 | 9,569,051 | 8.73 |
| Total | 1,146,313(45.13) | 753,340(29.66) | 160,267(6.31) | 383,234(15.09) | 97,148(3.82) | 2,540,302 | 49,974,840 | 5.08 |
Abbreviations, OPD/ED, number of outpatients and emergency department visits
Fig. 1.
Distribution characteristics of ILI% in Zhejiang Province, 2018–2023. Abbreviations: Figure A shows the weekly distribution of ILI% for each year. Figure B shows the age composition of ILI% for each year
Among the ILI cases reported from the surveillance departments of sentinel hospitals, the 0–4 years group had the highest proportion (45.13%), followed by the 5–14 years group (29.66%), the 15–24 years group (6.31%), the 25–59 years group (15.09%), and the ≥ 60 years group (3.82%). From 2018 to 2022, the age distribution of ILI cases was similar, with the 0–4 years group, 5–14 years group, 25–59 years group, 15–24 years group, and ≥ 60 years group in descending order. In 2023, the proportion of ILI cases in the 5–14 years group exceeded that of the 0–4 years group, as shown in Table 1; Fig. 1B.
The average ILI% was 4.43% before the COVID-19 pandemic (before January 2020), 3.81% during the pandemic (January 2020 to the end of 2022), and 8.22% after the pandemic (the end of 2022 and after), showing a significant increase post-pandemic (χ² = 320,224.182, P < 0.001), as illustrated in Fig. 2.
Fig. 2.
The incidence of ILI in Zhejiang Province from 2018 to 2023
Pathogenic monitoring and analysis of ILI specimens
From 2018 to 2023, a total of 126,439 ILI samples were tested in the province, with 21,871 testing positives for influenza virus’s gene, resulting in a positive rate of 17.30%. The positive rate in 2020 was the lowest (5.88%), and the highest in 2023 (28.48%), with significant differences across different years (χ² = 6,862.021, P < 0.001). Dominant strains varied by year: A (H1N1) pdm09 in 2018, B/Yamagata in 2018, B/Victoria in 2019 and 2020, A (H1N1) pdm09 and A (H3N2) co-circulating in 2019 and 2020, B/Victoria in 2021, A (H3N2) in 2022, and A (H3N2) and A (H1N1) pdm09 co-circulating in 2023, as shown in Table 1; Fig. 3A. The detected rate of co-infection was 0.018% in all ILI samples, and 0.11% in positive samples, including 10 A (H1N1) pdm09 combined with A (H3N2), 8 A (H1N1) pdm09 combined with B/Yamagata, 5 A (H1N1) pdm09 combined with B/Victoria.
Fig. 3.
Distribution of influenza virus subtypes in Zhejiang Province from 2018 to 2023. Abbreviations: Figure A shows the composition of influenza virus subtypes in each year. Figure B shows the composition of influenza virus subtypes in different age groups.
Among different age groups, the 5–14 years group had the highest positive rate (26.01%), while the 0–4 years group had the lowest (9.11%), with significant differences across all age groups (χ² = 3,186.491, P < 0.001). There was no significant difference in the positive rate of influenza virus detection between sex (χ² = 9.851, P = 0.002). Statistically significant differences were observed in the composition of influenza subtypes across age groups (χ² = 876.661, P < 0.001), as shown in Table 2; Fig. 3B. Except for 2020, the detection of influenza virus exhibited a seasonal pattern in other years. In 2023, the influenza epidemic entered a period of rapid increase from week 36, which was earlier than that before COVID-19 pandemic, as depicted in Fig. 4
Table 2.
Etiological surveillance results of ILI samples in Zhejiang province, 2018–2023
| Surveillance years | Number of tests | Number of positive | Positive rate (%) | The number of positive results of influenza virus subtypes [n (%)] | ||||
|---|---|---|---|---|---|---|---|---|
| A(H1N1)pdm09 | A(H3N2) | B/Victoria | B/Yamagata | Co-infection | ||||
| 2018 | 17,751 | 2669 | 15.04 | 1564(58.60) | 143(5.36) | 176(6.59) | 775(29.04) | 11(0.41) |
| 2019 | 19,334 | 5215 | 26.97 | 1853(35.53) | 1372(26.31) | 1972(37.81) | 15(0.29) | 3(0.06) |
| 2020 | 20,401 | 1200 | 5.88 | 184(15.33) | 432(36.0) | 581(48.42) | 0(0) | 3(0.25) |
| 2021 | 23,549 | 1765 | 7.50 | 0(0) | 0(0) | 1764(99.94) | 1(0.06) | 0(0) |
| 2022 | 22,970 | 4633 | 20.17 | 0(0) | 2934(63.33) | 1699(36.67) | 0(0) | 0(0) |
| 2023 | 22,434 | 6389 | 28.48 | 2577(40.33) | 3459(54.14) | 347(5.43) | 0(0) | 6(0.09) |
| Total | 126,439 | 21,871 | 17.30 | 6178(28.25) | 8340(38.13) | 6539(29.90) | 791(3.62) | 23(0.11) |
Fig. 4.
Distribution of influenza positive cases and subtypes in Zhejiang Province from 2018 to 2023
Correlation analysis of ILI and positive rate of nucleic acid
As shown in Fig. 5, the positive rate curve of influenza virus nucleic acid detection in the 0–4 years group did not align with the ILI curve after the COVID-19 pandemic. In 2020, the peaks and troughs of the positive rate curve were more pronounced than those of the ILI curve. The positive rate curve of influenza virus nucleic acid detection in the 5–14 years and ≥ 15 years groups were relatively consistent with the ILI curve. Spearman correlation analysis showed that the correlation between ILI and the positive rate of influenza virus nucleic acid detection in the 0–4 years group was not statistically significant from 2020 to 2022 (rs = −0.035, 0.223, 0.256; P = 0.805, 0.113, 0.067), but showed a positive correlation in 2018–2019 and 2023. The correlation for the 5–14 years group was not statistically significant in 2020 (rs = 0.048; P = 0.731), but was positively correlated in other years. The correlation between ILI and the positive rate of influenza virus among ≥ 15 years group was statistically significant in each year Table 3.
Fig. 5.
The correlation between the number of ILI and the positive rate of influenza virus in Zhejiang Province from 2018 to 2023. Abbreviations: Figure A shows the correlation graph for the age group of 0–4 years old, Figure B for the age group of 5–14 years old, and Figure C for the age group of 15 years old and above
Table 3.
Sex and age specific detection of influenza virus in ILI in Zhejiang province, 2018–2023
| Groups | Number of tests | Number of positive | Positive rate (%) | The number of positive results of influenza virus subtypes [n (%)] | ||||
|---|---|---|---|---|---|---|---|---|
| A(H1N1)pdm09 | A(H3N2) | B/Victoria | B/Yamagata | Co-infection | ||||
| Sex | ||||||||
| Male | 61,754 | 10,893 | 17.64 | 3034(27.85) | 4221(38.75) | 3277(29.62) | 348(3.19) | 13(0.12) |
| Female | 64,685 | 10,978 | 16.97 | 3144(28.64) | 4119(37.52) | 3262(29.71) | 443(4.04) | 10(0.09) |
| Age groups | ||||||||
| 0–4 | 27,448 | 2501 | 9.11 | 1038(41.50) | 764(30.55) | 590(23.59) | 107(4.28) | 2(0.08) |
| 5–14 | 26,801 | 7013 | 26.17 | 1582(22.56) | 2881(41.08) | 2359(33.64) | 182(2.60) | 9(0.13) |
| 15–24 | 17,871 | 3416 | 19.11 | 833(24.39) | 1651(48.33) | 783(22.92) | 148(4.33) | 1(0.03) |
| 25–59 | 43,214 | 7909 | 18.30 | 2340(29.59) | 2613(33.04) | 2682(33.91) | 268(3.39) | 6(0.08) |
| ≥ 60 | 11,105 | 1032 | 9.29 | 385(37.31) | 431(41.76) | 125(12.11) | 86(8.33) | 5(0.48) |
Discussion
Influenza viruses are prone to mutation, necessitating long-term and systematic monitoring to identify their epidemic patterns and mutations promptly, and to inform the selection of vaccine strains and disease prevention and control strategies [13]. The results showed that both ILI% and the positive rate of influenza virus detection in Zhejiang Province in 2020 decreased significantly compared with that before the COVID-19 pandemic, remaining at a low epidemic level throughout the year without obvious seasonality, which was similar to the national influenza epidemic situation during the same period [14]. This may be attributed to the implementation of stringent nonpharmaceutical interventions (NPIs) against the COVID-19 in China from January 20, 2020, and the adoption of various public health prevention and control measures, which effectively prevented and controlled the COVID-19 outbreak and curtailed the spread of influenza [15, 16]. In 2021, the COVID-19 epidemic in China stabilized, and social production and life gradually returned, people’s awareness of prevention and control waned, leading to a resurgence in the influenza epidemic, with a peak in winter and spring. The influenza epidemic in 2022 featured three peaks in July, December, and March of the following year, with the positive rate of influenza virus nucleic acid detection similar to that of 2019 before the COVID-19 epidemic. The July peak may be related to factors such as the geographical location of Zhejiang Province [17]. Zhejiang Province is located in the southeast coastal area, during summer, it is often affected by typhoons and the rainy season, resulting in a decrease in temperature, which provides ideal conditions for the survival and spread of influenza viruses. Additionally, July is the summer vacation period in China, increased population mobility activities such as traveling and gatherings have accelerated the spread of influenza virus. ILI% showed a rapid upward trend from November 2022, peaking in December, but the positive detection rate of influenza virus during the same period was very low, suggesting that the rapid increase in ILI% maybe associated with the rapid and high-intensity spread of the COVID-19, and the majority of ILI patients were infected with SARS-CoV-2 [18]. Research findings reveal that in the context of the COVID-19 pandemic, influenza surveillance should integrate the consistency between ILI% and the positive detection rate of influenza virus, and simultaneously test for both influenza virus and SARS-CoV-2 in ILI and SARI samples to monitor the impact of COVID-19. In early 2023, the number of ILI cases in Zhejiang Province increased rapidly and peaked in March, primarily driven by A (H1N1) influenza viruses. This may be related to the easing of COVID-19-related NPIs of China on January 8, 2023, and the gradual resumption of social order and travel activities following the adjustment of various control measures. Additionally, the immunological gap related to the COVID-19 [19], the reduced immune function caused by SARS-CoV-2 [20, 21], and seasonal factors [22] are also important reasons for the post-COVID-19 influenza outbreak. Similar “triple outbreaks” of influenza, respiratory syncytial virus (RSV), and the COVID-19 have been observed in Europe and the United States since the release of the COVID-19 pandemic [23]. From a population distribution perspective, ILI cases in Zhejiang Province were predominantly concentrated in children under 5 years old, with fewer cases among the elderly. This is because children, due to their weaker immune systems, are a high-risk group for influenza infection and typically seek medical attention for symptoms such as fever and cough, whereas the elderly may seek treatment for other symptoms [24].
Etiological monitoring results showed that the positive rate of influenza virus varied across different years, with different dominant strains, exhibiting a general pattern of alternating A and B types. The primary reason may be that when a particular strain is circulating, the population gains some immunity, reducing the likelihood of reinfection with the same strain in subsequent years. Influenza virus positive rates were highest in children under 15 years old. Among younger populations, the proportions of influenza A and B were higher, while in older populations, the proportion of A (H3N2) was higher and the proportion of influenza B was lower. These findings are consistent with previous studies [25]. Notably, influenza B (Yamagata) cases were detected in 2018–2019 but were nearly absent during the COVID-19 pandemic, which was basically in line with the global epidemic trend of influenza B (Yamagata) [26]. This possibly related to the inherent vulnerability of this lineage. Indeed, the B (Yamagata) lineage has low antigenic diversity [27], lower effective reproduction number, and a shorter transmission chain compared with the B (Victoria) lineage [26], making non-pharmaceutical interventions more effective in controlling the B (Yamagata) lineage. Furthermore, the previously long-lived clades of B (Yamagata) went extinct maybe another reason for the decline of B (Yamagata) [28]. Lastly, in 2008–2019, the B (Yamagata) lineage demonstrated greater global movement than B (Victoria) lineage [28]. The B (Yamagata) lineage may have already been at a low prevalence cycle at the beginning of COVID-19 pandemic.
Influenza surveillance is conducted globally according to the ILI definition recommended by WHO. Theoretically, the positive rate of influenza virus nucleic acid detection should be positively correlated with the number of ILI cases. However, this study found that after the pandemic, the positive rate curve of influenza virus nucleic acid detection in the 0–4 years group did not align with the ILI curve, with more pronounced peaks and troughs. In particular, in 2020, after the implementation of strict prevention and control measures for the COVID-19 epidemic, the correlation between ILI number and the influenza virus positive rate has no significant difference in children under 15 years old, suggesting that children and infants often have multiple respiratory pathogen infections, the current definition of ILI may not be suitable for influenza surveillance in children.
After the COVID-19 pandemic, respiratory syncytial virus, adenovirus, mycoplasma pneumoniae, and other respiratory infectious diseases have superimposed in China [29], presenting new challenges for influenza prevention and control. Influenza vaccination remains the most effective means for preventing influenza, and efforts should be made to improve vaccination rates through optimized vaccination services and policy advocacy, particularly focusing on key groups such as healthcare workers, the elderly, young children, and individuals with chronic diseases. Public health authorities should enhance the dissemination of influenza prevention and control knowledge, timely issue influenza epidemic warnings and risk assessments, and raise public awareness of self-prevention and control. Emphasizing the mechanism of multi-disease prevention and control for respiratory infectious diseases and incorporating respiratory multi-pathogen surveillance into the influenza sentinel surveillance network is crucial for the precise prevention and control of influenza and other respiratory infectious diseases.
To ensure the quality and level of influenza network surveillance, the Zhejiang Provincial Center for Disease Control and Prevention annually evaluated the quality of influenza surveillance work of collaborating laboratories and sentinel hospitals in various cities every year. The evaluation mainly includes the completeness and timeliness of the monitoring data report, pathogen detection, specimen collection, and other related aspects. The influenza monitoring tasks in all cities were all completed satisfactorily. However, our study still has some limitations. First, we did not test for any pathogens other than influenza, which prevented us from ruling out other viral, bacterial, and fungal pathogens that could cause ILI. So, it is difficult to determine whether the number of ILI cases is caused by SARS-CoV-2. Second, due to the unavailability of influenza vaccination rates across years and age groups of Zhejiang province, leading us unable to conform the exact impact of the COVID-19 pandemic on influenza.
Conclusions
The epidemic characteristics of influenza in different stages of COVID-19 prevention and control from 2018 to 2023 are different. By improving the quality of ILI report, specimen collection and timely adjustment of surveillance program, a more scientific and effective effect of influenza surveillance can be obtained. Children under 15 years are the focus group for influenza prevention and control. Additionally, the typical seasonal patterns of respiratory viruses might be changed after COVID-19 pandemic, which should be brought great attentions of clinicians. It is necessary to further strengthen the surveillance of influenza cases, pay close attention to the change rules of influenza virus strains, actively promote the influenza vaccination of key groups, and promote the change of health behaviors of the whole population.
Acknowledgements
Not applicable.
Authors’ contributions
HY, ZY: conceptualized and designed this study. HY, GP C and ZY: collected the data. HY, QG: conducted the analysis and wrote the manuscript. HY, BD Z: revised the manuscript. All authors reviewed the manuscript.
Funding
This study was supported by the Science and Technology Programme Project of Zhejiang Provincial Bureau of Disease Control and Prevention (2025JK308).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Our research follows the requirements of the Helsinki Declaration. The use of this database was reviewed and approved by the Ethics Review Committee of the Zhejiang Provincial Center for Disease Control and Prevention, IRB No. 2024-091-01. Due to the fact that our data comes from national routine monitoring projects and identifiable information has been removed, the Ethics Review Committee of the Zhejiang Provincial Center for Disease Control and Prevention has exempted informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hui Yang and Qing Gao contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.