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. 2026 Jul 13;26:1782. doi: 10.1186/s12879-026-13938-8

Spatiotemporal epidemiology of influenza in Fujian Province: an 18-year observational study

Minhua Li 1,#, Shaojian Cai 2,#, Jiake Chen 3,#, Lu Qin 1, Meirong Zhan 2, Jinzhang Wang 2, Yu Jiang 1, Chuancheng Wu 1,✉, Jianjun Xiang 1,✉
PMCID: PMC13644170  PMID: 42443781

Abstract

Objective

To investigate the spatiotemporal epidemiological characteristics and temporal trends of influenza in Fujian Province, China, from 2005 to 2022.

Methods

Descriptive epidemiological methods were used to analyze the temporal, demographic, occupational, and regional distribution of influenza cases. Spatial autocorrelation analysis, trend surface analysis, and spatiotemporal scan statistics were applied to explore the spatial distribution and clustering characteristics of influenza.

Results

A total of 215,338 influenza cases were reported in Fujian Province during 2005–2022, with the incidence rate showing an overall fluctuating upward trend over time (Z = 4.394, P < 0.001). Children aged 0–5 years and students accounted for the largest proportions of reported cases. Influenza activity demonstrated marked temporal and seasonal variation, with relatively higher activity observed in summer and winter. Trend surface analysis indicated higher case concentrations in eastern and central regions of Fujian Province. Significant positive spatial autocorrelation was identified in multiple years, and six significant spatiotemporal clusters were detected, mainly distributed in southeastern Fujian.

Conclusion

Influenza in Fujian Province showed an increasing annual incidence trend, marked temporal fluctuation, and dynamic spatial clustering during 2005–2022. These findings may support more targeted influenza surveillance, prevention, and resource allocation in high-risk populations and areas.

Keywords: Influenza, Spatiotemporal clustering, Spatial autocorrelation, Trend analysis

Introduction

Influenza is an acute respiratory infectious disease caused by the influenza viruses and remains a major public health concern worldwide [1]. Owing to its high transmissibility, frequent antigenic variation, and recurrent seasonal epidemics, influenza can cause substantial morbidity, mortality, healthcare burden, and social disruption each year [2, 3].

The epidemiological patterns of influenza are complex and may vary across time, population groups, and geographic regions [4, 5]. Understanding the temporal and spatial distribution of influenza is essential for strengthening surveillance, early warning, and targeted prevention. Traditional descriptive analyses can identify overall temporal trends and high-risk populations, but they may not fully capture geographic heterogeneity or the dynamic clustering of influenza transmission [6, 7]. Spatial epidemiological methods, including spatial autocorrelation analysis and space–time scan statistics, provide useful tools for identifying high-risk areas and periods, detecting spatial clustering, and supporting more precise allocation of public health resources [8–10]. Therefore, integrating spatiotemporal analytical approaches into influenza surveillance may contribute to a more comprehensive understanding of transmission dynamics.

Fujian Province is located in southeastern China and has a large population, frequent population movement, and diverse geographic and climatic characteristics. These features make it an important region for influenza surveillance and prevention. Although influenza surveillance has been conducted continuously in Fujian Province, comprehensive studies integrating long-term temporal trends, population distribution, regional variation, and spatiotemporal clustering at a fine spatial scale remain limited [11]. Such evidence is needed to better characterize the evolving epidemiological patterns of influenza and to identify populations and areas that may require strengthened surveillance and control measures.

To address this gap, this 18-year observational study analyzed influenza surveillance data in Fujian Province from 2005 to 2022. We aimed to characterize the temporal trends, monthly and seasonal distribution of influenza, as well as its demographic distribution and regional variation. In addition, spatial analytical methods were applied to explore the spatial distribution and clustering characteristics of influenza. The findings of this study provide a better understanding of influenza epidemiological characteristics in Fujian Province and may support more targeted influenza surveillance, prevention strategies, and public health resource allocation in Fujian Province.

Materials and methods

Data sources

Influenza surveillance data for Fujian Province from January 1, 2005, to December 31, 2022, were obtained from the surveillance report management system of the China Center for Disease Control and Prevention’s information system. The dataset included information on reported influenza cases, including date of report, sex, age group, occupation, and residential administrative region. Demographic data was sourced from the National Bureau of Statistics, and geographical data were retrieved from the National Geographic Information Resource Catalog Service System. All data used in this study were de-identified and aggregated before analysis. No personal identifiers were available to the research team.

Statistical analysis

Descriptive epidemiological methods were used to analyze the temporal and regional distribution of influenza cases. Temporal trends in annual influenza incidence rates were evaluated using the non-parametric Mann–Kendall trend test, with corresponding Z statistics and P values reported. Monthly and seasonal distributions were described using monthly time-series plots, multi-year average monthly distributions, seasonal summaries, and year–month heatmaps. For visualization of monthly case counts across years with large differences in magnitude, logarithmic transformation was applied where appropriate.

Trend surface analysis was performed to examine the overall spatial distribution trends of influenza [12]. County-level cumulative reported case counts were projected onto the x–z and y–z planes to evaluate spatial distribution patterns in the west–east and south–north directions within the study area. Spatial autocorrelation of influenza incidence was assessed using both Global Moran’s I and Local Moran’s I statistics [13–15]. Spatial relationships among spatial units were defined using an inverse distance spatial weights matrix based on Euclidean distance, with a distance threshold of 52,264.54 m and no row standardization in ArcGIS.

Space–time scan analysis was conducted using a discrete Poisson model to identify spatiotemporal clusters of influenza cases from 2005 to 2022. The discrete Poisson model was selected because influenza data were available as aggregated case counts with corresponding population data for each spatial unit and time period. In this study, the spatial unit used for the analysis was the county-level administrative division. The data were preprocessed at the county-level spatial scale, with the time window interval set as “month” and no spatial overlap. The spatial cluster size was restricted to no more than 30% of the total population, and Monte Carlo simulations were performed with 999 replications. Regions with the highest log likelihood ratio (LLR) values and P < 0.05 were defined as primary clusters, while others were defined as secondary clusters [16].

Data were organized using Excel 2021. Visualizations were created using R version 4.3.0. Spatial autocorrelation analysis was conducted using ArcGIS version 10.8, and spatiotemporal scan analysis was performed using SaTScan version 10.1. A P-value of less than 0.05 was considered statistically significant.

Results

Annual epidemiological characteristics

From 2005 to 2022, a total of 215,338 influenza cases were reported in Fujian Province. As shown in Fig. 1, the annual incidence rate exhibited a statistically significant increasing trend over time, as assessed using the Mann–Kendall trend test (Z = 4.394, P < 0.001). The average annual incidence rate during the study period was 29.3 per 100,000 population, with the lowest incidence observed in 2007 (1.28 per 100,000) and the highest incidence recorded in 2022 (159.42 per 100,000). Several notable epidemic peaks were observed, particularly in 2009 (22.49 per 100,000), 2019 (87.78 per 100,000), and 2022 (159.42 per 100,000). More influenza cases were consistently reported among males than females throughout the study period.

Fig. 1.

Fig. 1

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Annual influenza cases by sex and overall incidence rate in Fujian Province, 2005–2022. Bars represent the annual number of reported influenza cases for males and females (×1,000). The green line represents the annual incidence rate per 100,000 population, calculated using yearly population data. Temporal trends in annual incidence rates were assessed using the non-parametric Mann–Kendall trend test

Monthly and seasonal distribution

Figure 2 illustrates the temporal distribution of influenza cases in Fujian Province from 2005 to 2022 using four complementary perspectives. As shown in Fig. 2A, which presents the complete monthly time series, influenza activity exhibited substantial temporal fluctuations across the study period. Relatively low and sporadic case numbers were observed during the early years (2005–2012), whereas progressively larger and more frequent peaks emerged in later years, particularly after 2013.

Fig. 2.

Fig. 2

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Monthly and seasonal distribution of influenza cases in Fujian Province, 2005–2022. (A) Monthly time series of influenza cases from January 2005 to December 2022. Monthly case counts were aggregated according to the reported date, and the y-axis is displayed on a log10 scale to improve visualization of variations across years. (B) Multi-year average monthly distribution of influenza cases from 2005 to 2022. Bars represent the average number of reported cases for each calendar month, while the line connects the monthly averages to facilitate visual comparison across months. (C) Multi-year average seasonal distribution of influenza cases from 2005 to 2022. Seasons were defined as spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). Bars represent the average number of reported cases for each season, while the line connects the seasonal averages to facilitate visual comparison across seasons. (D) Year–month heatmap of influenza cases from 2005 to 2022. Each cell represents the number of reported cases in a given month and year. Color intensity represents the number of monthly reported cases on a logarithmic (log10) scale, which was applied to improve visualization and reduce the influence of extreme peak values

Figure 2B summarizes the multi-year average monthly distribution of cases. The highest average case numbers were observed in June, indicating increased activity during this period. Relatively elevated levels were also observed in December and January, while lower activity occurred from August to October, suggesting marked intra-annual variation in influenza incidence. To further characterize these patterns at a broader temporal scale, Fig. 2C presents the average seasonal distribution of cases. Influenza activity was highest in summer, followed by winter, whereas autumn showed the lowest activity, indicating a clear seasonal variation in overall transmission intensity.

Figure 2D, the year–month heatmap, further illustrates interannual variation in monthly case distributions, highlighting increased activity in several later years, particularly in 2019 and 2022. Notably, in 2022, influenza activity persisted throughout the year, with a marked increase during June and July, during which a total of 50,709 cases were reported.

Age distribution

As shown in Fig. 3, influenza cases were reported across all age groups throughout the study period. Based on the cumulative reported cases from 2005 to 2022, the majority of cases occurred in the 0–5-year age group (68,212 cases, accounting for 31.7% of all reported cases during the study period), followed by the 5–10-year age group (61,457 cases, 28.5%) and the 10–20-year age group (33,059 cases, 15.4%). In terms of annual proportional distribution, from 2012 to 2015, the 0–5-year age group became the primary population affected by influenza, accounting for more than 40% of the reported cases in each of these years.

Fig. 3.

Fig. 3

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Age distribution of influenza cases in Fujian Province, 2005–2022. The stacked bar chart shows the annual percentage distribution of reported influenza cases across different age groups from 2005 to 2022. Each bar represents one year, and different colors indicate different age groups. The y-axis represents the percentage of cases within each year

Occupational distribution

Figure 4 presents the occupational distribution of influenza cases from 2005 to 2022. Based on the cumulative reported cases during the entire study period, the top three occupational groups with the highest proportions of cases were nursery/kindergarten children (90,005 cases, accounting for 41.8% of all reported cases), students (71,463 cases, 33.2%), and individuals working in agriculture, forestry, animal husbandry, fishery, and manufacturing sectors (30,426 cases, 14.1%). These occupational groups consistently represented the largest proportions of influenza cases across most years, although their annual proportional distributions varied over time.

Fig. 4.

Fig. 4

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Occupational distribution of influenza cases in Fujian Province, 2005–2022. The grouped bar chart shows the annual percentage distribution of reported influenza cases across different occupational groups from 2005 to 2022. Each group of bars represents one year, and different colors indicate different occupational categories. The y-axis represents the percentage of cases within each year

Regional distribution

Figure 5 illustrates the annual distribution of reported influenza cases across the 9 prefecture-level cities of Fujian Province from 2005 to 2022. Based on cumulative reported cases during 2005–2022, the top three cities were Zhangzhou (69,880 cases, accounting for 32.5% of all reported cases), Xiamen (51,191 cases, 23.8%), and Quanzhou (27,384 cases, 12.7%). Xiamen consistently reported relatively high numbers of influenza cases throughout the study period, particularly in 2022, when it experienced a peak in reported cases that exceeded those of other cities. Quanzhou and Zhangzhou also showed prominent fluctuations in annual case numbers during epidemic periods. In contrast, cities such as Fuzhou, Sanming, Ningde, and Longyan generally reported lower numbers of cases, with relatively smaller fluctuations over the observation period.

Fig. 5.

Fig. 5

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Annual distribution of influenza cases across the nine prefecture-level cities in Fujian Province, 2005–2022. The circular heatmap illustrates the annual number of reported influenza cases in each prefecture-level city from 2005 to 2022. Each concentric ring represents a calendar year, with the innermost ring corresponding to 2005 and the outermost ring corresponding to 2022. Each sector represents one prefecture-level city. Color intensity reflects the number of reported cases, with colors gradually transitioning from blue to red to indicate increasing case numbers

Spatial distribution and trend surface analysis

Figure 6 presents the three-dimensional spatial trend surface analysis of reported influenza cases in Fujian Province from 2005 to 2022. In this figure, the X-axis represents longitude, the Y-axis represents latitude, and the Z-axis represents the number of reported influenza cases. The projection on the X–Z plane shows the west–east spatial variation, with relatively lower-case counts in western areas and higher case concentrations toward eastern areas. The projection on the Y–Z plane shows the south–north spatial variation, with a curved pattern suggesting relatively higher case concentrations in central-latitude areas.

Fig. 6.

Fig. 6

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Three-dimensional spatial trend surface analysis of reported influenza cases in Fujian Province, 2005–2022. The X-axis represents longitude (°E), the Y-axis represents latitude (°N), and the Z-axis represents the number of reported influenza cases. Gray vertical points represent observed county-level cumulative case counts from 2005 to 2022. The blue points and fitted curve show the projection on the X–Z plane, reflecting west–east spatial variation, while the red points and fitted curve show the projection on the Y–Z plane, reflecting south–north spatial variation. Quadratic polynomial curves were fitted to visualize the spatial trend pattern

Global and local spatial autocorrelation

Table 1 summarizes the results of the global spatial autocorrelation analysis of influenza in Fujian Province from 2005 to 2022. Significant positive spatial autocorrelation was observed in 2009, 2010, and 2013–2022, indicating persistent spatial clustering of influenza cases during these periods. In contrast, no significant spatial autocorrelation was detected in 2005–2008 and 2011–2012, suggesting a spatially random distribution of cases in those years.

Table 1.

Global spatial autocorrelation analysis of influenza in Fujian Province from 2005 to 2022

Years Moran’s I Variance Z-value P-value
2005 0.033 0.005 0.647 0.518
2006 0.131 0.005 1.939 0.052
2007 0.116 0.005 1.754 0.079
2008 -0.021 0.004 -0.125 0.901
2009 0.261 0.005 3.825 < 0.001
2010 0.247 0.004 3.918 < 0.001
2011 0.016 0.004 0.452 0.651
2012 0.094 0.005 1.443 0.149
2013 0.164 0.005 2.395 0.017
2014 0.261 0.006 3.550 < 0.001
2015 0.294 0.006 4.006 < 0.001
2016 0.219 0.004 3.549 < 0.001
2017 0.277 0.005 4.111 < 0.001
2018 0.523 0.005 7.376 < 0.001
2019 0.718 0.006 9.454 < 0.001
2020 1.097 0.005 15.059 < 0.001
2021 0.269 0.004 4.235 < 0.001
2022 1.239 0.005 16.907 < 0.001
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Note: Global Moran’s I statistics were calculated in ArcGIS using an inverse distance spatial weights matrix based on Euclidean distance, with a distance threshold of 52,264.54 m and no row standardization. Under this spatial weights specification, the attainable range of Moran’s I depends on the distance-based weight structure and is not strictly limited to the range of − 1 to + 1. Statistical significance was evaluated using the corresponding Z-values and P-values

To further explore the spatial patterns identified in the global analysis, local spatial autocorrelation analysis was conducted using Local Moran’s I statistics, as shown in Fig. 7. Only years with statistically significant local spatial autocorrelation were displayed, while years without significant spatial clustering were not presented. The analysis identified distinct high–high clusters (areas with high incidence) and low–low clusters (areas with low incidence) in several years, particularly in 2009, 2010, and 2013–2022. High–high clusters were frequently observed in southern regions, particularly around Zhangzhou and Quanzhou, while low–low clusters were mainly identified in northwestern areas, including Sanming and Nanping. In addition, the spatial distribution of clustering varied across years, indicating dynamic changes in local spatial patterns over time.

Fig. 7.

Fig. 7

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Year-specific local spatial autocorrelation of influenza at the township level in Fujian Province, China. Local spatial autocorrelation was assessed using Local Moran’s I statistics for each year from 2005 to 2022. Each map represents the spatial clustering pattern of influenza incidence at the township level for years in which statistically significant local spatial autocorrelation was detected. Years without significant local spatial autocorrelation are not displayed

Spatiotemporal clustering analysis

The space time scan analysis identified six significant clusters, as shown in Fig. 8. The most likely cluster was located in southeastern Fujian, covering 24 counties. The center of the primary cluster was at 24.784846°N, 118.108184°E, with a cluster radius of 39.74 km. The incidence rate within this cluster was 1.99 times higher than that in the surrounding areas (RR = 1.99, P < 0.001), and the cluster duration was from June 2022 to July 2022.

Fig. 8.

Fig. 8

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Spatiotemporal clusters of influenza cases in Fujian Province identified using space–time scan statistics at the county level. Six statistically significant clusters were detected during the study period. The most likely cluster is shown in dark red, while secondary clusters are displayed in progressively lighter colors according to their order of significance. The circles represent the spatial extent of each detected cluster, and the shaded areas indicate counties included within each cluster. Areas not included in significant clusters are shown in beige

In addition, five statistically significant secondary clusters were detected in other regions. The second cluster was located in the northwestern part of Fujian Province, covering seven counties (RR = 23.12, P < 0.001), while the third cluster was identified in the northeastern part, covering two counties (RR = 6.86, P < 0.001). The remaining secondary clusters were smaller in spatial extent and are presented in Fig. 8.

Discussion

This study characterized the temporal trends, population distribution, and spatiotemporal clustering patterns of influenza in Fujian Province from 2005 to 2022. The findings showed that influenza incidence exhibited an overall increasing trend with marked interannual and seasonal fluctuations. Children aged 0–5 years and students accounted for the largest proportions of reported cases. Spatial analyses further identified persistent and dynamic clustering patterns, with relatively high-risk areas mainly concentrated in southeastern coastal regions. These results suggest that influenza activity in Fujian Province was not randomly distributed over time or space, but showed clear epidemiological shifts and geographic heterogeneity.

From a temporal perspective, the influenza incidence rate in Fujian Province exhibited an overall fluctuating upward trend, with notable epidemic peaks in 2009, 2019, and 2022. The peak in 2009 was consistent with the global H1N1 influenza pandemic, while the peak in 2019 was associated with the emergence of COVID-19, reflecting the substantial impact of global epidemic events on regional epidemiological patterns [17, 18]. The residual peaks in 2017 and 2018 showed an increase in influenza case numbers, suggesting the presence of notable short-term fluctuations during these years. Studies based on influenza surveillance data in China and globally have indicated that the 2017–2018 influenza season exhibited unusually elevated epidemic intensity, primarily associated with the co-circulation of Influenza A(H3N2) and B/Victoria lineage [19, 20]. Moreover, this strain combination has been suggested to potentially reduce vaccine matching, thereby increasing the risk of infection in the population to some extent [21]. Meanwhile, previous multicenter epidemiological studies conducted in China and other subtropical regions have found that interannual variations in meteorological conditions may be associated with fluctuations in influenza activity. For example, some studies have reported anomalous changes in temperature and humidity in certain years in parts of southern China, and climatic factors are considered to potentially modulate influenza epidemic intensity by influencing viral survival and transmission processes [22, 23].

The influenza incidence level in 2020–2021 declined compared with that in 2019, which was likely related to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic, such as mask-wearing, reduced population mobility, and social distancing measures. These interventions may have suppressed the transmission of the influenza virus to some extent [24, 25]. Notably, the influenza incidence rate in 2022 was substantially higher than in previous years, forming an unusually prominent epidemic peak. This phenomenon is unlikely to be attributed to a single factor, but rather to the combined effects of multiple mechanisms. First, from the perspective of actual epidemic dynamics, as non-pharmaceutical interventions were gradually relaxed in the later stage of the COVID-19 pandemic, the resumption of population movement and increased social contact may have led to a rapid exposure of the susceptible population accumulated during the earlier period, thereby generating a rebound effect and amplifying the epidemic scale [26–28]. In addition, improvements in influenza surveillance in China after the COVID-19 pandemic, including the standardization of reporting procedures, digital upgrades of information systems, and enhanced laboratory testing capacity, may have increased the detection rate and reporting completeness of cases in 2022 [29].

Based on the analysis of monthly and seasonal distributions, this study found that influenza cases were generally higher in summer, with the highest average number of cases observed in June. This phenomenon may be related to the combined effects of high temperature and high humidity conditions on viral transmission. Previous studies have shown that under higher humidity conditions, the evaporation, deposition, and stability of respiratory droplets may change, thereby affecting the efficiency of virus transmission during close contact [30–32]. In addition, short-term meteorological events such as rainfall and typhoons occur more frequently in Fujian during summer. These weather processes may temporarily alter population activity patterns, such as increasing time spent indoors and opportunities for close contact, thereby facilitating viral transmission [33, 34]. Meanwhile, under high-temperature conditions, the use of air conditioning increases, leading to greater congregation in relatively enclosed indoor environments with limited ventilation, which may further elevate the risk of respiratory virus transmission [35, 36]. Therefore, the higher level of influenza activity in summer may result from the combined effects of meteorological conditions and changes in human behavior.

In terms of population distribution, this study found that children aged 0–5 years and students represented the main high-risk groups for influenza. This finding may be driven by multiple factors. First, from a biological perspective, the immune system of children is not yet fully developed, and their immunological memory against influenza viruses is relatively limited, making them more susceptible to infection and more likely to develop clinical symptoms [37]. Second, from a social and behavioral perspective, collective settings such as kindergartens and schools are characterized by high population density and frequent close contact, providing favorable conditions for influenza virus transmission [38, 39]. Furthermore, the overall influenza vaccination coverage in China remains relatively low, which may, to some extent, increase the disease burden among children. Previous studies have shown that influenza vaccination coverage in the general population in China has remained at a relatively low level for a long period. For example, a nationwide multi-regional survey reported that influenza vaccination coverage among children in China was only approximately 10%–30% in most years, with substantial regional variation; in some non-urban areas, the coverage was even lower [40–44]. This relatively limited vaccine coverage implies that a large proportion of children remain susceptible, thereby increasing the risk of influenza transmission and outbreaks.

In addition to vaccination-related factors, potential influences of surveillance and healthcare-seeking behavior should also be considered. Children with symptoms such as fever and cough are more likely to be promptly taken to healthcare facilities by their parents, and are therefore more likely to be captured by surveillance systems. In contrast, adults infected with influenza are more likely to self-medicate or not seek medical care, particularly when symptoms are mild, which may lead to an underestimation of adult cases in surveillance data [45, 46].

In terms of spatial distribution, this study identified significant spatial clustering of influenza incidence in Fujian Province based on spatial autocorrelation and spatiotemporal cluster analyses. High–high clusters were mainly distributed in the southeastern coastal regions, such as Zhangzhou, Xiamen, and Quanzhou, whereas low–low clusters were more commonly observed in inland areas, such as Sanming and Nanping. This spatial pattern may be associated with climatic differences across regions in Fujian Province. For example, the southeastern coastal areas, including Zhangzhou and Quanzhou, belong to the southern subtropical climate zone, whereas the northeastern, northern, and western regions belong to the mid-subtropical climate zone [47]. The relative humidity in the southeastern coastal regions is generally higher than that in inland areas, and such differences in humidity may influence the environmental survival and transmission pathways of respiratory viruses, thereby shaping the spatial distribution of influenza to some extent [32, 48]. In addition, higher population density and more frequent population movement in coastal cities may further facilitate virus transmission, contributing to the higher levels of influenza incidence observed in these regions [49].

Limitations

This study has several limitations. First, due to dataset limitations, regionally consistent climate data and population density data with comparable spatial and temporal resolutions covering the entire study period were unavailable. Therefore, reliable quantitative spatial correlation analyses between these factors and influenza incidence could not be conducted within the current study framework. The discussion regarding climatic influences was mainly based on evidence from previous studies rather than direct evidence from the current study.

Second, the study spans a relatively long period, during which the surveillance system, case definitions, and reporting capacity may have changed. However, these potential changes were not quantitatively adjusted for in the present study and may, to some extent, affect the comparability of temporal trends. Third, this study was based on aggregated surveillance data and lacked individual-level information, such as vaccination status, healthcare-seeking behavior, and viral strain characteristics, thereby limiting more detailed analyses of population differences. Future studies integrating meteorological, demographic, virological, and individual-level data may help further explain the observed spatiotemporal patterns of influenza.

Conclusion

This 18-year observational study characterized the temporal trends and spatiotemporal clustering patterns of influenza in Fujian Province from 2005 to 2022. Influenza incidence showed an overall increasing trend with marked interannual and seasonal fluctuations, and notable epidemic peaks were observed in 2009, 2019, and 2022. Children aged 0–5 years and students accounted for the largest proportions of reported cases, indicating the need for strengthened surveillance and prevention in childcare and school settings. Spatial analysis indicated persistent high-risk clustering in the southeastern coastal regions, with the extent of clustering showing dynamic changes over time. These findings highlight the importance of integrating temporal variation, population distribution, and spatial clustering into influenza surveillance and control, and may support more targeted allocation of public health resources in high-risk populations and areas.

Acknowledgements

The authors gratefully acknowledge the Fujian Provincial Center for Disease Control and Prevention for providing the influenza surveillance data used in this study.

Author contributions

M.L. contributed to data curation, formal analysis, manuscript revision, and figure preparation during the revision and resubmission stage. S.C. was responsible for data collection. J.C. conceived the study and drafted the original manuscript. L.Q. contributed to data analysis. M.Z., J.W., and Y.J. provided supervision. C.W. and J.X. contributed to study conceptualization, supervision, and manuscript review and editing. All authors reviewed and approved the final manuscript.

Funding

This work was supported by the Minjiang Scholar Start-up Research Fund of Fujian Province (Grant No. 2019-9202001001) and the 2021 Fujian Province Health and Technology Plan Projects (2021GGB010).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was ethically approved by the Biomedical Research Ethics Committee of Fujian Medical University (Approval No. 2022 − 111). All methods employed in this study were strictly carried out in accordance with the principles outlined in the Declaration of Helsinki. The data provided for this study were de-identified and aggregated before analysis, and no personal identifiers were accessible to the research team. All analyses were conducted at aggregated temporal and spatial levels, and no direct contact, intervention, recruitment, or biological sampling of individual participants was involved. In accordance with the requirements of the institutional review board, the requirement for individual informed consent was waived because the study involved only secondary analysis of anonymized and aggregated surveillance data and posed minimal risk to individuals.

Consent for publication

Not application.

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.

Minhua Li, Shaojian Cai and Jiake Chen contributed equally to this work.

Contributor Information

Chuancheng Wu, Email: zhwcy@fjmu.edu.cn.

Jianjun Xiang, Email: jianjun.xiang@fjmu.edu.cn.

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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 analyzed during the current study are available from the corresponding author on reasonable request.

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