Prevalence and Distribution of Respiratory Pathogens in Paediatric Acute Respiratory Infections After the Cessation of Strict Non-Pharmaceutical Interventions in Putian, China.

Prevalence and Distribution of Respiratory Pathogens in Paediatric Acute Respiratory Infections After the Cessation of Strict Non-Pharmaceutical Interventions in Putian, China. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Distribution of Respiratory Pathogens in Paediatric Acute Respiratory Infections After the Cessation of Strict Non-Pharmaceutical Interventions in Putian, China. Jinwei Zhu, Suqing Wu, Yan Chen, Liping Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5425847/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 4 You are reading this latest preprint version Abstract Background Acute respiratory infections (ARIs) are a significant cause of morbidity in children. This study aimed to investigate the prevalence and distribution of respiratory pathogens in paediatric ARIs in Putian, China, following the cessation of strict non-pharmaceutical interventions (NPIs). Methods A total of 3,790 paediatric patients with suspected ARIs were included in the study. Nasopharyngeal swabs were collected and analyzed using RT-PCR to identify 13 common respiratory tract pathogens. Statistical analyses were performed to examine the distribution of pathogens among patients stratified by sex, age, and season. Results The overall pathogen positivity rate was 78.9%. No significant difference in detection rates was observed between males (79.7%) and females (77.9%). The highest positivity rate was found in the school-age group, with elevated rates noted during autumn and winter. Among the positive cases, 81.9% had a single pathogen, with Mycoplasma pneumoniae (Mp) being the most common (33.6%), followed by Human rhinovirus (HRV) and Human respiratory syncytial virus (HRSV). Age-dependent distribution indicated that Influenza A (InfA) was more prevalent in preschool and school-age children, whereas HRSV was most prevalent in infants. Temporal distribution showed that InfA peaked in spring, while Mp, Human metapneumovirus (HMPV), and Human adenovirus (HADV) were most common in winter. Co-infections were more frequent in autumn and winter, with the HRV + Mp co-infection being the most prevalent pattern. Conclusion The prevalence of respiratory pathogens in children with ARI has returned to pre-COVID-19 pandemic levels following the discontinuation of stringent NPIs. Additionally, the epidemiology of certain pathogens has shifted from traditional patterns. These findings underscore the dynamic nature of respiratory pathogen distribution and highlight the necessity for ongoing surveillance to inform effective treatment and prevention strategies for ARIs in children. prevalence Acute Respiratory infections Pathogens Pediatric Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Acute Respiratory Infections (ARIs) pose a significant burden on healthcare systems, particularly affecting pediatric populations[ 1 ]. Globally, young children experience between 5 and 8 episodes of ARIs annually[ 2 ], making these infections one of the leading causes of childhood morbidity and mortality. ARIs can be classified into two categories: acute upper respiratory tract infections (AURI) and acute lower respiratory tract infections (ALRTI), both of which are commonly caused by viral or bacterial pathogens. According to the United Nations International Children's Emergency Fund (UNICEF)[ 3 ], acute respiratory infections are the primary cause of death among children under the age of 5. The pathogens responsible for ARIs include both viruses and bacteria, which often present overlapping clinical features. Viral infections, in particular, can trigger host immune responses that facilitate bacterial growth, complicating diagnosis and leading to frequent misuse of antibiotics[ 4 ]. Research indicates that approximately 50% of ARI cases are misdiagnosed as bacterial infections, contributing to unnecessary antibiotic use[ 5 ]. Therefore, the epidemiological study of respiratory pathogens is essential for accurate diagnosis and appropriate treatment. Recent advancements in molecular diagnostic techniques, such as reverse-transcription polymerase chain reaction (RT-PCR), have significantly enhanced the detection of respiratory pathogens[ 6 ], leading to the identification of a diverse array of viruses and bacteria associated with ARIs. Notably, pathogens such as respiratory syncytial virus (RSV), influenza viruses, rhinoviruses, adenoviruses are frequently detected in children with ARIs[ 7 , 8 ]. Influenza, characterized by its well-established seasonal peaks, and Mycoplasma pneumoniae, a common cause of bacterial ARIs, are of particular concern[ 8 , 9 ]. Furthermore, air quality and temperature have been demonstrated to influence the incidence of respiratory infections[ 10 ]. China, with its vast geographical expanse and diverse climates, exhibits regional variations in the prevalence and seasonal patterns of respiratory pathogens. Research conducted in cities such as Beijing, Shanghai, and Chengdu has revealed differing epidemiological profiles of acute respiratory infection (ARI) pathogens, highlighting significant regional disparities in pathogen distribution and seasonal trends[ 11 – 13 ]. However, there is a paucity of research regarding the prevalence of respiratory pathogens in Putian, a city situated on the southeast coast of China. Putian is characterized by its distinctive subtropical marine climate, which significantly influences the seasonal distribution of respiratory infections. In light of these factors, our study was designed to investigate the prevalence and distribution of respiratory pathogens among pediatric patients with acute respiratory infections (ARIs) in Putian, China, following the cessation of strict non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic. These NPIs included social distancing, quarantine, lockdowns, mask-wearing, hand hygiene, school and workplace closures, travel restrictions, and the cancellation of mass gatherings. This study provides a comprehensive analysis of respiratory pathogen prevalence and seasonal trends, offering valuable insights into the post-NPI era and informing targeted strategies for the management of ARIs in children. Methods Study Population and Sample Collection This study was conducted at the Affiliated Hospital of Putian University, located in Putian City, Fujian Province, China. Patients presented with clinical manifestations indicative of acute respiratory infections, including cough, pharyngitis, and influenza-like symptoms (e.g., pyrexia, malaise, cephalgia, nasal obstruction, and rhinorrhea), as well as respiratory distress. The inclusion criteria were as follows: (1) inpatients under 14 years of age; (2) inpatients diagnosed with acute respiratory infections (ARI) admitted between March 2023 and February 2024; and (3) medical records must contain comprehensive results from nasopharyngeal swabs. The exclusion criteria included: (1) immunocompromising conditions or immunodeficiencies; (2) patients diagnosed with COVID-19; (3) congenital inherited metabolic diseases; and (4) non-respiratory infectious diseases. This retrospective study protocol received approval from the Ethics Committee of the Affiliated Hospital of Putian University (approval number: 2024201). Nasopharyngeal swabs (NTS) were collected from patients upon admission by healthcare professionals and stored in sterile Cell Preservation Media Tubes (HEALTH Gene Technologies Co., Ltd., Ningbo, China). Samples were tested within 24 hours of collection after being transported and stored at 2°C. Molecular Detection of Respiratory Pathogens Pathogen detection was performed using a multiplex detection kit designed for 13 respiratory pathogens, employing the capillary electrophoresis fragment analysis method based on PCR (HEALTH Gene Technologies Co., Ltd., Ningbo, China). The kit includes an RT-PCR internal control to monitor the entire detection process, which encompasses nucleic acid extraction, RT-PCR, and capillary electrophoresis. Following successful extraction and amplification, capillary electrophoresis fragment analysis was conducted using the 3500DX Genetic Analyzer manufactured by Thermo Fisher Scientific. Each experiment included both positive and negative controls to ensure the precision and reliability of the results. The nucleic acid detection panel encompasses 13 types of respiratory pathogens, including the following: Influenza A virus (subtypes H1N1, H3N2, H5N2, H7N9), the 2009 H1N1 virus, H3N2 virus, Human Respiratory Syncytial virus (groups A and B), Influenza B virus (Yamagata and Victoria lineages), Human Adenoviruses (groups B, C, and E), Human Rhinovirus, Mycoplasma pneumoniae, Chlamydia (C. trachomatis and C. pneumoniae), Human Parainfluenza viruses (types 1–4), Human Bocavirus, Human Coronaviruses (subtypes 229E, HKU1, NL63, and OC43), and Human Metapneumovirus. The results indicate only the presence or absence of the aforementioned viruses, with no additional testing for subtypes performed. Grouping of Study Subjects All eligible patients were categorized based on specific characteristics. They were divided into male and female groups according to gender. Participants were further classified into four age groups: infants (under 12 months), toddlers (1 to 2 years), preschoolers (3 to 5 years), and school-age children (6 to 14 years). Additionally, patients were grouped according to the month of onset into four categories: the spring group (March to May), the summer group (June to August), the autumn group (September to November), and the winter group (December 2023 to February 2024). Statistical analyses Statistical analyses were performed using SPSS version 29.0 software (IBM, New York, USA). Categorical variables were reported as frequencies (%), while continuous variables were summarized as medians with interquartile ranges. A chi-square test was utilized to evaluate the differences in proportions among categorical variables, including virus detection rates, sex, and age. All tests were conducted on a two-tailed basis, with statistical significance defined as a P-value < 0.05. Results Demographic Information The study included a total of 3,790 pediatric patients, all aged under 14 years, with a mean age of 4.36 years (SD = 3.39 years). Among the participants, 2,188 (57.7%) were male, while 1,602 (42.3%) were female. In terms of age distribution, 576 participants were classified in the infancy group, 763 in the toddler group, 1,110 in the preschool group, and 1,338 in the school-age group. Seasonal distribution revealed that 522 cases occurred in spring, 937 in summer, 1,096 in autumn, and 1,235 in winter. Additional details can be found in Table 1. Table 1 Demographic and Seasonal Information of Participants with Acute Respiratory Infections (ARIs) Characteristics Total cases N = 3790 (%) Positive cases N = 2992 (%) Negative cases N = 798 (%) χ 2 P Gender Male 2188 (57.7) 1744 (79.7) 444 (20.3) 1.813 0.178 Female 1602 (42.3) 1248 (77.9) 354 (22.1) Age Infants (<1y) 576 (15.2) 415 (72.0) 161 (28.0) 44.248 =6y) 1338 (35.3) 1131 (84.5) abc 210 (15.7) Season Spring (Mar,Apr,May) 522 (13.8) 358 (68.6) 164 (31.4) 73.894 <0.001 Summer (Jun,Jul,Aug) 937 (24.7) 695 (74.2) a 242 (25.8) Autumn (Sep,Oct,Nov) 1096 (28.9) 897 (81.8) ab 199 (18.2) Winter (Dec,Jan,Feb) 1235 (32.6) 1042 (84.4) ab 193 (15.6) Notes: The notation ‘ a ’ indicates statistical significance (P < 0.008) for comparisons involving the infant group (age) or the spring group (season), as determined by the chi-square test. The notation ‘ b ’ refers to statistical significance (P < 0.008) for comparisons with the toddler group (age) or the summer group (season). Meanwhile, ‘ c ’ signifies significance (P < 0.008) for comparisons involving the preschool group (age) or the autumn group (season), based on the chi-square test. Characterization of the Distribution of Overall Positivity Rates for Respiratory Pathogens Among the 3,790 pediatric patients, 2,992 (78.9%) tested positive for at least one pathogen. Specifically, 1,744 males (79.7%, 1,744/2,188) and 1,248 females (77.9%, 1,248/1,602) tested positive for pathogens; however, no statistically significant difference was found in the detection rates between the two groups (χ² = 1.813, P = 0.178). Significant differences were observed across age groups (χ² = 44.248, P < 0.001), with the school-age group exhibiting the highest positivity rate for pathogens. Additionally, significant differences were noted among the seasons (χ² = 73.894, P < 0.001), with higher positivity rates for pathogens observed during the autumn and winter seasons. Further details are provided in Table 1. Among the 2,992 pediatric patients who tested positive for pathogens, 2,451 (81.9%) had a single pathogen, while 541 (18.1%) had multiple pathogens. Of the 541 cases with multiple pathogens, 502 tested positive for dual pathogens and 39 for triple pathogens. The positivity rates for detected pathogens, ranked from highest to lowest, were as follows: Mp (33.6%), HRV (12.4%), HRSV (12.1%), HMPV (9.1%), HADV (6.8%), InfA (5.8%), HPIV (5.6%), and InfB (3.8%). Of the 219 cases of InfA detected, 86 were H1N1 and 117 were H3N2. Among the single infections, the most frequently detected pathogens were Mp at 25.5%, followed by HRSV at 10.3% and HRV at 8.1%. The top three pathogens identified in co-infections were Mp (7.9%), HRV (6.9%), and HMPV (3.6%). Further details are illustrated in Figure 1. Age-Dependent Distribution of Respiratory Pathogens in Pediatric Patients A total of 2,992 children with positive pathogen detections were analyzed across various age groups, with the distribution of 11 pathogens detailed in Table 2. Influenza A (InfA) was found to be more prevalent among preschool and school-age children (χ 2 = 31.960, p < 0.001). Human parainfluenza virus (HPIV), human metapneumovirus (HMPV), and human rhinovirus (HRV) exhibited higher prevalence rates in infants, toddlers, and preschool children (χ 2 = 60.742, 31.266, 68.073, P < 0.001). School-aged children demonstrated the highest positivity rates for Mycoplasma pneumoniae (Mp) and Influenza B (InfB), with positivity rates increasing with age (χ 2 = 773.388, 28.994, P < 0.001). Human respiratory syncytial virus (HRSV) had the highest positivity rate in infants (181/576, 31.4%), showing a decline as age increased (χ 2 = 444.657, P < 0.001). Human adenovirus (HADV) was most commonly detected in early childhood, particularly among preschool and school-age children (χ 2 = 25.867, P<0.001), whereas Bocavirus (Boca) was more frequently identified in preschool and school-age children (χ 2 = 25.674, P < 0.001). Temporal and Seasonal Distribution of Pathogens The distribution characteristics of pathogens across months and seasons were analyzed, with the results presented in Table 3 and Figure 2. Notably, Influenza A (InfA) exhibited a higher positivity rate in spring compared to other seasons (χ² = 216.862, P < 0.001), peaking in March at 33.5%. The highest positivity rates for Human Parainfluenza Virus (HPIV) and Boca virus were observed in autumn, significantly surpassing those in spring, summer, and winter (χ² = 222.361, P < 0.001); however, the monthly distribution curve indicated that their overall positivity rates remained low. Human Metapneumovirus (HMPV), Human Adenovirus (HADV), and Influenza B (InfB) demonstrated the highest positivity rates in winter (χ² = 57.989, P < 0.001). The positivity rate for InfB increased from October, peaking in December, while it was nearly undetectable in other months. The positivity rate for HADV began to rise in November, reaching its peak in February of the following year. HMPV maintained elevated levels throughout the year, with its highest peak occurring in the subsequent year. Mycoplasma pneumoniae (Mp) also exhibited positive rates during the fall and winter seasons, with the highest positivity rates occurring in autumn and winter (χ² = 242.985, P < 0.001), and positivity beginning to rise in May, remaining consistently high after July. Human respiratory syncytial virus (HRSV) positivity was higher in summer than in other seasons, with the lowest rate observed in winter (χ² = 205.480, P < 0.001). The monthly distribution curve indicated that the peak occurred in June, with minimal detections from November to February of the following year. Human Rhinovirus (HRV) positivity rates were higher in spring compared to other seasons (χ² = 122.642, P < 0.001). Human Coronavirus (HCOV) exhibited lower positivity rates across all seasons, with the highest rate occurring in summer (χ² = 16.835, P < 0.001). The distribution characteristic of Single Pathogen To isolate the effects of multiple pathogen infections, we analyzed the distribution characteristics of 2,451 cases of single pathogen infections, as illustrated in Figure 3. In the age subgroups, Human Respiratory Syncytial Virus (HRSV) and Human Rhinovirus (HRV) emerged as the predominant pathogens among infants and toddlers; notably, HRSV positivity decreased with age and was not detected in school-age children. Mycoplasma pneumoniae (Mp) was identified as the primary pathogen, with its positivity rate increasing with age. Regarding seasonal variations, HRV, Influenza A (InfA), and HRSV were the principal pathogens in spring, whereas Mp and HRSV predominated in summer, as well as in fall and winter. Table 2 Pathogen Detection Rates in Pediatric Patients with Acute Respiratory Infections by Age Group. Pathogen Infants N=576 (%) Toddlers N=763 (%) Preschoolers N=1110 (%) School-age N=1338 (%) χ 2 P InfA 14 (2.4) 25 (3.3) 79 (7.1) ab 101 (7.6) ab 31.960 ＜0.001 HPIV 40 (6.9) 64 (8.4) 86 (7.8) 23 (1.7) abc 60.742 ＜0.001 HMPV 54 (9.4) 87 (11.4) 127 (11.4) 76 (5.7) abc 31.266 ＜0.001 Mp 39 (6.8) 118 (15.5) a 298 (26.9) ab 817 (61.7) abc 773.388 ＜0.001 HRSV 181 (31.4) 167 (21.9) a 99 (8.9) ab 10 (0.8) abc 444.657 ＜0.001 HADV 12 (2.1) 52 (6.8) a 93 (8.4) a 102 (7.6) a 25.867 ＜0.001 HRV 105 (18.2) 151 (19.8) 197 (17.8) 115 (8.6) abc 68.073 ＜0.001 Boca 5 (0.9) 16 (2.1) 26 (2.3) 3 (0.2) bc 25.674 ＜0.001 Ch* 4 (0.7) 0 (0) 1 (0.1) 1 (0.1) 12.593 0.006 InfB 18 (3.1) 13 (1.7) 32 (2.9) 79 (5.9) abc 28.994 ＜0.001 HCOV 9 (1.6) 7 (0.9) 10 (0.9) 14 (1.1) 1.811 0.613 Note: ' a ' indicates significant differences for the infants' group (P < 0.008), ' b ' for the toddlers' group (P < 0.008), and ' c ' for the preschoolers' group (P < 0.008) as determined by chi-square tests. An asterisk (*) denotes cells with expected counts of less than 5, where, despite the overall chi-square test being significant, Fisher's exact test did not produce statistically significant results. Table 3 Seasonal Distribution of Respiratory Pathogens in Pediatric Participants with Acute Respiratory Infections (ARIs) Pathogen Spring N=510(%) Summer N=937(%) Autumn N=1096(%) Winter N=1247(%) χ 2 P InfA 90(17.7) 2(0.2) a 28(2.6) ab 99(8.0) abc 216.862 ＜0.001 HPIV 3(0.6) 43(4.6) a 154(14.1) ab 13(1.0) bc 222.361 ＜0.001 HMPV 17(3.3) 78(8.3) a 79(7.2) a 171(13.7) abc 57.989 ＜0.001 Mp 23(4.5) 303(32.3) a 444(40.5) ab 503(40.3) ab 242.985 ＜0.001 HRSV 77(15.1) 216(23.1) a 125(11.4) ab 39(3.1) abc 205.480 ＜0.001 HADV 15(2.9) 21(2.2) 43(3.9) 180(14.4) abc 170.915 ＜0.001 HRV 147(28.8) 73(7.8) a 187(17.1) ab 161(12.9) abc 122.642 ＜0.001 Boca 1(0.2) 6(0.6) 29(2.7) ab 14(1.1) c 23.448 ＜0.001 Ch 3(0.6) 1(0.1) 1(0.1) 1(0.1) 6.915 0.075 InfB 1(0.2) 2(0.2) 16(1.5) b 123(9.9) abc 195.540 ＜0.001 HCOV 3(0.6) 21(2.2) a 7(0.6) b 9(0.7) b 16.835 ＜0.001 Notes: Superscripts indicate significant chi-square test results (P < 0.008) for specific seasons: ' a ' denotes a significant difference from Spring, ' b ' denotes a significant difference from Summer, and ' c ' denotes a significant difference from Autumn The distribution characteristic of co-infections The distribution characteristics of co-infections reveal that, among the 541 cases analyzed, 502 involved dual pathogens, significantly outnumbering the 39 cases of triple pathogen infections. Consequently, our primary focus was on the distribution characteristics of dual pathogens, with results illustrated in Figure 4. Seasonal analysis indicated that co-infections predominantly occurred in autumn and winter, with the most common infection pattern being HRV+Mp. The highest incidence of co-infections was recorded in winter, followed by HADV+Mp and HMPV+InfB as the second and third most prevalent patterns, respectively. In terms of age group distribution, co-infections were most common among preschool and school-age children, with HRV+Mp identified as the predominant infection pattern. Notably, the highest prevalence of co-infections was observed in the school-age group, where the three most common infection patterns were HRV+Mp, HADV+Mp, and HMPV+Mp. Discussion To the best of our knowledge, this study represents the first investigation into the role of 11 respiratory pathogens in acute respiratory infections among hospitalized children in the Putian area of southeast China, particularly following the suspension of strict COVID-19 management policies. The pathogens examined, including Mp, HRV, HRSV, and HPIV, were detected using RT-PCR. We subsequently analyzed their epidemiological characteristics, focusing on infection frequency, gender distribution, age groups, and seasonal patterns, to provide insights into the burden of these pathogens and to inform targeted prevention and treatment strategies for acute respiratory infections (ARIs) in children. Our findings indicate that the overall positivity rate for the 11 pathogens analyzed was 78.9%, a figure comparable to the rates observed during periods of non-strict Non-Pharmaceutical Interventions (NPIs) in Shanghai[ 14 ] (78.7%), Zibo[ 15 ] (77.9%), and Shenzhen[ 16 ] (80.5%), but significantly higher than during the strict NPI period. This suggests that, following the deregulation of NPIs, pathogen infection rates have reverted to pre-COVID-19 pandemic levels. In our study, the proportion of children enrolled during the spring season was the lowest, with an increasing number of participants observed from spring to summer and then to autumn. This trend aligns with findings reported by Xu et al[ 14 ]. Although we accounted for the impact of NPIs in our study design and established a washout period of two months following the implementation of strict NPI policies, our data indicated a significant increase in enrollment numbers only five months after the policy was lifted. Human Rhinovirus (HRV) exhibited the highest positivity rate during the Spring Festival, which contrasts with the limited number of participants included in the spring season. This observation suggests that HRV is less influenced by non-pharmaceutical interventions (NPIs), consistent with previous studies conducted in New Zealand[ 17 ]. One potential explanation for this phenomenon is that HRV, being a non-enveloped virus, demonstrates reduced susceptibility to disinfectants such as alcohol, enabling it to remain viable on surfaces for extended periods[ 17 – 19 ]. Furthermore, following the spring season, the positivity rates of other respiratory pathogens gradually increased, while HRV rates declined in comparison to those observed in spring. This finding stands in contrast to the research by Xu et al. [ 14 ], which reported consistently high HRV positivity rates throughout the pandemic. The dynamics observed may be related to viral interactions[ 20 ], as the increasing prevalence of other viruses, which are less affected by NPI measures, could lead to the secretion of interferons that inhibit HRV infection. Several long-term retrospective studies have identified HRSV and HMPV as seasonal diseases of significant prevalence, predominantly affecting children under the age of six[ 21 , 22 ]. This observation is consistent with our findings. Notably, in our study, the peak positivity rate for HRSV detection occurred in June, indicating a departure from the typical winter seasonality. This pattern corresponds with the epidemiological trends observed in the United States and Australia following the relaxation of Non-Pharmaceutical Interventions (NPIs)[ 23 , 24 ]. One possible explanation for this shift is that stringent NPI measures, which included school closures, restrictions on family gatherings, and the shutdown of restaurants and hotels, initially curtailed the transmission of respiratory viruses. Consequently, this may have led to a buildup of susceptible individuals, resulting in off-season outbreaks of pathogens that are typically associated with seasonal transmission[ 25 ]. Additionally, our study found that the annual average incidence rate of Human Metapneumovirus (HMPV) was 9.6%, significantly higher than the rates reported in previous studies, which indicated rates of less than 5%. This observation aligns with findings by Kuang et al. [ 22 ], particularly following the relaxation of non-pharmaceutical intervention (NPI) strategies. The increase in HMPV infections may be attributed to the recurring cycle of mild or asymptomatic reinfections within households and communities, as well as the prolonged shedding of infectious viruses by individuals who have recovered from HMPV infection[ 26 ]. Furthermore, a study examining pathogen prevalence before and after the implementation of strict NPI measures against the COVID-19 outbreak found that these measures significantly reduced HMPV infections. These findings indicate that greater emphasis should be placed on the prevention of HMPV in children under six years of age. Specifically, NPIs should be employed to prevent infection during social interactions with individuals who are currently experiencing or recovering from HMPV infection. Additionally, enhancing health education for HMPV patients and minimizing social interactions with children under six years of age during the infection period can prove effective. Similar to other respiratory pathogens, Mycoplasma pneumoniae (Mp) exhibits a distinct seasonal and demographic pattern. Previous studies have reported that Mp outbreaks primarily occur in autumn, predominantly affecting school-age children[ 27 ], which aligns with our findings. In our study, Mp reached its peak in autumn but remained elevated throughout the summer, autumn, and winter, suggesting a potential Mp epidemic in 2023. A similar trend was noted in Guangdong Province[ 28 ]. This pattern may be attributed to the cessation of strict non-pharmaceutical intervention (NPI) policies, which have resulted in 48%-83.2% of the Chinese population being infected with COVID-19[ 29 ]. Prolonged immune dysfunction has been observed following COVID-19 infection, which may render children with immature immune systems more susceptible to Mp infection[ 30 ]. A pronounced peak in adenovirus (ADV) detection was observed during the winter months, while detection rates were significantly lower in other seasons, indicating a potential ADV outbreak in winter. This observation is consistent with a meta-analysis conducted in China, which systematically reviewed 950 articles on ADV outbreaks published between 2009 and 2020[ 31 ]. Furthermore, the timing of ADV outbreaks coincided with the prevalence period of Mycoplasma pneumoniae, and our study frequently documented mixed infections of Mycoplasma pneumoniae and human adenovirus (HADV) during the winter. These findings suggest that in clinical practice, children presenting with persistent or recurrent fever after initial resolution should not be attributed solely to Mycoplasma pneumoniae infection; a thorough evaluation for the possibility of HADV infection is also warranted. However, our study has limitations, as all data were collected from a single hospital, which may introduce selection bias. Additionally, bacterial data were not collected. Ongoing monitoring of respiratory pathogen epidemiological trends and further research are essential for enhancing the management of acute respiratory tract infections (ARTIs). Conclusion In this study, the epidemiological landscape of acute respiratory infections (ARIs) among pediatric patients in Putian City, China, has significantly changed following the cessation of stringent non-pharmaceutical interventions (NPIs). The findings indicate that the rate of pathogen positivity has reverted to levels observed prior to the COVID-19 pandemic, with Mycoplasma pneumoniae (Mp), Human rhinovirus (HRV), Human respiratory syncytial virus (HRSV), and Human metapneumovirus (HMPV) identified as the most prevalent pathogens. Pathogen distribution exhibited variations by age and season, with notable seasonal trends for several pathogens, including Influenza A (InfA), Human adenovirus (HADV), and Mp. These results underscore the importance of continuous surveillance and the development of tailored prevention strategies for respiratory infections, particularly in the context of evolving viral dynamics and the potential for off-season outbreaks. Ultimately, the findings highlight the necessity for a proactive, data-driven approach to managing ARIs in pediatric populations to mitigate associated morbidity. Abbreviations ARI Acute Respiratory Infections RT-PCR Reverse transcription polymerase chain reaction NPI Non-Pharmaceutical interventions COVID-19 Coronavirus disease 2019 InfA Influenza virus A InfB Influenza virus B Boca Human Bocavirus HRV Human Rhinovirus HRSV Human Respiratory Syncytial Virus HPIV Human Parainfluenza virus HADV Human Adenovirus HCOV Human Coronavirus HMPV Human Metapneumovirus Ch Chlamydia Mp Mycoplasma Pneumoniae Declarations Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of the Affiliated Hospital of Putian University (approval number: 2024201). Written informed consent was obtained from all participants prior to participation in the study. All procedures adhered to the ethical standards stated in the Declaration of Helsinki. Clinical trial number not applicable Consent for publication Not applicable Availability of data and material The data can be obtained from the corresponding authors upon reasonable request. Competing interests The authors declare no competing interests. Funding This research was funded by the Putian City Science and Technology Foundation - Putian City Science and Technology Plan Project (grant number 2024S3F005). Author contributions J.W. Zhu designed the study, conducted the data analysis, and drafted the manuscript. S.Q. Wu contributed the revision of the manuscript; Y. Chen; L.P. Zheng responsible for data collection; All authors read and approved the final manuscript. Acknowledgements We extend our gratitude to the members of the Laboratory Department of the Affiliated Hospital of Putian University for their assistance in detecting respiratory pathogens, and to all participants for their valuable contributions to this study. Authors' information Jinwei Zhu; Pediatrics Department, Section 2, Affiliated Hospital of Putian University Suqing Wu; Department of Ultrasound, Affiliated Hospital of Putian University, Putian, China Yan Chen; Department of Laboratory Medicine, Affiliated Hospital of Putian University, Putian, China Liping Zheng; Pediatrics Department, Section 1, Affiliated Hospital of Putian University, Putian, China References Collaborators. GDaI. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020. doi: 10.1016/s0140-6736(20)30925-9. Monto AS. Epidemiology of viral respiratory infections. Am J Med. 2002. doi: 10.1016/s0002-9343(01)01058-0. UNICEF: Child health: Pneumonia (UNICEF Data). https://data.unicef.org/topic/child-health/pneumonia/ (2023). Accessed 13 Sept 2024. Hanada S, Pirzadeh M, Carver KY, Deng JC. Respiratory Viral Infection-Induced Microbiome Alterations and Secondary Bacterial Pneumonia. Front Immunol. 2018. doi: 10.3389/fimmu.2018.02640. Campbell H, El Arifeen S, Hazir T, O'Kelly J, Bryce J, Rudan I, et al. Measuring coverage in MNCH: challenges in monitoring the proportion of young children with pneumonia who receive antibiotic treatment. PLoS Med. 2013. doi: 10.1371/journal.pmed.1001421. Chen Y, Shi K, Liu H, Yin Y, Zhao J, Long F, et al. Development of a multiplex qRT-PCR assay for detection of African swine fever virus, classical swine fever virus and porcine reproductive and respiratory syndrome virus. J Vet Sci. 2021. doi: 10.4142/jvs.2021.22.e87. Li Y, Wang X, Blau DM, Caballero MT, Feikin DR, Gill CJ, et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in children younger than 5 years in 2019: a systematic analysis. Lancet. 2022. doi: 10.1016/s0140-6736(22)00478-0. Gao M, Yao X, Mao W, Shen C, Zhang Z, Huang Q, et al. Etiological analysis of virus, mycoplasma pneumoniae and chlamydia pneumoniae in hospitalized children with acute respiratory infections in Huzhou. Virol J. 2020. doi: 10.1186/s12985-020-01380-4. Christopher Troeger BFB, Ibrahim A Khalil, Stephanie R M Zimsen, Samuel B. Albertson, Degu Abate, Jemal Abdela. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2019. doi: 10.1016/s2213-2600(18)30496-x. Lei C, Lou CT, Io K, SiTou KI, Ip CP, U H, et al. Viral etiology among children hospitalized for acute respiratory tract infections and its association with meteorological factors and air pollutants: a time-series study (2014-2017) in Macao. BMC Infect Dis. 2022. doi: 10.1186/s12879-022-07585-y. Zhao Y, Lu R, Shen J, Xie Z, Liu G, Tan W. Comparison of viral and epidemiological profiles of hospitalized children with severe acute respiratory infection in Beijing and Shanghai, China. BMC Infect Dis. 2019. doi: 10.1186/s12879-019-4385-5. Tang M, Dong W, Yuan S, Chen J, Lin J, Wu J, et al. Comparison of respiratory pathogens in children with community-acquired pneumonia before and during the COVID-19 pandemic. BMC Pediatr. 2023. doi: 10.1186/s12887-023-04246-0. Gao ZX, Wang Y, Yan LY, Liu T, Peng LW. Epidemiological characteristics of respiratory viruses in children during the COVID-19 epidemic in Chengdu, China. Microbiol Spectr. 2024. doi: 10.1128/spectrum.02614-23. Xu M, Liu P, Su L, Cao L, Zhong H, Lu L, et al. Comparison of Respiratory Pathogens in Children With Lower Respiratory Tract Infections Before and During the COVID-19 Pandemic in Shanghai, China. Front Pediatr. 2022. doi: 10.3389/fped.2022.881224. Lv G, Shi L, Liu Y, Sun X, Mu K. Epidemiological characteristics of common respiratory pathogens in children. Sci Rep. 2024. doi: 10.1038/s41598-024-65006-3. Li L, Wang H, Liu A, Wang R, Zhi T, Zheng Y, et al. Comparison of 11 respiratory pathogens among hospitalized children before and during the COVID-19 epidemic in Shenzhen, China. Virol J. 2021. doi: 10.1186/s12985-021-01669-y. Huang QS, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, et al. Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. Nat Commun. 2021. doi: 10.1038/s41467-021-21157-9. Winther B, McCue K, Ashe K, Rubino JR, Hendley JO. Environmental contamination with rhinovirus and transfer to fingers of healthy individuals by daily life activity. J Med Virol. 2007. doi: 10.1002/jmv.20956. Savolainen-Kopra C, Korpela T, Simonen-Tikka ML, Amiryousefi A, Ziegler T, Roivainen M, et al. Single treatment with ethanol hand rub is ineffective against human rhinovirus--hand washing with soap and water removes the virus efficiently. J Med Virol. 2012. doi: 10.1002/jmv.23222. Nickbakhsh S, Mair C, Matthews L, Reeve R, Johnson PCD, Thorburn F, et al. Virus-virus interactions impact the population dynamics of influenza and the common cold. Proc Natl Acad Sci U S A. 2019. doi: 10.1073/pnas.1911083116. García-Arroyo L, Prim N, Del Cuerpo M, Marín P, Roig MC, Esteban M, et al. Prevalence and seasonality of viral respiratory infections in a temperate climate region: A 24-year study (1997-2020). Influenza Other Respir Viruses. 2022. doi: 10.1111/irv.12972. Kuang L, Xu T, Wang C, Xie J, Zhang Y, Guo M, et al. Changes in the epidemiological patterns of respiratory syncytial virus and human metapneumovirus infection among pediatric patients and their correlation with severe cases: a long-term retrospective study. Front Cell Infect Microbiol. 2024. doi: 10.3389/fcimb.2024.1435294. Foley DA, Yeoh DK, Minney-Smith CA, Martin AC, Mace AO, Sikazwe CT, 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. doi: 10.1093/cid/ciaa1906. Hamid S, Winn A, Parikh R, Jones JM, McMorrow M, Prill MM, et al. Seasonality of Respiratory Syncytial Virus - United States, 2017-2023. MMWR Morb Mortal Wkly Rep. 2023. doi: 10.15585/mmwr.mm7214a1. 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. doi: 10.1073/pnas.2013182117. Bell C, He C, Norton D, Goss M, Chen G, Temte J. Household transmission of human metapneumovirus and seasonal coronavirus. Epidemiol Infect. 2024. doi: 10.1017/s0950268824000517. Gao LW, Yin J, Hu YH, Liu XY, Feng XL, He JX, et al. The epidemiology of paediatric Mycoplasma pneumoniae pneumonia in North China: 2006 to 2016. Epidemiol Infect. 2019. doi: 10.1017/s0950268819000839. Li Y, Wu M, Liang Y, Yang Y, Guo W, Deng Y, et al. Mycoplasma pneumoniae infection outbreak in Guangzhou, China after COVID-19 pandemic. Virol J. 2024. doi: 10.1186/s12985-024-02458-z. Fu D, He G, Li H, Tan H, Ji X, Lin Z, et al. Effectiveness of COVID-19 Vaccination Against SARS-CoV-2 Omicron Variant Infection and Symptoms - China, December 2022-February 2023. China CDC Wkly. 2023. doi: 10.46234/ccdcw2023.070. Liu K, Fu HM, Lu Q. [Advancement in epidemiology of Mycoplasma pneumoniae pneumonia in children in China]. Zhonghua Er Ke Za Zhi. 2024. doi: 10.3760/cma.j.cn112140-20240407-00247. Liu MC, Xu Q, Li TT, Wang T, Jiang BG, Lv CL, et al. Prevalence of human infection with respiratory adenovirus in China: A systematic review and meta-analysis. PLoS Negl Trop Dis. 2023. doi: 10.1371/journal.pntd.0011151. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 13 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 10 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5425847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377589512,"identity":"af2942ce-b334-44a4-93aa-b4633b8729e1","order_by":0,"name":"Jinwei Zhu","email":"","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":false,"prefix":"","firstName":"Jinwei","middleName":"","lastName":"Zhu","suffix":""},{"id":377589513,"identity":"3aafdd35-cab1-4f76-9db7-3c3075f78bd1","order_by":1,"name":"Suqing Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCQYGA4YKOWYwh4d4LWeMSdTCwNhmzEC8FvnZPQbFvPMM2HVnJDA+eNvGIG9OSAvjnDMGxrzbDJjNbiQwG85tYzDc2UBAC7NEjoFx7rY/IC1s0rxtDAkGBwhoYQNrmQO2hf03UVp4wFoawFrYmInSIiGRVmD85xhQy5mHzZJzzkkYbiCkRX5G8jbDGTUGyWbHkw9+eFNmI0/QFpB3DIBEMjDwGhgg0UQYMD8AEnZEKR0Fo2AUjIKRCQDaWDedoZE/cwAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":true,"prefix":"","firstName":"Suqing","middleName":"","lastName":"Wu","suffix":""},{"id":377589514,"identity":"8d436c2b-ca64-4274-9890-d758c08c2cb5","order_by":2,"name":"Yan Chen","email":"","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chen","suffix":""},{"id":377589517,"identity":"5efefdd4-30a8-48ad-bcaf-9481af91741d","order_by":3,"name":"Liping Zheng","email":"","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-11-10 12:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5425847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425847/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-10670-7","type":"published","date":"2025-02-26T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71634367,"identity":"1837239a-b3bc-4086-9b3a-af68cdb7887f","added_by":"auto","created_at":"2024-12-17 09:55:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686324,"visible":true,"origin":"","legend":"\u003cp\u003eDetection Count and Positivity Rate of Pathogens in participants with Acute Respiratory Infections (ARIs).\u003c/p\u003e","description":"","filename":"Figure.1DetectionCountandPositivityRateofPathogensinparticipantswithAcuteRespiratoryInfectionsARIs..jpg","url":"https://assets-eu.researchsquare.com/files/rs-5425847/v1/da4ee3abfce04b2ede118afb.jpg"},{"id":71634371,"identity":"1858d493-0eb5-4e17-a23c-88a1a89f6b2c","added_by":"auto","created_at":"2024-12-17 09:55:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554633,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Distribution of Respiratory Pathogens in participants with Acute Respiratory Tract Infections (ARTIs)\u003c/p\u003e","description":"","filename":"Figure.2MonthlyDistributionofRespiratoryPathogensinparticipantswithAcuteRespiratoryTractInfectionsARTIs.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5425847/v1/0363c8871cd1146145f4b8ce.jpg"},{"id":71634369,"identity":"fee7614c-4b65-4d14-b90a-9c70c480dbb1","added_by":"auto","created_at":"2024-12-17 09:55:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":492481,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Single Pathogen across Different Groups. A. Distribution of Single Pathogen across Age Groups. B. Distribution of Single Pathogen across Seasonal Groups.\u003c/p\u003e","description":"","filename":"Figure.3DistributionofSinglePathogenacrossDifferentjpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5425847/v1/62ce3c39a2729bb151a562eb.jpg"},{"id":71635042,"identity":"5b43c6be-03af-45ee-a0aa-e15b3e3b97b0","added_by":"auto","created_at":"2024-12-17 10:03:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1599391,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Dual Pathogen across Different Groups.\u003c/p\u003e","description":"","filename":"Figure.4DistributionofDualPathogenacrossDifferentGroups.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5425847/v1/a9b9e664aeca5d3c8ec36df7.jpg"},{"id":77623670,"identity":"2262d01b-4ecc-4d89-a701-0d72faa6dbe2","added_by":"auto","created_at":"2025-03-03 16:11:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4199670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425847/v1/38b3188e-f338-4050-8161-e7cac41a1ebc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Distribution of Respiratory Pathogens in Paediatric Acute Respiratory Infections After the Cessation of Strict Non-Pharmaceutical Interventions in Putian, China.","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute Respiratory Infections (ARIs) pose a significant burden on healthcare systems, particularly affecting pediatric populations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, young children experience between 5 and 8 episodes of ARIs annually[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], making these infections one of the leading causes of childhood morbidity and mortality. ARIs can be classified into two categories: acute upper respiratory tract infections (AURI) and acute lower respiratory tract infections (ALRTI), both of which are commonly caused by viral or bacterial pathogens. According to the United Nations International Children's Emergency Fund (UNICEF)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], acute respiratory infections are the primary cause of death among children under the age of 5.\u003c/p\u003e \u003cp\u003eThe pathogens responsible for ARIs include both viruses and bacteria, which often present overlapping clinical features. Viral infections, in particular, can trigger host immune responses that facilitate bacterial growth, complicating diagnosis and leading to frequent misuse of antibiotics[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research indicates that approximately 50% of ARI cases are misdiagnosed as bacterial infections, contributing to unnecessary antibiotic use[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, the epidemiological study of respiratory pathogens is essential for accurate diagnosis and appropriate treatment.\u003c/p\u003e \u003cp\u003eRecent advancements in molecular diagnostic techniques, such as reverse-transcription polymerase chain reaction (RT-PCR), have significantly enhanced the detection of respiratory pathogens[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], leading to the identification of a diverse array of viruses and bacteria associated with ARIs. Notably, pathogens such as respiratory syncytial virus (RSV), influenza viruses, rhinoviruses, adenoviruses are frequently detected in children with ARIs[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Influenza, characterized by its well-established seasonal peaks, and Mycoplasma pneumoniae, a common cause of bacterial ARIs, are of particular concern[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, air quality and temperature have been demonstrated to influence the incidence of respiratory infections[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina, with its vast geographical expanse and diverse climates, exhibits regional variations in the prevalence and seasonal patterns of respiratory pathogens. Research conducted in cities such as Beijing, Shanghai, and Chengdu has revealed differing epidemiological profiles of acute respiratory infection (ARI) pathogens, highlighting significant regional disparities in pathogen distribution and seasonal trends[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, there is a paucity of research regarding the prevalence of respiratory pathogens in Putian, a city situated on the southeast coast of China. Putian is characterized by its distinctive subtropical marine climate, which significantly influences the seasonal distribution of respiratory infections.\u003c/p\u003e \u003cp\u003eIn light of these factors, our study was designed to investigate the prevalence and distribution of respiratory pathogens among pediatric patients with acute respiratory infections (ARIs) in Putian, China, following the cessation of strict non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic. These NPIs included social distancing, quarantine, lockdowns, mask-wearing, hand hygiene, school and workplace closures, travel restrictions, and the cancellation of mass gatherings. This study provides a comprehensive analysis of respiratory pathogen prevalence and seasonal trends, offering valuable insights into the post-NPI era and informing targeted strategies for the management of ARIs in children.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population and Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at the Affiliated Hospital of Putian University, located in Putian City, Fujian Province, China. Patients presented with clinical manifestations indicative of acute respiratory infections, including cough, pharyngitis, and influenza-like symptoms (e.g., pyrexia, malaise, cephalgia, nasal obstruction, and rhinorrhea), as well as respiratory distress. The inclusion criteria were as follows: (1) inpatients under 14 years of age; (2) inpatients diagnosed with acute respiratory infections (ARI) admitted between March 2023 and February 2024; and (3) medical records must contain comprehensive results from nasopharyngeal swabs. The exclusion criteria included: (1) immunocompromising conditions or immunodeficiencies; (2) patients diagnosed with COVID-19; (3) congenital inherited metabolic diseases; and (4) non-respiratory infectious diseases. This retrospective study protocol received approval from the Ethics Committee of the Affiliated Hospital of Putian University (approval number: 2024201).\u003c/p\u003e\n\u003cp\u003eNasopharyngeal swabs (NTS) were collected from patients upon admission by healthcare professionals and stored in sterile Cell Preservation Media Tubes (HEALTH Gene Technologies Co., Ltd., Ningbo, China). Samples were tested within 24 hours of collection after being transported and stored at 2\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Detection of Respiratory Pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathogen detection was performed using a multiplex detection kit designed for 13 respiratory pathogens, employing the capillary electrophoresis fragment analysis method based on PCR (HEALTH Gene Technologies Co., Ltd., Ningbo, China). The kit includes an RT-PCR internal control to monitor the entire detection process, which encompasses nucleic acid extraction, RT-PCR, and capillary electrophoresis. Following successful extraction and amplification, capillary electrophoresis fragment analysis was conducted using the 3500DX Genetic Analyzer manufactured by Thermo Fisher Scientific. Each experiment included both positive and negative controls to ensure the precision and reliability of the results.\u003c/p\u003e\n\u003cp\u003eThe nucleic acid detection panel encompasses 13 types of respiratory pathogens, including the following: Influenza A virus (subtypes H1N1, H3N2, H5N2, H7N9), the 2009 H1N1 virus, H3N2 virus, Human Respiratory Syncytial virus (groups A and B), Influenza B virus (Yamagata and Victoria lineages), Human Adenoviruses (groups B, C, and E), Human Rhinovirus, Mycoplasma pneumoniae, Chlamydia (C. trachomatis and C. pneumoniae), Human Parainfluenza viruses (types 1\u0026ndash;4), Human Bocavirus, Human Coronaviruses (subtypes 229E, HKU1, NL63, and OC43), and Human Metapneumovirus. The results indicate only the presence or absence of the aforementioned viruses, with no additional testing for subtypes performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrouping of Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll eligible patients were categorized based on specific characteristics. They were divided into male and female groups according to gender. Participants were further classified into four age groups: infants (under 12 months), toddlers (1 to 2 years), preschoolers (3 to 5 years), and school-age children (6 to 14 years). Additionally, patients were grouped according to the month of onset into four categories: the spring group (March to May), the summer group (June to August), the autumn group (September to November), and the winter group (December 2023 to February 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 29.0 software (IBM, New York, USA). Categorical variables were reported as frequencies (%), while continuous variables were summarized as medians with interquartile ranges. A chi-square test was utilized to evaluate the differences in proportions among categorical variables, including virus detection rates, sex, and age. All tests were conducted on a two-tailed basis, with statistical significance defined as a P-value \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included a total of 3,790 pediatric patients, all aged under 14 years, with a mean age of 4.36 years (SD = 3.39 years). Among the participants, 2,188 (57.7%) were male, while 1,602 (42.3%) were female. In terms of age distribution, 576 participants were classified in the infancy group, 763 in the toddler group, 1,110 in the preschool group, and 1,338 in the school-age group. Seasonal distribution revealed that 522 cases occurred in spring, 937 in summer, 1,096 in autumn, and 1,235 in winter. Additional details can be found in Table 1.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic and Seasonal Information of Participants with Acute Respiratory Infections (ARIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal cases\u003c/p\u003e\n \u003cp\u003eN = 3790 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive cases\u003c/p\u003e\n \u003cp\u003eN = 2992 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative cases\u003c/p\u003e\n \u003cp\u003eN = 798 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2188 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1744 (79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e444 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1602 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1248 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfants (\u0026lt;1y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e576 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e415 (72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e44.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eToddlers (1-2y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e763 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e581(76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreschool (3-5y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1110 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e865 (77.9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e245 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSchool-age (\u0026gt;=6y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1338 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1131 (84.5)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpring (Mar,Apr,May)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e522 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e358 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e73.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Summer (Jun,Jul,Aug)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e937 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e695 (74.2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e242 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Autumn (Sep,Oct,Nov)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1096 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e897 (81.8)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e199 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Winter (Dec,Jan,Feb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1235 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1042 (84.4)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e The notation \u0026lsquo;\u003csup\u003ea\u003c/sup\u003e\u0026rsquo; indicates statistical significance (P \u0026lt; 0.008) for comparisons involving the infant group (age) or the spring group (season), as determined by the chi-square test. The notation \u0026lsquo;\u003csup\u003eb\u003c/sup\u003e\u0026rsquo; refers to statistical significance (P \u0026lt; 0.008) for comparisons with the toddler group (age) or the summer group (season). Meanwhile, \u0026lsquo;\u003csup\u003ec\u003c/sup\u003e\u0026rsquo; signifies significance (P \u0026lt; 0.008) for comparisons involving the preschool group (age) or the autumn group (season), based on the chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of the Distribution of Overall Positivity Rates for Respiratory Pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 3,790 pediatric patients, 2,992 (78.9%) tested positive for at least one pathogen. Specifically, 1,744 males (79.7%, 1,744/2,188) and 1,248 females (77.9%, 1,248/1,602) tested positive for pathogens; however, no statistically significant difference was found in the detection rates between the two groups (\u0026chi;\u0026sup2; = 1.813, P = 0.178). Significant differences were observed across age groups (\u0026chi;\u0026sup2; = 44.248, P \u0026lt; 0.001), with the school-age group exhibiting the highest positivity rate for pathogens. Additionally, significant differences were noted among the seasons (\u0026chi;\u0026sup2; = 73.894, P \u0026lt; 0.001), with higher positivity rates for pathogens observed during the autumn and winter seasons. Further details are provided in Table 1.\u003c/p\u003e\n\u003cp\u003eAmong the 2,992 pediatric patients who tested positive for pathogens, 2,451 (81.9%) had a single pathogen, while 541 (18.1%) had multiple pathogens. Of the 541 cases with multiple pathogens, 502 tested positive for dual pathogens and 39 for triple pathogens. The positivity rates for detected pathogens, ranked from highest to lowest, were as follows: Mp (33.6%), HRV (12.4%), HRSV (12.1%), HMPV (9.1%), HADV (6.8%), InfA (5.8%), HPIV (5.6%), and InfB (3.8%). Of the 219 cases of InfA detected, 86 were H1N1 and 117 were H3N2. Among the single infections, the most frequently detected pathogens were Mp at 25.5%, followed by HRSV at 10.3% and HRV at 8.1%. The top three pathogens identified in co-infections were Mp (7.9%), HRV (6.9%), and HMPV (3.6%). Further details are illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge-Dependent Distribution of Respiratory Pathogens in Pediatric Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2,992 children with positive pathogen detections were analyzed across various age groups, with the distribution of 11 pathogens detailed in Table 2. Influenza A (InfA) was found to be more prevalent among preschool and school-age children (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 31.960, p \u0026lt; 0.001). Human parainfluenza virus (HPIV), human metapneumovirus (HMPV), and human rhinovirus (HRV) exhibited higher prevalence rates in infants, toddlers, and preschool children (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 60.742, 31.266, 68.073, P \u0026lt; 0.001). School-aged children demonstrated the highest positivity rates for Mycoplasma pneumoniae (Mp) and Influenza B (InfB), with positivity rates increasing with age (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 773.388, 28.994, P \u0026lt; 0.001). Human respiratory syncytial virus (HRSV) had the highest positivity rate in infants (181/576, 31.4%), showing a decline as age increased (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 444.657, P \u0026lt; 0.001). Human adenovirus (HADV) was most commonly detected in early childhood, particularly among preschool and school-age children (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 25.867, P\u0026lt;0.001), whereas Bocavirus (Boca) was more frequently identified in preschool and school-age children (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 25.674, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal and Seasonal Distribution of Pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution characteristics of pathogens across months and seasons were analyzed, with the results presented in Table 3 and Figure 2. Notably, Influenza A (InfA) exhibited a higher positivity rate in spring compared to other seasons (\u0026chi;\u0026sup2; = 216.862, P \u0026lt; 0.001), peaking in March at 33.5%. The highest positivity rates for Human Parainfluenza Virus (HPIV) and Boca virus were observed in autumn, significantly surpassing those in spring, summer, and winter (\u0026chi;\u0026sup2; = 222.361, P \u0026lt; 0.001); however, the monthly distribution curve indicated that their overall positivity rates remained low. Human Metapneumovirus (HMPV), Human Adenovirus (HADV), and Influenza B (InfB) demonstrated the highest positivity rates in winter (\u0026chi;\u0026sup2; = 57.989, P \u0026lt; 0.001). The positivity rate for InfB increased from October, peaking in December, while it was nearly undetectable in other months. The positivity rate for HADV began to rise in November, reaching its peak in February of the following year. HMPV maintained elevated levels throughout the year, with its highest peak occurring in the subsequent year. Mycoplasma pneumoniae (Mp) also exhibited positive rates during the fall and winter seasons, with the highest positivity rates occurring in autumn and winter (\u0026chi;\u0026sup2; = 242.985, P \u0026lt; 0.001), and positivity beginning to rise in May, remaining consistently high after July. Human respiratory syncytial virus (HRSV) positivity was higher in summer than in other seasons, with the lowest rate observed in winter (\u0026chi;\u0026sup2; = 205.480, P \u0026lt; 0.001). The monthly distribution curve indicated that the peak occurred in June, with minimal detections from November to February of the following year. Human Rhinovirus (HRV) positivity rates were higher in spring compared to other seasons (\u0026chi;\u0026sup2; = 122.642, P \u0026lt; 0.001). Human Coronavirus (HCOV) exhibited lower positivity rates across all seasons, with the highest rate occurring in summer (\u0026chi;\u0026sup2; = 16.835, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe distribution characteristic of Single Pathogen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo isolate the effects of multiple pathogen infections, we analyzed the distribution characteristics of 2,451 cases of single pathogen infections, as illustrated in Figure 3. In the age subgroups, Human Respiratory Syncytial Virus (HRSV) and Human Rhinovirus (HRV) emerged as the predominant pathogens among infants and toddlers; notably, HRSV positivity decreased with age and was not detected in school-age children. Mycoplasma pneumoniae (Mp) was identified as the primary pathogen, with its positivity rate increasing with age. Regarding seasonal variations, HRV, Influenza A (InfA), and HRSV were the principal pathogens in spring, whereas Mp and HRSV predominated in summer, as well as in fall and winter.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003ePathogen Detection Rates in Pediatric Patients with Acute Respiratory Infections by Age Group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInfants\u003c/p\u003e\n \u003cp\u003eN=576 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eToddlers\u003c/p\u003e\n \u003cp\u003eN=763 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePreschoolers\u003c/p\u003e\n \u003cp\u003eN=1110 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSchool-age\u003c/p\u003e\n \u003cp\u003eN=1338 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79 (7.1)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101 (7.6)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (1.7)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76 (5.7)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118 (15.5)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e298 (26.9)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e817 (61.7)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e773.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e181 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e167 (21.9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99 (8.9)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (0.8)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e444.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 (6.8)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93 (8.4)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102 (7.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115 (8.6)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBoca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (0.2)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCh*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79 (5.9)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHCOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026apos;\u003csup\u003ea\u003c/sup\u003e\u0026apos; indicates significant differences for the infants\u0026apos; group (P \u0026lt; 0.008), \u0026apos;\u003csup\u003eb\u003c/sup\u003e\u0026apos; for the toddlers\u0026apos; group (P \u0026lt; 0.008), and \u0026apos;\u003csup\u003ec\u003c/sup\u003e\u0026apos; for the preschoolers\u0026apos; group (P \u0026lt; 0.008) as determined by chi-square tests. An asterisk (*) denotes cells with expected counts of less than 5, where, despite the overall chi-square test being significant, Fisher\u0026apos;s exact test did not produce statistically significant results.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSeasonal Distribution of Respiratory Pathogens in Pediatric Participants with Acute Respiratory Infections (ARIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003cp\u003eN=510(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003cp\u003eN=937(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAutumn\u003c/p\u003e\n \u003cp\u003eN=1096(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003cp\u003eN=1247(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(0.2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28(2.6)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99(8.0)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43(4.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e154(14.1)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13(1.0)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78(8.3)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79(7.2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171(13.7)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e303(32.3)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e444(40.5)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e503(40.3)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e242.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77(15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216(23.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125(11.4)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39(3.1)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e205.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180(14.4)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e170.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73(7.8)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187(17.1)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161(12.9)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBoca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29(2.7)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(1.1)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16(1.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123(9.9)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e195.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHCOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21(2.2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7(0.6)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(0.7)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eSuperscripts indicate significant chi-square test results (P \u0026lt; 0.008) for specific seasons: \u0026apos;\u003csup\u003ea\u003c/sup\u003e\u0026apos; denotes a significant difference from Spring, \u0026apos;\u003csup\u003eb\u003c/sup\u003e\u0026apos; denotes a significant difference from Summer, and \u0026apos;\u003csup\u003ec\u003c/sup\u003e\u0026apos; denotes a significant difference from Autumn\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe distribution characteristic of co-infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution characteristics of co-infections reveal that, among the 541 cases analyzed, 502 involved dual pathogens, significantly outnumbering the 39 cases of triple pathogen infections. Consequently, our primary focus was on the distribution characteristics of dual pathogens, with results illustrated in Figure 4. Seasonal analysis indicated that co-infections predominantly occurred in autumn and winter, with the most common infection pattern being HRV+Mp. The highest incidence of co-infections was recorded in winter, followed by HADV+Mp and HMPV+InfB as the second and third most prevalent patterns, respectively. In terms of age group distribution, co-infections were most common among preschool and school-age children, with HRV+Mp identified as the predominant infection pattern. Notably, the highest prevalence of co-infections was observed in the school-age group, where the three most common infection patterns were HRV+Mp, HADV+Mp, and HMPV+Mp.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study represents the first investigation into the role of 11 respiratory pathogens in acute respiratory infections among hospitalized children in the Putian area of southeast China, particularly following the suspension of strict COVID-19 management policies. The pathogens examined, including Mp, HRV, HRSV, and HPIV, were detected using RT-PCR. We subsequently analyzed their epidemiological characteristics, focusing on infection frequency, gender distribution, age groups, and seasonal patterns, to provide insights into the burden of these pathogens and to inform targeted prevention and treatment strategies for acute respiratory infections (ARIs) in children.\u003c/p\u003e \u003cp\u003eOur findings indicate that the overall positivity rate for the 11 pathogens analyzed was 78.9%, a figure comparable to the rates observed during periods of non-strict Non-Pharmaceutical Interventions (NPIs) in Shanghai[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] (78.7%), Zibo[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] (77.9%), and Shenzhen[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (80.5%), but significantly higher than during the strict NPI period. This suggests that, following the deregulation of NPIs, pathogen infection rates have reverted to pre-COVID-19 pandemic levels.\u003c/p\u003e \u003cp\u003eIn our study, the proportion of children enrolled during the spring season was the lowest, with an increasing number of participants observed from spring to summer and then to autumn. This trend aligns with findings reported by Xu et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although we accounted for the impact of NPIs in our study design and established a washout period of two months following the implementation of strict NPI policies, our data indicated a significant increase in enrollment numbers only five months after the policy was lifted.\u003c/p\u003e \u003cp\u003eHuman Rhinovirus (HRV) exhibited the highest positivity rate during the Spring Festival, which contrasts with the limited number of participants included in the spring season. This observation suggests that HRV is less influenced by non-pharmaceutical interventions (NPIs), consistent with previous studies conducted in New Zealand[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One potential explanation for this phenomenon is that HRV, being a non-enveloped virus, demonstrates reduced susceptibility to disinfectants such as alcohol, enabling it to remain viable on surfaces for extended periods[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, following the spring season, the positivity rates of other respiratory pathogens gradually increased, while HRV rates declined in comparison to those observed in spring. This finding stands in contrast to the research by Xu et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which reported consistently high HRV positivity rates throughout the pandemic. The dynamics observed may be related to viral interactions[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as the increasing prevalence of other viruses, which are less affected by NPI measures, could lead to the secretion of interferons that inhibit HRV infection.\u003c/p\u003e \u003cp\u003eSeveral long-term retrospective studies have identified HRSV and HMPV as seasonal diseases of significant prevalence, predominantly affecting children under the age of six[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This observation is consistent with our findings. Notably, in our study, the peak positivity rate for HRSV detection occurred in June, indicating a departure from the typical winter seasonality. This pattern corresponds with the epidemiological trends observed in the United States and Australia following the relaxation of Non-Pharmaceutical Interventions (NPIs)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. One possible explanation for this shift is that stringent NPI measures, which included school closures, restrictions on family gatherings, and the shutdown of restaurants and hotels, initially curtailed the transmission of respiratory viruses. Consequently, this may have led to a buildup of susceptible individuals, resulting in off-season outbreaks of pathogens that are typically associated with seasonal transmission[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, our study found that the annual average incidence rate of Human Metapneumovirus (HMPV) was 9.6%, significantly higher than the rates reported in previous studies, which indicated rates of less than 5%. This observation aligns with findings by Kuang et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], particularly following the relaxation of non-pharmaceutical intervention (NPI) strategies. The increase in HMPV infections may be attributed to the recurring cycle of mild or asymptomatic reinfections within households and communities, as well as the prolonged shedding of infectious viruses by individuals who have recovered from HMPV infection[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, a study examining pathogen prevalence before and after the implementation of strict NPI measures against the COVID-19 outbreak found that these measures significantly reduced HMPV infections. These findings indicate that greater emphasis should be placed on the prevention of HMPV in children under six years of age. Specifically, NPIs should be employed to prevent infection during social interactions with individuals who are currently experiencing or recovering from HMPV infection. Additionally, enhancing health education for HMPV patients and minimizing social interactions with children under six years of age during the infection period can prove effective.\u003c/p\u003e \u003cp\u003eSimilar to other respiratory pathogens, Mycoplasma pneumoniae (Mp) exhibits a distinct seasonal and demographic pattern. Previous studies have reported that Mp outbreaks primarily occur in autumn, predominantly affecting school-age children[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which aligns with our findings. In our study, Mp reached its peak in autumn but remained elevated throughout the summer, autumn, and winter, suggesting a potential Mp epidemic in 2023. A similar trend was noted in Guangdong Province[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This pattern may be attributed to the cessation of strict non-pharmaceutical intervention (NPI) policies, which have resulted in 48%-83.2% of the Chinese population being infected with COVID-19[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Prolonged immune dysfunction has been observed following COVID-19 infection, which may render children with immature immune systems more susceptible to Mp infection[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA pronounced peak in adenovirus (ADV) detection was observed during the winter months, while detection rates were significantly lower in other seasons, indicating a potential ADV outbreak in winter. This observation is consistent with a meta-analysis conducted in China, which systematically reviewed 950 articles on ADV outbreaks published between 2009 and 2020[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, the timing of ADV outbreaks coincided with the prevalence period of Mycoplasma pneumoniae, and our study frequently documented mixed infections of Mycoplasma pneumoniae and human adenovirus (HADV) during the winter. These findings suggest that in clinical practice, children presenting with persistent or recurrent fever after initial resolution should not be attributed solely to Mycoplasma pneumoniae infection; a thorough evaluation for the possibility of HADV infection is also warranted.\u003c/p\u003e \u003cp\u003eHowever, our study has limitations, as all data were collected from a single hospital, which may introduce selection bias. Additionally, bacterial data were not collected. Ongoing monitoring of respiratory pathogen epidemiological trends and further research are essential for enhancing the management of acute respiratory tract infections (ARTIs).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the epidemiological landscape of acute respiratory infections (ARIs) among pediatric patients in Putian City, China, has significantly changed following the cessation of stringent non-pharmaceutical interventions (NPIs). The findings indicate that the rate of pathogen positivity has reverted to levels observed prior to the COVID-19 pandemic, with Mycoplasma pneumoniae (Mp), Human rhinovirus (HRV), Human respiratory syncytial virus (HRSV), and Human metapneumovirus (HMPV) identified as the most prevalent pathogens. Pathogen distribution exhibited variations by age and season, with notable seasonal trends for several pathogens, including Influenza A (InfA), Human adenovirus (HADV), and Mp. These results underscore the importance of continuous surveillance and the development of tailored prevention strategies for respiratory infections, particularly in the context of evolving viral dynamics and the potential for off-season outbreaks. Ultimately, the findings highlight the necessity for a proactive, data-driven approach to managing ARIs in pediatric populations to mitigate associated morbidity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARI Acute Respiratory Infections \u003c/p\u003e\n\u003cp\u003eRT-PCR Reverse transcription polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eNPI Non-Pharmaceutical interventions\u003c/p\u003e\n\u003cp\u003eCOVID-19 Coronavirus disease 2019 \u003c/p\u003e\n\u003cp\u003eInfA Influenza virus A \u003c/p\u003e\n\u003cp\u003eInfB Influenza virus B\u003c/p\u003e\n\u003cp\u003eBoca Human Bocavirus \u003c/p\u003e\n\u003cp\u003eHRV Human Rhinovirus \u003c/p\u003e\n\u003cp\u003eHRSV Human Respiratory Syncytial Virus \u003c/p\u003e\n\u003cp\u003eHPIV Human Parainfluenza virus \u003c/p\u003e\n\u003cp\u003eHADV Human Adenovirus \u003c/p\u003e\n\u003cp\u003eHCOV Human Coronavirus \u003c/p\u003e\n\u003cp\u003eHMPV Human Metapneumovirus \u003c/p\u003e\n\u003cp\u003eCh Chlamydia\u003c/p\u003e\n\u003cp\u003eMp Mycoplasma Pneumoniae\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of the Affiliated Hospital of Putian University (approval number: 2024201). Written informed consent was obtained from all participants prior to participation in the study. All procedures adhered to the ethical standards stated in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be obtained from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Putian City Science and Technology Foundation - Putian City Science and Technology Plan Project (grant number 2024S3F005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.W. Zhu designed the study, conducted the data analysis, and drafted the manuscript. S.Q. Wu contributed the revision of the manuscript; Y. Chen; L.P. Zheng responsible for data collection; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the members of the Laboratory Department of the Affiliated Hospital of Putian University for their assistance in detecting respiratory pathogens, and to all participants for their valuable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinwei Zhu; Pediatrics Department, Section 2, Affiliated Hospital of Putian University\u003c/p\u003e\n\u003cp\u003eSuqing Wu; Department of Ultrasound, Affiliated Hospital of Putian University, Putian, China\u003c/p\u003e\n\u003cp\u003eYan Chen; Department of Laboratory Medicine, Affiliated Hospital of Putian University, Putian, China\u003c/p\u003e\n\u003cp\u003eLiping Zheng; Pediatrics Department, Section 1, Affiliated Hospital of Putian University, Putian, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators. GDaI. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020. doi: 10.1016/s0140-6736(20)30925-9.\u003c/li\u003e\n\u003cli\u003eMonto AS. Epidemiology of viral respiratory infections. Am J Med. 2002. doi: 10.1016/s0002-9343(01)01058-0.\u003c/li\u003e\n\u003cli\u003eUNICEF: Child health: Pneumonia (UNICEF Data). https://data.unicef.org/topic/child-health/pneumonia/ (2023). Accessed 13 Sept 2024.\u003c/li\u003e\n\u003cli\u003eHanada S, Pirzadeh M, Carver KY, Deng JC. Respiratory Viral Infection-Induced Microbiome Alterations and Secondary Bacterial Pneumonia. Front Immunol. 2018. doi: 10.3389/fimmu.2018.02640.\u003c/li\u003e\n\u003cli\u003eCampbell H, El Arifeen S, Hazir T, O\u0026apos;Kelly J, Bryce J, Rudan I, et al. Measuring coverage in MNCH: challenges in monitoring the proportion of young children with pneumonia who receive antibiotic treatment. PLoS Med. 2013. doi: 10.1371/journal.pmed.1001421.\u003c/li\u003e\n\u003cli\u003eChen Y, Shi K, Liu H, Yin Y, Zhao J, Long F, et al. Development of a multiplex qRT-PCR assay for detection of African swine fever virus, classical swine fever virus and porcine reproductive and respiratory syndrome virus. J Vet Sci. 2021. doi: 10.4142/jvs.2021.22.e87.\u003c/li\u003e\n\u003cli\u003eLi Y, Wang X, Blau DM, Caballero MT, Feikin DR, Gill CJ, et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in children younger than 5 years in 2019: a systematic analysis. Lancet. 2022. doi: 10.1016/s0140-6736(22)00478-0.\u003c/li\u003e\n\u003cli\u003eGao M, Yao X, Mao W, Shen C, Zhang Z, Huang Q, et al. Etiological analysis of virus, mycoplasma pneumoniae and chlamydia pneumoniae in hospitalized children with acute respiratory infections in Huzhou. Virol J. 2020. doi: 10.1186/s12985-020-01380-4.\u003c/li\u003e\n\u003cli\u003eChristopher Troeger BFB, Ibrahim A Khalil, Stephanie R M Zimsen, Samuel B. Albertson, Degu Abate, Jemal Abdela. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2019. doi: 10.1016/s2213-2600(18)30496-x.\u003c/li\u003e\n\u003cli\u003eLei C, Lou CT, Io K, SiTou KI, Ip CP, U H, et al. Viral etiology among children hospitalized for acute respiratory tract infections and its association with meteorological factors and air pollutants: a time-series study (2014-2017) in Macao. BMC Infect Dis. 2022. doi: 10.1186/s12879-022-07585-y.\u003c/li\u003e\n\u003cli\u003eZhao Y, Lu R, Shen J, Xie Z, Liu G, Tan W. Comparison of viral and epidemiological profiles of hospitalized children with severe acute respiratory infection in Beijing and Shanghai, China. BMC Infect Dis. 2019. doi: 10.1186/s12879-019-4385-5.\u003c/li\u003e\n\u003cli\u003eTang M, Dong W, Yuan S, Chen J, Lin J, Wu J, et al. Comparison of respiratory pathogens in children with community-acquired pneumonia before and during the COVID-19 pandemic. BMC Pediatr. 2023. doi: 10.1186/s12887-023-04246-0.\u003c/li\u003e\n\u003cli\u003eGao ZX, Wang Y, Yan LY, Liu T, Peng LW. Epidemiological characteristics of respiratory viruses in children during the COVID-19 epidemic in Chengdu, China. Microbiol Spectr. 2024. doi: 10.1128/spectrum.02614-23.\u003c/li\u003e\n\u003cli\u003eXu M, Liu P, Su L, Cao L, Zhong H, Lu L, et al. Comparison of Respiratory Pathogens in Children With Lower Respiratory Tract Infections Before and During the COVID-19 Pandemic in Shanghai, China. Front Pediatr. 2022. doi: 10.3389/fped.2022.881224.\u003c/li\u003e\n\u003cli\u003eLv G, Shi L, Liu Y, Sun X, Mu K. Epidemiological characteristics of common respiratory pathogens in children. Sci Rep. 2024. doi: 10.1038/s41598-024-65006-3.\u003c/li\u003e\n\u003cli\u003eLi L, Wang H, Liu A, Wang R, Zhi T, Zheng Y, et al. Comparison of 11 respiratory pathogens among hospitalized children before and during the COVID-19 epidemic in Shenzhen, China. Virol J. 2021. doi: 10.1186/s12985-021-01669-y.\u003c/li\u003e\n\u003cli\u003eHuang QS, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, et al. Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. Nat Commun. 2021. doi: 10.1038/s41467-021-21157-9.\u003c/li\u003e\n\u003cli\u003eWinther B, McCue K, Ashe K, Rubino JR, Hendley JO. Environmental contamination with rhinovirus and transfer to fingers of healthy individuals by daily life activity. J Med Virol. 2007. doi: 10.1002/jmv.20956.\u003c/li\u003e\n\u003cli\u003eSavolainen-Kopra C, Korpela T, Simonen-Tikka ML, Amiryousefi A, Ziegler T, Roivainen M, et al. Single treatment with ethanol hand rub is ineffective against human rhinovirus--hand washing with soap and water removes the virus efficiently. J Med Virol. 2012. doi: 10.1002/jmv.23222.\u003c/li\u003e\n\u003cli\u003eNickbakhsh S, Mair C, Matthews L, Reeve R, Johnson PCD, Thorburn F, et al. Virus-virus interactions impact the population dynamics of influenza and the common cold. Proc Natl Acad Sci U S A. 2019. doi: 10.1073/pnas.1911083116.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Arroyo L, Prim N, Del Cuerpo M, Mar\u0026iacute;n P, Roig MC, Esteban M, et al. Prevalence and seasonality of viral respiratory infections in a temperate climate region: A 24-year study (1997-2020). Influenza Other Respir Viruses. 2022. doi: 10.1111/irv.12972.\u003c/li\u003e\n\u003cli\u003eKuang L, Xu T, Wang C, Xie J, Zhang Y, Guo M, et al. Changes in the epidemiological patterns of respiratory syncytial virus and human metapneumovirus infection among pediatric patients and their correlation with severe cases: a long-term retrospective study. Front Cell Infect Microbiol. 2024. doi: 10.3389/fcimb.2024.1435294.\u003c/li\u003e\n\u003cli\u003eFoley DA, Yeoh DK, Minney-Smith CA, Martin AC, Mace AO, Sikazwe CT, 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. doi: 10.1093/cid/ciaa1906.\u003c/li\u003e\n\u003cli\u003eHamid S, Winn A, Parikh R, Jones JM, McMorrow M, Prill MM, et al. Seasonality of Respiratory Syncytial Virus - United States, 2017-2023. MMWR Morb Mortal Wkly Rep. 2023. doi: 10.15585/mmwr.mm7214a1.\u003c/li\u003e\n\u003cli\u003eBaker 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. doi: 10.1073/pnas.2013182117.\u003c/li\u003e\n\u003cli\u003eBell C, He C, Norton D, Goss M, Chen G, Temte J. Household transmission of human metapneumovirus and seasonal coronavirus. Epidemiol Infect. 2024. doi: 10.1017/s0950268824000517.\u003c/li\u003e\n\u003cli\u003eGao LW, Yin J, Hu YH, Liu XY, Feng XL, He JX, et al. The epidemiology of paediatric Mycoplasma pneumoniae pneumonia in North China: 2006 to 2016. Epidemiol Infect. 2019. doi: 10.1017/s0950268819000839.\u003c/li\u003e\n\u003cli\u003eLi Y, Wu M, Liang Y, Yang Y, Guo W, Deng Y, et al. Mycoplasma pneumoniae infection outbreak in Guangzhou, China after COVID-19 pandemic. Virol J. 2024. doi: 10.1186/s12985-024-02458-z.\u003c/li\u003e\n\u003cli\u003eFu D, He G, Li H, Tan H, Ji X, Lin Z, et al. Effectiveness of COVID-19 Vaccination Against SARS-CoV-2 Omicron Variant Infection and Symptoms - China, December 2022-February 2023. China CDC Wkly. 2023. doi: 10.46234/ccdcw2023.070.\u003c/li\u003e\n\u003cli\u003eLiu K, Fu HM, Lu Q. [Advancement in epidemiology of Mycoplasma pneumoniae pneumonia in children in China]. Zhonghua Er Ke Za Zhi. 2024. doi: 10.3760/cma.j.cn112140-20240407-00247.\u003c/li\u003e\n\u003cli\u003eLiu MC, Xu Q, Li TT, Wang T, Jiang BG, Lv CL, et al. Prevalence of human infection with respiratory adenovirus in China: A systematic review and meta-analysis. PLoS Negl Trop Dis. 2023. doi: 10.1371/journal.pntd.0011151.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prevalence, Acute Respiratory infections, Pathogens, Pediatric","lastPublishedDoi":"10.21203/rs.3.rs-5425847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute respiratory infections (ARIs) are a significant cause of morbidity in children. This study aimed to investigate the prevalence and distribution of respiratory pathogens in paediatric ARIs in Putian, China, following the cessation of strict non-pharmaceutical interventions (NPIs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 3,790 paediatric patients with suspected ARIs were included in the study. Nasopharyngeal swabs were collected and analyzed using RT-PCR to identify 13 common respiratory tract pathogens. Statistical analyses were performed to examine the distribution of pathogens among patients stratified by sex, age, and season.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall pathogen positivity rate was 78.9%. No significant difference in detection rates was observed between males (79.7%) and females (77.9%). The highest positivity rate was found in the school-age group, with elevated rates noted during autumn and winter. Among the positive cases, 81.9% had a single pathogen, with Mycoplasma pneumoniae (Mp) being the most common (33.6%), followed by Human rhinovirus (HRV) and Human respiratory syncytial virus (HRSV). Age-dependent distribution indicated that Influenza A (InfA) was more prevalent in preschool and school-age children, whereas HRSV was most prevalent in infants. Temporal distribution showed that InfA peaked in spring, while Mp, Human metapneumovirus (HMPV), and Human adenovirus (HADV) were most common in winter. Co-infections were more frequent in autumn and winter, with the HRV\u0026thinsp;+\u0026thinsp;Mp co-infection being the most prevalent pattern.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prevalence of respiratory pathogens in children with ARI has returned to pre-COVID-19 pandemic levels following the discontinuation of stringent NPIs. Additionally, the epidemiology of certain pathogens has shifted from traditional patterns. These findings underscore the dynamic nature of respiratory pathogen distribution and highlight the necessity for ongoing surveillance to inform effective treatment and prevention strategies for ARIs in children.\u003c/p\u003e","manuscriptTitle":"Prevalence and Distribution of Respiratory Pathogens in Paediatric Acute Respiratory Infections After the Cessation of Strict Non-Pharmaceutical Interventions in Putian, China.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 09:55:04","doi":"10.21203/rs.3.rs-5425847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-13T09:36:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T07:15:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T07:09:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-11-10T12:12:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea3c44db-0532-4619-a5e8-861a94af0886","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:11:18+00:00","versionOfRecord":{"articleIdentity":"rs-5425847","link":"https://doi.org/10.1186/s12879-025-10670-7","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-02-26 15:58:16","publishedOnDateReadable":"February 26th, 2025"},"versionCreatedAt":"2024-12-17 09:55:04","video":"","vorDoi":"10.1186/s12879-025-10670-7","vorDoiUrl":"https://doi.org/10.1186/s12879-025-10670-7","workflowStages":[]},"version":"v1","identity":"rs-5425847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5425847","identity":"rs-5425847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}