Analysis of pediatric respiratory virus epidemiology and its association with atmospheric parameters in Chengdu

Introduction

The coronavirus disease 2019 (COVID-19) pandemic led to the widespread implementation of non-pharmaceutical interventions (NPIs), which altered the circulation patterns of various respiratory viruses, including influenza, respiratory syncytial virus, adenovirus, and rhinovirus (1). The period following the widespread relaxation of these stringent NPIs, often referred to as the “post-pandemic era,” is characterized by a rebound in the activity of various respiratory pathogens and a shift in their epidemiological dynamics, presenting new challenges for the public health system (2).

Atmospheric environmental parameters, encompassing meteorological factors (e.g., ambient temperature and relative humidity) and air pollutants [e.g., particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃)], are known to influence the transmission of respiratory viruses. These factors can affect viral stability in the environment, host susceptibility by impairing airway barrier function and immune responses, and population exposure patterns (3,4). Children are particularly vulnerable to respiratory infections. Understanding the evolving epidemiology of these viruses and their environmental drivers is crucial for public health planning.

However, comprehensive data on multiple respiratory viruses and their association with atmospheric factors in the post-pandemic era, particularly in Southwestern China, are limited. Therefore, this study aims to characterize the epidemiological profiles of multiple respiratory viruses in the post-pandemic era and preliminarily explore their correlations with atmospheric parameters, thereby providing a reference for the formulation of regionally tailored containment strategies and individualized protective measures for children. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-455/rc).

Methods

Study design and participants

Cases were retrospectively gathered from children under the age of 18 with acute respiratory infections (ARIs) who visited Chengdu Women’s and Children’s Central Hospital between January 2023 and December 2024. All cases satisfied the following criteria: (I) clinical symptoms: at least two respiratory infection symptoms (fever, cough, nasal congestion, rhinorrhea, sore throat, nasal/pharyngeal congestion); (II) clinical classification: diagnosed as an acute upper respiratory infection, acute bronchitis, or pneumonia by lung auscultation (moist rales/wheezing) or chest imaging (X-radiation/computed tomography). Cases diagnosed with pneumonia that met any of the following criteria were classed as severe pneumonia (5): (I) drowsiness/lethargy; (II) feeding refusal or indicators of dehydration; (III) dyspnea symptoms include tachypnea (infants ≥70 breaths/min, older children ≥50 breaths/min) or cyanosis; (IV) oxygen saturation (SpO₂) ≤92%; (V) chest imaging reveals multilobar infiltrates, >2/3 unilateral lobe involvement, or pleural effusion; (VI) extra-pulmonary problem.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective cohort study was approved by the Ethics Committee of the Chengdu Women’s and Children’s Central Hospital (No. 2023-113) and individual consent for this retrospective analysis was waived.

Specimen collection and testing

Pharyngeal swab samples were collected by trained medical staff on the day of the hospital visit. Samples were placed in sterile tubes with viral transport medium and transported at 4 ℃ to the laboratory within 2 hours. Multiplex real-time polymerase chain reaction (PCR) was used to detect five respiratory viruses at once: human respiratory syncytial virus (HRSV), influenza B virus (InfB), human adenovirus (HADV), influenza A virus (InfA), and human rhinovirus. Sansure Biotech Inc. provided the Six Respiratory Pathogen Nucleic Acid Detection Kit (PCR-fluorescent probe technique) for testing, and all protocols and result interpretations were strictly adhered to.

Data collection

Data were collected from two primary sources. Demographic information (sex, age), pathogen collection date, coinfection status, pediatric intensive care unit (PICU) admission status, and clinical diagnosis were extracted from electronic medical records. Patients were stratified into five age groups: infancy (<1 year), toddlerhood (1–2 years), preschool (3–5 years), school-age (6–11 years), and adolescence (12–18 years). The four seasons were defined as follows: Spring (March to May), Summer (June to August), Autumn (September to November), and Winter (December to February of the following year). Simultaneously, ambient air quality data for Chengdu from January 2023 to December 2024 were obtained from the China Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/historydata/). These included monthly averages of temperature (℃), relative humidity (%), AQI, PM_2.5 (µg/m³), PM₁₀ (µg/m³), NO (µg/m³), SO₂ (µg/m³), CO (mg/m³), and O₃ (µg/m³). All indicators underwent monthly statistical aggregation before further.

Statistical analysis

Data were processed and analyzed using SPSS software (version 26.0; IBM Corp., Armonk, NY, USA). Categorical data were presented as counts and percentages (n, %). Chi-squared (χ²) tests were employed for intergroup comparisons of categorical variables, such as comparing positivity rates across different sex, age, and season groups. Associations between monthly virus positivity rates and atmospheric parameters were assessed using Pearson correlation analysis. Univariate linear regression models were constructed to quantify the relationships between virus positivity rates (dependent variable) and the single atmospheric parameter with which each virus showed the strongest correlation (independent variable). The goodness-of-fit for each model was determined by the coefficient of determination (R²). Multicollinearity among independent variables was assessed using the variance inflation factor (VIF); a VIF value of less than 5 was considered indicative of no significant multicollinearity. A two-tailed P value of less than 0.05 was considered statistically significant for all tests, including the correlation analyses and model fitness evaluations.

Results

Respiratory virus nucleic acid testing results

Among 322,238 children with ARIs, 30,523 tested positive for viral nucleic acids, yielding an overall positivity rate of 9.47%. Pathogen distribution was: HRV: 12,379 cases (3.84%); HADV: 8,025 cases (2.49%); HRSV: 5,998 cases (1.86%); InfA: 2,909 cases (0.90%); InfB: 1,212 cases (0.38%). Positivity rates in ascending order were: InfB (0.38%) < InfA (0.90%) < HRSV (1.86%) < HADV (2.49%) < HRV (3.84%).

Sex distribution differences in respiratory virus infections

Of the 322,238 children, 179,485 were male (17,393 positive) and 142,753 were female (13,130 positive), with a male-to-female positive case ratio of 1.32:1. The overall viral positivity rate was significantly higher in males. Subgroup analysis showed a statistically significant difference in InfB positivity between genders, but no significant differences for HRSV, HADV, InfA, or HRV (Table 1).

Age distribution characteristics of respiratory virus infections

Positivity rates varied significantly by age group, highest in preschoolers (10.58%) and lowest in adolescents (5.94%). Age-specific peak positivity rates were observed: HADV peaked in preschoolers (3.42%); HRSV in infants (3.20%); HRV in toddlers (4.53%); InfB in school-age children (0.76%); and InfA in adolescents (3.42%) (Table 2).

Monthly distribution characteristics of respiratory viruses

The five respiratory viruses exhibited distinct temporal patterns throughout the study period (Figure 1). HRSV demonstrated a biphasic circulation, with a primary peak in April 2023 (positivity rate: 35.27%) followed by a secondary, smaller peak in February 2024. In contrast, InfB exhibited a unimodal and confined epidemic window, with activity predominantly concentrated between November 2023 and January 2024 (peak: 12.40% in December), remaining at low levels (<1%) for the rest of the period. HADV showed a contrasting summer predominance. Its activity gradually increased from early 2023, culminating in an intense surge during the summer months of 2024, with the peak observed in July (30.10%). InfA activity was characterized by stacked bimodal peaks. An initial peak occurred in March 2023, followed by a second peak that coincided with the InfB epidemic period in late 2023. Finally, HRV caused recurrent outbreaks throughout the year at approximately two-month intervals, with a notable upward trend in the amplitude of these peaks over time, reaching its highest level in October 2024. The temporal trend in the absolute number of nucleic acid-positive cases for each virus followed a similar pattern to the positivity rates shown in Figure 1 (see Figure S1).

Figure 1 Monthly distribution of respiratory virus positivity rates during the study. HADV, human adenovirus; HRSV, human respiratory syncytial virus; HRV, human rhinovirus; InfA, influenza A virus; InfB, influenza B virus.

Seasonal distribution characteristics of respiratory viruses

The nucleic acid positivity rates of respiratory viruses demonstrated significant seasonal variation. The overall positivity rate was highest in winter (10.90%) and lowest in summer (7.68%). Each virus exhibited a distinct seasonal pattern: both HRSV and influenza viruses (InfA, InfB) were most prevalent in winter, with their positivity rates increasing in the order of summer, autumn, spring, and winter. Notably, the positivity rate of HRSV reached 3.46% in winter. In contrast, HADV showed a unique summer predominance, with its positivity rate in summer (3.69%) being significantly higher than in other seasons. HRV was most active in autumn, with a positivity rate of 4.94% (Table 3).

Clinical characteristics of respiratory viruses

The five respiratory viruses demonstrated distinct patterns in clinical manifestations. HRSV was associated with the highest PICU admission rate (4.13%,), contrasting sharply with HADV, which showed the lowest rate (0.66%). InfA exhibited the highest co-infection rate (42.04%), while HRSV demonstrated the lowest frequency of viral co-detections (26.39%). Disease spectrum analysis revealed virus-specific tropism. HADV accounted for the highest proportion of upper respiratory tract infections (52.25%). InfA showed a predilection for bronchitis cases (21.52%). Conversely, HRSV demonstrated the strongest association with pneumonia (71.96%) and particularly with severe pneumonia (5.79%). All observed differences in clinical presentation were statistically significant (Table 4).

Correlation between atmospheric parameters and nucleic acid positivity rates of respiratory viruses

Correlation analyses between monthly virus positivity rates and atmospheric parameters revealed significant associations. HRSV positivity correlated negatively with temperature. InfB positivity correlated negatively with temperature and O₃, but positively with PM_2.5, PM₁₀, NO₂, and CO. HADV positivity correlated positively with temperature but negatively with PM_2.5, PM₁₀, and NO₂. InfA positivity correlated negatively with temperature and positively with PM₁₀ and NO₂. HRV positivity correlated negatively with PM_2.5, PM₁₀, and AQI. No significant correlations were found with relative humidity for any virus (Table 5).

Linear regression analysis of atmospheric parameters and virus positivity rates

Based on prior correlation analyses, key linear regression models between viral positivity rates and their strongest-associated environmental parameters were established. All models were statistically significant and showed no collinearity. The specific models are as follows: HRSV vs. temperature: Y=17.939−0.490*X (R²=0.170); InfB vs. CO: Y=−12.966+23.976*X (R²=0.496); HADV vs. PM₁₀: Y=23.433−0.492*X (R²=0.204); InfA vs. NO₂: Y=−7.951+0.509*X (R²=0.332); HRV vs. AQI: Y=40.780 − 0.311*X (R²=0.431). Y represents the viral positivity rate, and X represents the corresponding environmental parameter.

Discussion

Chengdu Women’s and Children’s Central Hospital is a leading tertiary maternal and child specialty hospital in Southwest China. The inclusion of 322,238 pediatric ARIs cases provides representative data for analyzing respiratory virus epidemiology in this region.

Following the relaxation of COVID-19 NPIs, respiratory viruses have resurged, with HRV showing the most notable rebound (6). The absolute dominance of HRV and its recurrent epidemic pattern in our study are closely related to the environmental stability conferred by its non-enveloped structure (7,8). This provides critical implications for formulating targeted infection control strategies, consequently underscoring the necessity of placing greater emphasis on thorough disinfection of environmental surfaces when preventing and controlling such viruses.

Consistent with previous studies, males had significantly higher overall and InfB positivity rates than females (9,10). We speculate that this discrepancy might be related to the more common exposure behaviors in public places that men engage in. We observed significant differences in the age distribution of viral infections. HRSV, HRV, and HADV primarily affected infants and young children (<5 years), whereas influenza viruses (InfA, InfB) were more common in school-aged children. This disparity may stem from the immature immune systems of young children, their lack of pre-existing immunity to multiple viral serotypes, and their specific behavioral patterns (e.g., frequent hand-to-mouth contact) (11-13). In contrast, the more frequent indoor group activities among school-aged children increase their exposure risk to influenza viruses transmitted via droplets. These findings support tiered prevention strategies: enhanced environmental disinfection using virucidal agents effective against non-enveloped viruses for young children, and increased influenza vaccination and improved ventilation for older children.

Temporally, each virus exhibited its inherent seasonal pattern. The typical winter-spring epidemic peaks of HRSV and influenza viruses align with the fact that low-temperature environments facilitate their transmission. Notably, the pandemic peak of InfA often occurred 1–2 months before that of HRSV. As a result, it is recommended to implement HRSV-specific preventative and control strategies prior to the conclusion of the local InfA epidemic phase. In contrast, HADV’s unique summer epidemic trend is consistent with its positive correlation with temperature. A notable finding was that the HADV positivity rate in this study (3.69%) was significantly lower than the 5.72% reported in previous studies from Northern China (14). The year-round circulation of HRV with increasingly higher peaks highlights its strong transmission adaptability and potential evolutionary pressure. It is necessary to employ molecular epidemiological techniques for more comprehensive studies to clarify the characteristics of HRV transmission dynamics (15).

Clinically, HRSV infection accounted for the highest burden of lower respiratory tract infections and severe disease. Its high PICU admission rate and proportion of severe pneumonia should warrant heightened alertness among clinicians (16,17). Although InfA had the highest co-infection rate, the severity of the disease it caused was milder, which may reflect the distinct pathogenic mechanisms of different viruses.

One of the most significant findings of this study is the revelation of specific associations between atmospheric parameters and viral transmission. Beyond confirming the expected relationship between temperature and the activity of most viruses, we are the first to report a negative correlation between PM_2.5/PM₁₀ and the positivity rates of HADV and HRV. A possible explanation is that particle adsorption may accelerate the sedimentation of viral aerosols, shortening their airborne suspension time and thus reducing transmission opportunities. Furthermore, the negative correlation between O₃ and InfB aligns with its virus-inactivating oxidative properties. These findings underscore the complex impact of the atmospheric environment on virus transmission, the mechanisms of which are far from singular. In contrast to most previous studies, our investigation found no significant association between relative humidity and viral activity (18-20). Future research requires the development of a multidimensional humidity evaluation framework that includes simultaneous monitoring of relative humidity, absolute humidity, and humidity fluctuation range in order to systematically elucidate the mechanisms by which humidity affects viral transmission.

There are several limitations in this study. First, it was a single-center, retrospective study, with all data sourced from a single medical institution. Although our center is one of the largest maternal and child specialty hospitals in Southwest China and the data possess certain regional representativeness, the generalizability of the findings (particularly to populations in different geographical and climatic conditions) should be interpreted with caution. Second, the retrospective design relied on electronic medical records for data extraction, and some potential confounding factors (such as detailed socioeconomic status, vaccination history, and household environment of the patients) were not fully captured or controlled for. Third, the association between the atmospheric environment and viral transmission is complex. This study only analyzed a limited set of pollutants (e.g., PM_2.5, NO₂) and meteorological parameters (temperature, relative humidity), failing to incorporate other potentially important environmental variables (e.g., absolute humidity, ultraviolet radiation, wind speed) and indoor air quality data, which might affect a comprehensive assessment of exposure effects. Fourth, the study’s timeframe was relatively short [2023–2024], covering only two full annual cycles. This may be insufficient to capture the long-term natural fluctuations and trends of respiratory viruses or to adequately assess the impact of interannual variability. Future research should prioritize multicenter, prospective long-term surveillance, integrating pathogen genomics, expanded environmental indicators, and individual-level exposure and immunological data to build more accurate predictive models and elucidate the underlying mechanisms.

Conclusions

This study delineates the epidemiological profiles of five common respiratory viruses among children in Chengdu during the post-pandemic period and reveals statistically significant associations with specific atmospheric parameters. The high prevalence of non-enveloped viruses (HRV and HADV) and the substantial burden of HRSV-related severe disease underscore the importance of regionally tailored surveillance and clinical management. The observed correlations with meteorological and environmental factors suggest possible influences on viral activity, although causative mechanisms require further investigation.

These findings provide preliminary, locally derived evidence that may inform the formulation of targeted public health strategies in Southwest China. However, the single-center design and limited timeframe necessitate cautious interpretation. Future multicenter, longitudinal studies incorporating broader virological and environmental data are needed to validate these associations and explore their generalizability.

Acknowledgments

We would like to thank Grammarly (www.grammarly.com) for English language editing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-455/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-455/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-455/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-455/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective cohort study was approved by the Ethics Committee of the Chengdu Women’s and Children’s Central Hospital (No. 2023-113) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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