Epidemiological characteristics of acute viral and mycoplasma respiratory infections in Yongzhou, China: a retrospective descriptive study

Abstract

Background: Acute respiratory infections (ARIs) are major global public health concerns. Understanding the epidemiological characteristics and evolution patterns of respiratory pathogens before and after the coronavirus disease 2019 (COVID-19) pandemic is crucial for disease control and prevention. This study identified the epidemiological characteristics and pathogen distribution in hospitalized patients with ARIs in Yongzhou, China.

Methods: A retrospective analysis was conducted on 10,728 hospitalized patients with ARIs at a tertiary hospital in Yongzhou, China, from January 2019 to June 2024. Respiratory specimens were tested using standardized protocols including multiplex real-time PCR for detecting common respiratory pathogens (FluA and FluB, RSV, ADV, HRV, and MP) and next-generation sequencing for additional pathogen identification when clinically indicated. Statistical analyses included descriptive statistics for demographic and clinical characteristics, and chi-square tests for comparing categorical variables across different age groups, seasons, and time periods.

Results: Overall, 43.12% (4,626/10,728) of samples were positive for at least one pathogen. The most frequently detected pathogens were FluA (11.95%), RSV (9.61%), and MP (8.73%). RSV primarily affected children under 5 years (38.63% of RSV cases), while SARS-CoV-2 showed higher detection rates in older adult populations (64.81% of COVID-19 cases). Co-infections were found in 23.76% (1,099/4,626) of positive samples, with preschool children (2-5 years) showing the highest rate at 32.58%. MP (n = 398, 36.21% of all co-infections) with HRV (n = 338, 30.76% of all co-infections) were the most frequently detected pathogens in co-infections. Significant seasonal variations were observed, with winter showing the highest pathogen detection rates (49.04%, p < 0.001), and seasonal patterns changed notably during the COVID-19 pandemic. The frequency and patterns of co-infections showed marked differences across pre-pandemic, pandemic, and post-pandemic (January 2023-June 2024) periods. Co-infections occurred in 1,209 cases, with significant differences across study periods: 20.6% in pre-pandemic, 0.6% during pandemic, and 78.8% in post-pandemic periods (p < 0.001).

Conclusion: This study revealed distinct age-dependent and seasonal distribution patterns of respiratory pathogens in Yongzhou, China. The significant changes in pathogen circulation dynamics before, during, and after the COVID-19 pandemic highlight the importance of continuous surveillance of respiratory viruses. These findings provide valuable insights for optimizing local ARI prevention and treatment services.

Keywords: COVID-19; acute respiratory infections; co-infections; epidemic characteristics; respiratory pathogens.

Figure 1

Positive cases of patients based on different dimensions. (A) Stacked diagram of patients for different genders. (B) That of patients based on ages. (C) That of patients based on seasons, the red line represents all hospitalized patients in that season, and the blue line represents the positive patients in that season. (D) Bar graph showing the positivity rate of patients in different seasons annually.

Figure 2

Identification of viral etiologies based on virus-positive patients with ARIs. (A) Overall positivity rate of all viruses (“total” means overall positivity rate of all patients). (B) Bar graph showing the positivity rate by gender for each virus. (C) Pheatmap displaying positivity rate of viral infections across different age groups, with each row representing an age group and each column representing a virus.

Figure 3

Seasonal distribution (months) of different respiratory viruses. (A) Monthly positivity rate of different respiratory viruses and total from January 2019 to June 2024. (B) Total seasonal positive rates for five major viruses. (C) Annual seasonal positivity rate for five major viruses. (D) Peatmap illustrates positivity rate of FluA, FluB, and RSV across different age groups and months.

Figure 4

Co-infection patterns across different time periods. Pie charts showing proportions of different co-infection patterns, with (A–C) representing pre-epidemic period, during the epidemic, and post-epidemic.