ABSTRACT
We conducted a retrospective study using data from Zhejiang Province’s pertussis surveillance system between February 1 and May 1, 2024, defining school-based clusters as ≥5 cases occurring within 21 d in the same class. We identified 124 outbreaks involving 825 confirmed cases, with cluster sizes ranging from 5 to 16 cases (median: 7). Most outbreaks occurred in primary schools (79.03%, 98/124), followed by kindergartens (19.35%, 24/124) and middle schools (1.61%, 2/124). Among cases, 19.3% (159/825) were index cases (predominantly aged 7–8 y) and 80.7% (666/825) were secondary infections. The median serial interval was 9.0 d (IQR: 5.0–13.0); it was significantly longer in kindergarten than in primary school cases (10.0 vs. 9.0 d, P = .007) and shorter with earlier diagnosis (7–14 d post-onset) (7.0 vs 10.0 d, P = .007). Our findings emphasize the importance of protecting high-risk school populations, implementing prompt outbreak control measures, and conducting timely health education to prevent pertussis transmission.
KEYWORDS: Pertussis, school, transmission, serial interval
Introduction
Pertussis, caused by Bordetella pertussis,1 is a highly contagious respiratory disease classified as a Class B infectious disease under China’s Law on the Prevention and Treatment of Infectious Diseases.2 Following the COVID-19 pandemic, China has faced new challenges and characteristics in the prevention and control of certain infectious diseases.3 Despite high routine immunization rates among children in some countries, the incidence of pertussis has shown a rapid upward trend. In the first quarter of 2024, China reported over 58,990 cases of pertussis, a tenfold increase compared to the same period in the previous year.4 Although the vaccination coverage among age-eligible children in Zhejiang Province has remained above 95%,5 the incidence of pertussis among school-aged children (3–9 y) has exceeded that among infants. Furthermore, 79.2% of confirmed cases had completed the full schedule of pertussis vaccination, a phenomenon that has been linked to the waning of vaccine-induced immunity over time.6 Additionally, the risk of clustered outbreaks in schools, childcare institutions, and households is significantly elevated.7 The characteristics of school and childcare settings, including their densely populated environments, have facilitated the widespread transmission of potential infection sources, significantly contributing to the rise in disease incidence.
In epidemiology, the serial interval, defined as the time interval between the onset of symptoms in a primary case and the onset of symptoms in a secondary case it infects, is a critical parameter for measuring the speed of disease transmission and assessing the effectiveness of intervention measures.8 While studies on the serial interval have been conducted for various infectious diseases, such as COVID-19,9,10 influenza,11 monkeypox,12 and tuberculosis,13 research specific to pertussis remains limited. Although some studies have examined the serial interval of pertussis transmission within households,14,15 While there have been studies on pertussis outbreaks in schools in South Korea,16 the role of schools as high-incidence settings for pertussis in China has not yet been fully assessed. Currently, there is a lack of large-scale data to support robust estimates of this metric in school settings. Given the significant contribution of school outbreaks to overall pertussis transmission, accurately calculating the serial interval and secondary attack rate in these environments is crucial. This not only enhances our ability to predict disease transmission patterns but also provides a solid evidence base for developing effective vaccination strategies and implementing targeted public health interventions to control further outbreaks.
Therefore, we conducted a retrospective analysis based on reported cases, matching specific classroom addresses within schools, to identify clustered outbreaks. We analyzed the age distribution of school outbreaks and investigated the variations in serial intervals among age groups along with their influencing factors. The aim of this study is to enhance the understanding of pertussis transmission dynamics in school settings and to inform the development of targeted prevention strategies.
Materials and methods
Data source
The study utilized data from Zhejiang Province’s pertussis surveillance system. According to provincial protocol, the Chinese Centers for Disease Control and Prevention (China CDC) personnel were required to complete epidemiological investigations within 48 hours of case notification. All data used in this study were obtained with formal permission from the Zhejiang Provincial CDC; the raw surveillance data are confidential and not publicly available to external parties. We conducted a retrospective analysis of laboratory-confirmed pertussis cases reported between February 1 and May 31, 2024, using standardized case investigation forms and questionnaire (Supplementary Material). Cases were geotemporally matched by classroom and date of symptom onset.
Case definition
Individual case
Case confirmation followed China’s Pertussis Prevention and Control Plan (2024 Edition)17 and the Pertussis Diagnosis and Treatment Protocol (2023 Edition).18 Confirmed cases were defined as laboratory-confirmed cases derived from suspected or clinically diagnosed cases that meet any of the following criteria: (1) Isolation of Bordetella pertussis through culture; (2) Positive nucleic acid detection for Bordetella pertussis; (3) Positive seroconversion of PT-IgG antibodies or a ≥4-fold increase in antibody titer between the convalescent and acute phases (excluding infants who received pertussis-containing vaccines within the past year or had a history of prior infection). Only laboratory-confirmed cases as defined above were included in the subsequent clustered outbreak analysis and all statistical analyses of this study.
Clustered outbreak
Cluster definitions aligned with the Guidelines for the Investigation and Management of Clustered Pertussis Outbreaks19 and the Zhejiang Province Pertussis Outbreak Investigation and Management Guidance (2024 Version).20
A pertussis cluster was operationally defined as ≥5 laboratory-confirmed (predominantly PCR-based nucleic acid positive) cases among students who: (1) shared the same physical classroom, (2) exhibited symptom onset within 21 d of each other, and (3) had no other plausible exposure sources.
Index case and subsequent case
Index case was defined as the earliest case in an outbreak based on the date of onset. If two cases in an outbreak had the same earliest date of onset, both were considered Index cases. A subsequent case is defined as a pertussis case that occurs in the same classroom as the index case of a clustered outbreak, has a later onset time than the index case, and is epidemiologically linked to the outbreak cluster.
Survey content
We extracted demographic (including gender, age, location), clinical information (including date of onset, date of confirmation, symptoms, treatment) and laboratory data (such as culture, nucleic acid detection, and serological tests) from epidemiological investigations. Immunization history, and classroom affiliation were verified through provincial electronic records. This multidimensional dataset allowed us to characterize the epidemiology of pertussis outbreaks in schools and identify key risk factors. We used a complete case analysis for all statistical comparisons in this study and did not perform data imputation.
Statistical analysis
Statistical analyses were performed using R 4.3.2 software. Measurement data with a normal or approximately normal distribution are expressed as mean ± standard deviation, and comparisons between groups were conducted using the independent two-sample t-test. Measurement data with a skewed distribution are expressed as median and interquartile range, and comparisons between groups were conducted using non-parametric tests, enumeration data are presented as frequency or percentage. A difference was considered statistically significant at a P-value of <.05.
Results
Overview of pertussis clusters
A total of 124 classroom-confined school-based pertussis clusters were identified across 9 prefecture-level municipalities and 42 county-level districts, involving 825 laboratory-confirmed cases. The scale of cluster ranged from 5 to 16 cases per classroom, with a median of 7 cases. Clustered outbreaks accounted for 62.90% of cases in March (78 out of 124), and 36.29% of cases in April (45 out of 124). The clusters occurred in kindergartens (19.35%, 24/124), primary schools (79.03%, 98/124), and middle schools (1.61%, 2/124).
Three municipalities accounted for the majority of outbreaks: Hangzhou reported the highest number of distinct outbreaks (n = 58, 46.77% of total), followed by Ningbo (n = 18, 14.52%) and Wenzhou (n = 15, 12.10%). Hangzhou exhibited the greatest variability in outbreak size, with affected classrooms reporting 5–16 cases per outbreak. Ningbo and Wenzhou showed more moderate outbreak patterns, with 5–10 cases per affected classroom. Smaller municipalities including Jiaxing and Lishui demonstrated contained transmission, with all outbreaks limited to 5–6 cases per classroom. Jinhua and Zhoushan similarly reported predominantly small-scale outbreaks at the 5-case baseline (Figure 1).
Figure 1.
Distribution of pertussis outbreaks by region in Zhejiang Province.
Epidemiological and clinical characteristics of cases
Epidemiological characteristics
Among the 825 cases, 159 (19.3%) were Index cases and 666 (80.7%) were subsequent cases. Detailed epidemiological characteristics were presented in Table 1. Cases were evenly distributed by sex (51.52% male, 48.48% female), with the highest proportion occurring in children aged 7–8 y (40.85%, 337/825). The majority of cases occurred in primary and secondary schools (82.30%, 679/825), while preschools accounted for 17.70% (146/825). Among them, 98.52% (796/825) of cases had received 4 doses of pertussis vaccine, 94.30% (778 cases) received antibiotic therapy, 11.03% (91 cases) received antibiotics before diagnosis, and 75.27% (621 cases) received antibiotics after diagnosis.
Table 1.
Characteristics distribution of index cases and subsequent cases.
| Total (N = 825) N(%) |
Index cases (N = 159) N(%) |
Subsequent cases (N = 666) N(%) |
p-value | |
|---|---|---|---|---|
| Sex | ||||
| Male | 425 (51.52) | 88 (55.35) | 337 (50.60) | .282 |
| Female | 400 (48.48) | 71 (44.65) | 329 (49.40) | |
| Age | ||||
| ≤6 y | 250 (30.30) | 43 (27.04) | 207 (31.08) | .094 |
| 7–8 y | 337 (40.85) | 77 (48.43) | 260 (39.04) | |
| ≥9 y | 238 (28.85) | 39 (24.53) | 199 (29.88) | |
| Institution | ||||
| Preschools | 146 (17.70) | 26 (16.35) | 120 (18.02) | .621 |
| Primary and secondary schools | 679 (82.30) | 133 (83.65) | 546 (81.98) | |
| Vaccinationa | ||||
| One dose | 3 (0.36) | 1 (0.63) | 2 (0.30) | .495 |
| Two dose | 1 (0.12) | 0 (0.00) | 1 (0.15) | |
| Three dose | 9 (1.09) | 1 (0.63) | 8 (1.20) | |
| Four dose | 796 (96.49) | 152 (95.60) | 644 (96.70) | |
| Antibiotic treatmenta | ||||
| Yes | 778 (94.30) | 150 (94.34) | 628 (94.30) | .546 |
| No | 29 (3.52) | 7 (4.40) | 22 (3.30) | |
| Time of medicationa | ||||
| Before diagnosis | 91 (11.03) | 15 (9.43) | 76 (11.41) | .553 |
| After diagnosis | 621 (75.27) | 125 (78.62) | 496 (74.48) | |
| The interval between onset and diagnosisa | <.001 | |||
| <7 d | 169 (20.48) | 30 (18.87) | 139 (20.87) | |
| 7–14 d | 132 (16.00) | 36 (22.64) | 96 (14.41) | |
| ≥14 d | 216 (26.18) | 63 (39.62) | 153 (22.97) | |
aFor vaccination, 809 cases were included in the analysis with 16 cases missing. For antibiotic treatment, 807 cases were included with 18 cases missing. For time of medication, 712 cases were included with 113 cases missing. For the interval between onset and diagnosis, 517 cases were included with 308 cases missing. All missing data were excluded from all analyses in this study. p-value < .05 was considered statistically significant.
In the total number of cases, 169 cases (20.48%) were diagnosed within 7 d after the onset of illness, 132 cases (16.00%) were diagnosed between 7 and 14 d after the onset of illness, 216 cases (26.18%) were diagnosed 14 d or more after the onset of illness. The difference of intervals from onset to diagnosis between Index cases and subsequent cases was statistically significant (χ2 = 37.260, P < .001) (Table 1). The overall median onset-to-diagnosis interval was 12.00 d (4.00,12.00). Specifically, the index case had a median interval of 13.00 d (8.00,23.00), while other cases showed a shorter median interval of 11.00 d (4.00,19.00).
Hematological indicator distribution and clinical characteristics
The distribution of hematological indicators was presented as median (interquartile range) in Table 2. The white blood cell (WBC) counts between the Index cases and other cases show a statistically significant difference (Z = 2.011, P = .044).
Table 2.
Distribution of clinical symptoms in pertussis between index and subsequent cases.
| Total (n = 825) |
Index cases (n = 159) |
Subsequent cases (n = 666) |
p-value | |
|---|---|---|---|---|
| Hematological Indicator，Median (Q1, Q3) | ||||
| White Blood Cell Count (×^/L), | 9.20 (7.65, 11.10) |
10.14 (8.19, 11.45) |
8.98 (7.40, 11.08) |
.044 |
| Neutrophil Percentage (%) | 52.60 (45.40, 61.00) |
54.60 (47.00, 62.15) |
52.00 (45.40, 60.40) |
.398 |
| Lymphocyte Percentage (%) | 36.60 (28.90, 44.25) |
36.10 (29.58, 43.50) |
36.60 (28.75, 44.30) |
.884 |
| Clinical Symptom，n (%) | ||||
| Paroxysmal Convulsive Cougha | .301 | |||
| Yes | 337 (40.85) | 58 (36.48) | 279 (41.89) | |
| No | 454 (55.03) | 96 (60.38) | 358 (53.75) | |
| High-Pitched Inspiratory Stridora | .893 | |||
| Yes | 53 (6.42) | 9 (5.66) | 44 (6.61) | |
| No | 739 (89.58) | 144 (90.57) | 595 (89.34) | |
| Nighttime Cough Exacerbationa | .810 | |||
| Yes | 427 (51.76) | 80 (50.31) | 347 (52.10) | |
| No | 362 (43.88) | 73 (45.91) | 289 (43.39) | |
| Vomitinga | .255 | |||
| Yes | 112 (13.58) | 28 (17.61) | 84 (12.61) | |
| No | 681 (82.54) | 125 (78.62) | 556 (83.48) | |
| Cyanosisa | .879 | |||
| Yes | 12 (1.45) | 3 (1.89) | 9 (1.35) | |
| No | 782 (94.79) | 150 (94.34) | 632 (94.90) | |
| Subconjunctival Hemorrhagea | .207 | |||
| Yes | 4 (0.49) | 2 (1.26) | 2 (0.30) | |
| No | 789 (95.63) | 150 (94.34) | 639 (95.95) | |
| Fevera | .596 | |||
| Yes | 93 (11.27) | 19 (11.95) | 74 (11.11) | |
| No | 700 (84.85) | 136 (85.54) | 564 (84.69) | |
aFor paroxysmal convulsive cough, 791 cases were included in the analysis with 34 cases missing. For high-pitched inspiratory stridor, 792 cases were included with 33 cases missing. For vomiting, 793 cases were included with 32 cases missing. For cyanosis, 794 cases were included with 31 cases missing. For subconjunctival hemorrhage, 793 cases were included with 32 cases missing. For fever, 793 cases were included with 32 cases missing. p-value <.05 was considered statistically significant.
Clinical manifestations were comparable between index (n = 159) and subsequent cases (n = 666). Paroxysmal cough and nocturnal exacerbation were most common, while inspiratory whoop and post-tussive vomiting occurred less frequently. Severe manifestations (cyanosis, subconjunctival hemorrhage) were rare (<2%). Fever incidence was similar (Table 2).
Serial interval distribution
The overall median serial interval was 9.0 d (IQR: 5.0–13.0). No significant differences were observed by sex (male: 9.0 d vs female: 8.0 d; P = .644) or age group (≤6 y: 9.0 d; 7–8 y: 9.0 d; ≥9 y: 9.0 d; P = .967). Significant temporal variation was noted, with intervals increasing from 7.0 d (March) to 14.0 d (May) (P < .001). Kindergarten cases had a significantly longer median serial interval than their primary/junior high school counterparts (10.0 vs 9.0 d, P = .007). Meanwhile, diagnosis at 7–14 d post-onset was associated with a shorter serial interval compared with diagnosis within 7 d of onset (7.0 vs 10.0 d, P = .007). Vaccination status (P = .137) and antibiotic treatment (P = .640) showed no significant effects (Figure 2).
Figure 2.
Distribution of serial interval across different characteristics.
Discussion
In the distribution of pertussis cases across all age groups, the increase in incidence among children aged 5–9 y is notable.21 This age group primarily spends their time in primary schools and childcare facilities, which are densely populated environments where children interact frequently and closely. These settings create highly favorable conditions for the spread of infectious diseases. Once a pertussis case emerges, it can quickly lead to cluster outbreaks. Schools and childcare facilities, as hubs of child gatherings, act as a “transmission hotbed,” where the virus can rapidly spread across classrooms, playgrounds, cafeterias, and other areas. In this context, conducting pertussis cluster outbreak investigations in schools and childcare facilities is crucial. Such investigations not only help identify potential risks early but also enable the implementation of effective control measures to halt disease transmission, prevent further infections, and protect children’s health and normal learning activities. Additionally, these investigations provide valuable experience and data to the broader public health system, supporting targeted strategies for managing infectious diseases in specific populations and ensuring social stability.
From our overall investigation results, most cluster outbreaks involved around 5 cases, with a low likelihood of large-scale clustering, and cases were mostly scattered. Even with a high vaccination coverage of four doses, cluster outbreaks predominantly occurred in primary and middle schools, where the Index cases and subsequent cases were mostly aged 7–8 y. This phenomenon can likely be attributed to two key factors: first, vaccine efficacy decline, as children vaccinated against pertussis during early childhood may experience a waning of protective antibody levels by the time they reach primary and middle school, leaving them vulnerable to infection; second, complex social interactions, as primary and middle school students engage in more extensive and varied social activities compared to children in childcare facilities, increasing their exposure to potential transmission.Cases were predominantly distributed in April (57.33%). The climatic conditions may favor the survival and transmission of Bordetella pertussis.
The serial interval, defined as the time interval between consecutive cases in a transmission chain, reflects the temporal characteristics of disease spread.22 Based on our measurements, the serial interval for school cluster outbreaks was 9.00 (5.00, 13.00) days, likely due to the broader contact range, higher contact frequency, and larger population size in schools compared to households, which may prolong the serial interval as the pathogen encounters individuals with varying immune statuses during transmission. Additionally, the timing of medication also influenced the serial interval: cases treated before diagnosis had a longer interval of 10.00 (6.75, 13.25) days, while those treated after diagnosis had a shorter interval of 9.00 (5.00, 13.00) days. Similarly, the time from onset to diagnosis played a role, with cases diagnosed within <7 d exhibiting a significantly higher serial interval of 10.00 (6.00, 14.00) days, compared to those diagnosed between 7–14 d (7.00 (4.00, 12.00) days) and those diagnosed after ≥14 d (8.00 (5.00, 12.00) days). These findings are from exploratory subgroup analyses only.
Our results are comparable to a study from South Korea on the serial interval of pertussis in schools, which reported a mean serial interval of 9.5 d (SD 1.6).16 In our study, the serial interval for pertussis was longer than that of other respiratory diseases such as COVID-1923 (4.6 d, 95% CI: 3.7–5.5 d), H3N2 influenza (2.2 d), H1N1 pdm09 influenza (2.8 d), and respiratory syncytial virus (7.5 d), but shorter than that of measles (11.7 d), varicella (14.0 d), smallpox (17.7 d), mumps (18.0 d), and rubella (18.3 d).8 This variation may be influenced by several factors, including the pathogen’s virulence and infectivity,24 the transmission mode and infection process of Bordetella pertussis, differences in population immunity (the general susceptibility during the early stages of COVID-19), variations in immune response type and duration compared to diseases like measles,25 Social behavior patterns (e.g., social distancing and hygiene practices) and disease-specific control measures account for transmission variations across different settings. Notably, the pertussis serial interval was longer in kindergartens than in primary and middle schools, likely due to immature hygiene behaviors among young children – such as improper handwashing, poor cough and sneeze etiquette, and limited ability to communicate physical discomfort. This finding aligns with previous research that has identified age-related behavioral deficiencies as a key driver of prolonged pertussis serial intervals in preschool populations,16 which may lead to delayed case detection and extended transmission cycles, as educators may face challenges in identifying all cases in a timely manner.
In our current immunization program, the pertussis antibodies in school-age children may not provide sufficient protection, making school settings particularly vulnerable to cluster outbreaks. These outbreaks are challenging to manage, often requiring quarantines and class suspensions that disrupt normal educational activities. To mitigate this, it is crucial to promote vaccination awareness through educational campaigns aimed at students, parents, and staff, emphasizing the importance and necessity of vaccination to encourage proactive immunization. Additionally, enhancing disease surveillance by thoroughly tracking and registering students with absenteeism due to illness, as well as improving prevention and control capabilities by strengthening awareness and skills in disease prevention, are essential steps to address the unique challenges posed by pertussis in school settings.
Our study has certain limitations. It only included cluster cases from the high-incidence period of February to May, which may not fully represent the broader transmission dynamics of pertussis. Given the definition of cluster cases, the sample size was relatively limited, and while a retrospective investigation was conducted, the specific transmission patterns of each case were not deeply explored. Second, due to the complexity of transmission networks in school settings and the lack of detailed contact tracing data, we were unable to determine the precise infector–infectee pairs for each case. Instead, we adopted a simplified approach by pairing each subsequent case with the index case in the same classroom to estimate the serial interval. This method assumes that all secondary infections originated from the index case, which may not capture multiple generations of transmission or interactions among subsequent cases. As a result, our estimates may be subject to bias, and the true serial interval could be shorter or more variable. Due to the retrospective study design and the data structure characteristics of Zhejiang Province’s pertussis surveillance system, cluster-adjusted analytical methods (e.g., mixed-effects models, generalized estimating equations) were not applied in the current analysis. Future studies should consider using probabilistic transmission models or detailed contact data to improve the accuracy of serial interval estimation in congregate settings.
Conclusion
In the vaccination era, pertussis outbreaks in schools mainly present as small clusters of 5–6 cases, with the Index cases typically being children aged 7–8 y. Effective prevention and control measures in school settings are essential to curb the emergence and transmission of these outbreak clusters.
Supplementary Material
Acknowledgments
We sincerely thank all the staff members from the municipal and prefectural Centers for Disease Control and Prevention in Zhejiang Province for their tremendous support in this study, and we also extend our heartfelt gratitude to all the participants who took part in this research.
Biographies
Yao Zhu is a field epidemiologist at Zhejiang Provincial Center for Disease Control and Prevention, China. Her research focuses on the surveillance and control of vaccine-preventable diseases, with a particular emphasis on pertussis prevention and control. She has led and participated in multiple research projects related to pertussis, including seroepidemiological analysis of pertussis and evaluation of pertussis vaccine effectiveness. Her work provides scientific evidence for optimizing local pertussis prevention and control strategies and improving vaccine immunization programs.
Dr. Hanqing He is a professional researcher focusing on the epidemiological characteristics and transmission dynamics of vaccine-preventable diseases. He has long been engaged in studies on immune barriers and epidemiological patterns in the vaccine era, with research covering integrated applications of epidemiology, disease surveillance, immune assessment, big data analytics and health economics. His work on measles, polio and pertussis has provided evidence for national immunization policy updates, including the 2-dose MMR schedule and the “2+2” polio vaccination strategy, with several findings published in Lancet series journals.
Funding Statement
This work was supported by the Disease Prevention and Control Technology Plan Project of Zhejiang Province [2025JK191] and Chinese Preventive Medicine Association Scientific Research Support Project for Young and Middle-aged Talents in Infectious Disease Prevention and Control [CPMA2024CRBFK].
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of the Zhejiang Center for Disease Control and Prevention (Approval No. 2023-040-01) and was conducted in accordance with the principles of the Declaration of Helsinki.
Abbreviations
- CI
Confidence Interval
- CDC
Centers for Disease Control and Prevention
- PCR
Polymerase Chain Reaction
- IgG
Immunoglobulin G
- PT
Pertussis Toxin
- IQR
Interquartile Range
- SD
Standard Deviation
- WHO
World Health Organization
Supplemental material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/21645515.2026.2654305
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.