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Norovirus is currently considered the leading cause of acute gastroenteritis worldwide (1). Clustering among children in schools can easily lead to norovirus outbreaks (2). Rapid and effective control of such outbreaks in schools remains a significant public health challenge (3). Norovirus spread should be regarded as a diffusion behavior in social networks and as a dynamic, continuously evolving process. Social network analysis, as a highly flexible research method, can recognize the inherent complexity of individuals’ connections. By simulating the transmission dynamics of social networks based on a norovirus outbreak in a primary school, 12 key nodes were identified during the spread and recession periods. This approach demonstrated that targeted control strategies, which transcend inherent epidemiological approaches, are effective and provide insights for emergency management.
Using data from a norovirus outbreak reported in a Zhuhai City primary school (4), the epidemic lifecycle — from emergence to spread and decline — was analyzed. A 63×63 transmission matrix was constructed for all nodes. Network analysis and characteristic parameter calculation were performed using UCINET (version 6.528, Analytic Technologies, USA). NetLogo (version 3D 6.1.1, Northwestern’s Center for Connected Learning and Computer-Based Modeling, USA) was used to model and simulate the effects of interventions during different epidemic periods. Methods for social network analysis and modeling are described in
Supplementary Material .The norovirus outbreak lasted 23 days, with 63 total cases reported across 6 grades and 18 classes. The class attack rate ranged from 1.96% (1/51) to 42.9% (21/49), and the overall attack rate was 4.2% (63/1,500). The outbreak initially affected 21 cases (Cases 1–21) in 3 classes over 2 days. This initial outbreak was primarily concentrated in Grade 3, Class 3, involving 19 cases, including the index case who experienced vomiting in the classroom on October 13, 2020. The incidence curve indicated a point-source exposure pattern. On October 14, classes for Grade 3, Class 3 were suspended. However, the epidemic continued with a spreading period involving 28 cases (Cases 22–49) across 13 classes over 9 days, suggesting a person-to-person transmission model. On-campus housing was suspended until the evening of October 23. On October 24, a recession period began, involving 14 cases (Cases 50–63) in 10 classes. This period lasted 12 days, and the epidemic curve showed a tailing pattern (Figure 1).
Figure 1.The onset date in different classes and hosting locations and periods of development of a norovirus outbreak at a primary school in Zhuhai City, Guangdong Province, China, 2023.
A rapid point-source outbreak occurred in Grade 3, Class 3, with 10 (Cases 2, 5, 7, 11–15, 19, and 20) of the 21 reported cases involved in mixed-class hosting at the school on October 13 and 14, 2020. This activity resulted in the direct infection of 19 students in other classes. The outbreak spread further to the classes of these students and through off-campus hosting centers. Case 23, which was hosted at hosting centers B and C at midday and in the evening, may have caused internal transmission at these two centers. None of the reported cases in Grade 3, Class 3, were cared for at hosting centers A and C. These two centers had longer transmission chains. Additionally, Case 9 in Grade 3, Class 3, and Case 43 in Grade 6, Class 2, were both at hosting center D; however, their 8-day onset time interval suggests latent infections within the network as possible sources or bridges of infection. Notably, Case 61 in Grade 6, Class 3, had no clear social relationships within the network (Figure 2).
Figure 2.The social network topology of 63 cases of a norovirus outbreak at a primary school in Zhuhai City, Guangdong Province, China, 2023.
During the outbreak period, the network was the tightest (density=0.871), with 10 main key nodes (cases 2, 5, 7, 11–15, 19, and 20), which were included in 17 core nodes, as shown by the core-periphery analysis results. The average distance between nodes in the outbreak period was 1.085, meaning any two nodes could communicate through an average of 1.1 intermediate nodes. During the spread period, network tightness decreased (density=0.495), and cases 9, 43, 23, 3, 16, 2/5/7/11–15/19/20 had greater betweenness centralization, meaning they were in a relatively central position in the network. During the recession period, the network was relatively loose (density=0.099). Five core nodes (41, 46–49) were in the core matrix, and cases 48 and 59 were the key nodes as the spread of vomiting according to social network topology. The degree and closeness centralization indicators of the whole network both point to key nodes 2, 5, 7, 11–15, 19, and 20. The average distance was 2.292, and 29 of the 63 cases were in the core matrix (Table 1, Figure 3).
Period Density Degree centralization Betweenness centralization Closeness centralization Average distance Core-periphery Out In Out In Outbreak 0.871 0.083 0.135 0.005 0.502 0.890 1.085 17 core nodes: 2–9, 11–16, 18–20 Rank 1–10: 2, 5, 7, 11, 12, 13, 14, 15, 19, 20 Spread 0.495 0.303 0.324 0.139 0.274 0.579 1.793 27 core nodes: 2, 5, 7, 11–15, 19–21, 24, 25, 28–31, 33–37, 39, 41, 46, 47, 49 Rank 1–10: 2, 5, 7, 11, 12, 13, 14, 15, 19, 20 Rank 1–10: 9, 43, 23, 3, 16, 2/5/7/11–15/19/20 Rank 1–10: 2, 5, 7, 11, 12, 13, 14, 15, 19, 20 Recession 0.099 0.229 0.324 0.119 0.666 0.664 2.292 5 core nodes: 41, 46–49 Rank 1–10: 48, 47, 41, 49, 53, 46, 56, 42, 58, 57 Rank 1–7: 48, 53, 49, 42, 41, 47, 59; Other node: 0 Rank 1–10: 52, 53, 41, 47, 48, 49, 46, 60, 56/57/58 Rank 1–10: 42, 43, 44, 48, 49, 41, 47, 46, 56/57/58 Whole network 0.314 0.304 0.320 0.085 0.125 0.306 2.100 29 core nodes: 2, 5, 7, 11–15, 17, 19–21, 24, 25, 28–31, 33–37, 39, 41, 46–49 Rank 1–10: 2, 5, 7, 11, 12, 13, 14, 15, 19, 20 Rank 1–10: 9, 43, 23, 48, 3, 16, 17, 49, 21, 29/31/30 Rank 1–10: 2, 5, 7, 11, 12, 13, 14, 15, 19, 20 Table 1. The social network characteristic parameters and key nodes of different periods of development of a norovirus outbreak at a primary school in Zhuhai City, Guangdong Province, China, 2023.
Figure 3.The social network matrix of different periods of development of a norovirus outbreak at a primary school in Zhuhai City, Guangdong Province, China, 2023. (A) Outbreak; (B) Spread; (C) Recession; (D) Whole network.
The curve generated with 10 randomly controlled nodes (decay step length=1,083) was similar to the curve generated without controlling any nodes (1,091). Controlling 10 key nodes (2, 5, 7, 11–15, 19, and 20) and eliminating the factors of mixed-class hosting at school significantly reduced the step length of the curve (819). Eliminating the key nodes (48,59) are controlling the spread of vomiting during the recession period further reduced the step length (680) (Figure 4).
Figure 4.The modeling network status of different situations of a norovirus outbreak at a primary school in Zhuhai City, Guangdong Province, China, 2023. (A) Actual situation; (B) Ten random controlled nodes; (C) During the spread period, 10 key nodes (cases 2, 5, 7, 11–15, 19, and 20) were controlled; (D) Control of 10 key nodes and 2 key nodes (cases 48 and 59) in the recession period.
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The network density of the norovirus outbreak gradually decreased from 0.871 to 0.099. This change was likely due to the concentration of cases within a single class during the outbreak period. As the outbreak progressed to the spread period, more classes were involved due to mixed-class activities at school and off-campus hosting. During the recession period, sporadic cases arose, likely due to the untimely isolation of infected individuals and improper handling of vomitus. Previous studies have identified risk factors for norovirus outbreaks in schools, including vomiting on campus and case activity in public areas (5). In this outbreak, mixed-class hosting during the spread period and the failure to standardize the handling of vomitus during the recession period were key factors contributing to the outbreak and the tailing of the epidemic curve. These factors created critical nodes in the transmission network with priority connection characteristics, increasing the likelihood of disease spreading at each stage. Based on theoretical parameters and operational feasibility, we selected 10 key nodes in the transmission network as primary targets for spread control and 2 key nodes for regression control. Modeling simulations demonstrated that implementing corresponding control measures could effectively reduce the extent and spread of the norovirus.
Social network studies have contributed significantly to understanding the occurrence and development of infectious diseases in recent years (6). Studies have shown that immunizing or isolating a small number of nodes can effectively control infectious disease outbreaks (7-8). This study analyzed the social network spread patterns and relationships of 63 cases to verify the effectiveness of this approach in identifying network structures, clarifying core members, and improving decision-making. Using social network analysis, researchers can feasibly identify core nodes and key relationships in infectious disease transmission to accurately and quickly interrupt transmission pathways and prevent large-scale spread or new outbreaks by bridging crowds.
Case 61, who lacked clearly defined social relationships, appeared peripherally in the network. Additionally, despite exhibiting social relationships, some nodes within the network had symptom onset intervals exceeding the incubation period. Notably, studies have reported that latent norovirus infections can account for up to 17.6% of outbreak cases (9). Therefore, latent infection within the network may represent a potential source of transmission.
This survey did not include collecting and testing of samples from healthy students or school staff for norovirus nucleic acid, precluding assessment of the proportion of latent infections. Factors such as inadequate vomit disposal practices during the initial stages, non-standard disinfection protocols, and cross-contamination from shared mop use between classes may confound the assessment of network relationships.
CDC physicians might consider incorporating social network analysis when managing norovirus outbreaks in schools. Understanding transmission patterns to identify and control key nodes could help to terminate outbreaks expeditiously.
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