Transmission characteristics of different students during a school outbreak of (H1N1) pdm09 influenza in China, 2009

Analysis of factors influencing influenza outbreaks in schools in Taicang City, China

Objective

This study aims to analyze the awareness of influenza prevention and control and the behavioral attitudes toward the work among parents and staff in schools in Taicang City and the impact of the vaccination rate among students on influenza outbreaks in schools. The findings can provide references for the development of effective control strategies for the spread of influenza.

Methods

An anonymous questionnaire survey was conducted on 10,962 students from 20 schools in Taicang City, with class as the unit of analysis. The survey investigated their awareness of influenza prevention and control, their attitudes, and the vaccination coverage.

Results

From January to June 2023, a total of 388 influenza outbreaks were reported in schools in Taicang City, involving 77 schools. There were 3,475 confirmed cases, with an average infection rate of 18.53%. In schools where influenza outbreaks had occurred, the incidence rate of those who received influenza vaccine was significantly lower than those who did not, and the vaccine protection rate was 28.22%. The knowledge awareness rates of "the main transmission routes of influenza" and "influenza vaccination can prevent influenza" among parents of students were 95.49 and 93.16%, respectively. The differences between schools involved in the epidemic and non-epidemic were statistically significant (p < 0.05). The correct attitudes of parents toward "actively reporting relevant symptoms to teachers when their children show symptoms" and "avoiding classes with diseases when their children are suspected to be sick" are 98.80 and 96.26%, respectively. The differences between schools with and without epidemic are statistically significant (p < 0.05). The correct attitudes of the class teacher toward "correct management and control of students with flu like symptoms in the class" and "taking correct prevention and control measures in the event of a flu epidemic in the class" were 89.36 and 92.55%, respectively. The differences between epidemic related and non-epidemic related classes were statistically significant (p < 0.05).

Conclusion

Enhance the knowledge level of influenza prevention and control among parents of students, Strengthening the training for class teachers in emergency response to infectious diseases and increasing vaccination coverage among students can effectively reduce the incidence of influenza and thereby the occurrence of cluster outbreaks in schools.

Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections

Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.

Increasing Seasonal Influenza Vaccination among University Students: A Systematic Review of Programs Using a Social Marketing Perspective

The health and economic consequences of seasonal influenza present great costs to communities. Promoting voluntary uptake of the seasonal influenza vaccine among university students, particularly during the COVID-19 pandemic, can deliver protective effects for both individuals and the wider community. Vaccine uptake will be greatest when more of the social marketing benchmarks are applied. This systematic review summarizes evidence from programs aiming to increase seasonal influenza vaccination among university students. Six major electronic databases for health promotion studies (PubMed, EBSCO, ProQuest, Ovid, Web of Science, and ScienceDirect) were searched in November 2021 to capture peer-reviewed studies reporting field trials that have sought to increase seasonal influenza vaccination in university student populations, without any restrictions regarding the publication period. Following PRISMA guidelines, this paper identified 12 peer-reviewed studies that were conducted in the field in the United States, Australia, and Spain. Three studies were targeted at healthcare students and the rest focused on wider university student populations. Studies were narratively summarized, evidence of social marketing principles were identified, and quantitative outcomes were meta-analyzed. The findings indicate that none of the field studies, even a self-classified social marketing study, had adopted all eight of the social marketing benchmarks in program design and implementation. The two studies that only used promotion, but not other marketing-mix and social marketing principles, reported increases in students’ intention to be vaccinated but not actual behavior. Given that change is more likely when more social benchmarks are applied, this paper identifies activities that can be included in flu vaccine programs to improve flu vaccine uptake rates. The analysis highlights a lack of field studies focusing on increasing rates of vaccination behavior as research outcomes in countries beyond the United States.

Motivating Seasonal Influenza Vaccination and Cross-Promoting COVID-19 Vaccination: An Audience Segmentation Study among University Students

In the response to the coronavirus pandemic, much attention has been invested in promoting COVID-19 vaccination. However, the impact of seasonal influenza should not be neglected, particularly during the winter influenza surge. Currently, most influenza vaccination campaigns target at healthcare workers or high-risk population groups, while COVID-19 vaccination programmes are targeting the whole population as a single homogeneous group. There is limited research on the promotion of influenza vaccination for university students who study, live, and socialise in close contact with a large variety of people on campus, resulting in a low vaccination rate among this underserved group. Thus, a vaccination programme tailored for university students should be developed to increase protection against influenza-like illnesses and complications, and to help achieve herd immunity across populations who spread viruses. WHO has advocated the potential value of social marketing in vaccination campaigns and highlighted the need for audience segmentation as a major prerequisite component of intervention design. This study aims to identify distinct and homogeneous groups of university students based on sociodemographic, psychographic, and behavioural attributes to inform interventions. Two-step cluster analysis was applied in a sample size of 530 university students and revealed four segments that demonstrate statistically significant differences in their attitudes, behaviours, intentions, and responses to promotion messages about seasonal influenza and COVID-19 vaccination. The findings provide a detailed understanding of segment characteristics among university students that can be applied to develop an effective social marketing campaign that can motivate influenza vaccination and cross-promote uptake of the COVID-19 vaccine.

Within and between classroom transmission patterns of seasonal influenza among primary school students in Matsumoto city, Japan

Schools play a central role in the transmission of many respiratory infections. Heterogeneous social contact patterns associated with the social structures of schools (i.e., classes/grades) are likely to influence the within-school transmission dynamics, but data-driven evidence on fine-scale transmission patterns between students has been limited. Using a mathematical model, we analyzed a large-scale dataset of seasonal influenza outbreaks in Matsumoto city, Japan, to infer social interactions within and between classes/grades from observed transmission patterns. While the relative contribution of within-class and within-grade transmissions to the reproduction number varied with the number of classes per grade, the overall within-school reproduction number, which determines the initial growth of cases and the risk of sustained transmission, was only minimally associated with class sizes and the number of classes per grade. This finding suggests that interventions that change the size and number of classes, e.g., splitting classes and staggered attendance, may have a limited effect on the control of school outbreaks. We also found that vaccination and mask-wearing of students were associated with reduced susceptibility (vaccination and mask-wearing) and infectiousness (mask-wearing), and hand washing was associated with increased susceptibility. Our results show how analysis of fine-grained transmission patterns between students can improve understanding of within-school disease dynamics and provide insights into the relative impact of different approaches to outbreak control.

[Epidemiological characteristics of infectious diseases of group A, B and C among Chinese students' population]

OBJECTIVE

To analyze the secular trend, ranking change, age- and regional- characteristics of leading infectious diseases among Chinese students population aged 6 to 22 years from 2008 to 2017.

METHODS

Data were drawn from the national surveillance from 2008 to 2017, and the participants were students aged from 6 to 22 years who were diagnosed with notifiable infectious diseases. A total of 40 infectious diseases were classified into three groups based on national notifiable infectious diseases classification of A, B and C. The morbidity and mortality rates from infectious diseases were calculated using the numbers of students published by the ministry of education as the denominator. The age- and province-specific infectious diseases with the highest incidence were selected as the leading infectious diseases for analysis.

RESULTS

From 2008 to 2017, the incidence rate, the number of cases and the number of deaths of infectious diseases among the boys aged 6-22 years in China were higher than that of the girls, and the overall trend was downward during the study period. The incidence rates in the boys and girls decreased from 2008 to 2015 with decrease of 43.4% and 40.1%, respectively. However, by 2017, the increase rate rebounded with the increases of 47.1% and 53.8%. The rebound trend was mainly caused by the increase of group C of infectious diseases. During the past decade, the top leading three diseases of groups A and B of infectious diseases were viral hepatitis, tuberculosis and dysentery in 2018, respectively, which changed to tuberculosis, scarlet fever and viral hepatitis in 2017. The top leading three infectious diseases in terms of mortality were rabies, tuberculosis, and human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) in 2008, which were transformed into HIV/AIDS, rabies, and tuberculosis in 2017. There was no significantly obvious change in the incidence and mortality order of group C of infectious diseases during the decade. In the analysis of age groups and regions, the leading infectious diseases in groups A and B transferred from viral hepatitis to scarlet fever and tuberculosis, while in group C, mumps and infectious diarrhoea almost always dominated the leading infectious diseases. But in recent years, influenza and hand-foot-and-mouth disease increased significantly in the eastern region.

CONCLUSION

During the past decade, China has got remarkable achievements in the prevention and control of children infectious disease. However, the change patterns and characteristics of notifiable infectious diseases among children and adolescents show the urgent need for prevention and control of respiratory tract infectious diseases, HIV/AIDS and other sexually transmitted diseases as well as new emerging infectious diseases in China in the future. This study provides important basis for policy making of Chinese national school-based infectious disease prevention and control mechanism.

[Epidemiological characteristics of infectious diseases of group A, B and C among Chinese students' population]

OBJECTIVE

To analyze the secular trend, ranking change, age- and regional- characteristics of leading infectious diseases among Chinese students population aged 6 to 22 years from 2008 to 2017.

METHODS

Data were drawn from the national surveillance from 2008 to 2017, and the participants were students aged from 6 to 22 years who were diagnosed with notifiable infectious diseases. A total of 40 infectious diseases were classified into three groups based on national notifiable infectious diseases classification of A, B and C. The morbidity and mortality rates from infectious diseases were calculated using the numbers of students published by the ministry of education as the denominator. The age- and province-specific infectious diseases with the highest incidence were selected as the leading infectious diseases for analysis.

RESULTS

From 2008 to 2017, the incidence rate, the number of cases and the number of deaths of infectious diseases among the boys aged 6-22 years in China were higher than that of the girls, and the overall trend was downward during the study period. The incidence rates in the boys and girls decreased from 2008 to 2015 with decrease of 43.4% and 40.1%, respectively. However, by 2017, the increase rate rebounded with the increases of 47.1% and 53.8%. The rebound trend was mainly caused by the increase of group C of infectious diseases. During the past decade, the top leading three diseases of groups A and B of infectious diseases were viral hepatitis, tuberculosis and dysentery in 2018, respectively, which changed to tuberculosis, scarlet fever and viral hepatitis in 2017. The top leading three infectious diseases in terms of mortality were rabies, tuberculosis, and human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) in 2008, which were transformed into HIV/AIDS, rabies, and tuberculosis in 2017. There was no significantly obvious change in the incidence and mortality order of group C of infectious diseases during the decade. In the analysis of age groups and regions, the leading infectious diseases in groups A and B transferred from viral hepatitis to scarlet fever and tuberculosis, while in group C, mumps and infectious diarrhoea almost always dominated the leading infectious diseases. But in recent years, influenza and hand-foot-and-mouth disease increased significantly in the eastern region.

CONCLUSION

During the past decade, China has got remarkable achievements in the prevention and control of children infectious disease. However, the change patterns and characteristics of notifiable infectious diseases among children and adolescents show the urgent need for prevention and control of respiratory tract infectious diseases, HIV/AIDS and other sexually transmitted diseases as well as new emerging infectious diseases in China in the future. This study provides important basis for policy making of Chinese national school-based infectious disease prevention and control mechanism.

Coupling dynamics of epidemic spreading and information diffusion on complex networks

The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

Estimation of the Basic Reproduction Number of Novel Influenza A (H1N1) pdm09 in Elementary Schools Using the SIR Model

Background:

The novel influenza A (H1N1) pdm09 (A/H1N1pdm) pandemic of 2009-2010 had a great impact on society.

Objective:

We analyzed data from the absentee survey, conducted in elementary schools of Oita City, to evaluate the A/H1N1pdm pandemic and to estimate the basic reproductive number (R₀ ) of this novel strain.

Method:

We summarized the overall absentee data and calculated the cumulative infection rate. Then, we classified the data into 3 groups according to school size: small (<300 students), medium (300–600 students), and large (>600 students). Last, we estimated the R₀ value by using the Susceptible-Infected-Recovered (SIR) mathematical model.

Results:

Data from 60 schools and 27,403 students were analyzed. The overall cumulative infection rate was 44.4%. There were no significant differences among the grades, but the cumulative infection rate increased as the school size increased, being 37.7%, 44.4%, and 46.6% in the small, medium, and large school groups, respectively. The optimal R₀ value was 1.33, comparable with that previously reported. The data from the absentee survey were reliable, with no missing values. Hence, the R₀ derived from the SIR model closely reflected the observed R₀ . The findings support previous reports that school children are most susceptible to A/H1N1pdm virus infection and suggest that the scale of an outbreak is associated with the size of the school.

Conclusion:

Our results provide further information about the A/H1N1pdm pandemic. We propose that an absentee survey should be implemented in the early stages of an epidemic, to prevent a pandemic.

Evaluating the effects of common control measures for influenza A (H1N1) outbreak at school in China: A modeling study

BACKGROUND

Influenza A (H1N1) outbreaks have become common at schools in China since 2009. However, the effects of common countermeasures for school influenza outbreak have not been quantified so far, including isolation, vaccination, antivirus and school closure. We conducted a mathematically modeling study to address this unsolved issue.

METHODS

We collected data of all small-scale school outbreaks caused by influenza A that occurred in Changsha city between January 2009 and December 2013. Two outbreaks (one was in 2009 and the other one was in 2013) were used for simulating the effects of single and combined use of common measures, including isolation (Iso), therapeutics (T), prophylactics (P), vaccinating 70% of susceptible individuals prior to the outbreak (VP70), vaccinating 70% of susceptible individuals every day during the outbreak (VD70) and school closure of one week (S1w). A susceptible-exposed-infectious/asymptomatic-recovered (SEIR) model was developed to implement the simulations based on the natural history of influenza A.

RESULTS

When no control measures are taken, the influenza is expected to spread quickly at school for the selected outbreak in 2013; the outbreak would last 56 days, and the total attack rate (TAR) would reach up to 46.32% (95% CI: 46.12-46.52). Of all single control measures, VP70 is most effective to control the epidemic (TAR = 8.68%), followed by VP50, VD70, VD50 and Iso. The use of VP70 with any other measure can reduce TAR to 3.37-14.04% and showed better effects than any other combination of two kinds of measures. The best two-measure combination is 'S1w+VP70' (TAR = 3.37%, DO = 41 days). All combinations of three kinds of measures were not satisfactory when Vp70 and VD70 were excluded. The most effective three-intervention combination was 'Iso+S1w+VP70' (with TAR = 3.23%). When VP70 or VD70 is included, the combinations of four or five kinds of interventions are very effective, reducing TAR to lower than 5%. But the TAR of combination of 'T+P+Iso+S1w' is 23.20%. Similar simulation results were observed for the selected outbreak in 2009.

CONCLUSION

Vaccinating no less than 70% of individuals prior to the outbreak and isolation are recommended as single measures to control H1N1outbreak at school. The combination of VP70+S1w can achieve very good control for school outbreak.

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017

When a novel influenza A virus with pandemic potential emerges, nonpharmaceutical interventions (NPIs) often are the most readily available interventions to help slow transmission of the virus in communities, which is especially important before a pandemic vaccine becomes widely available. NPIs, also known as community mitigation measures, are actions that persons and communities can take to help slow the spread of respiratory virus infections, including seasonal and pandemic influenza viruses.These guidelines replace the 2007 Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States - Early, Targeted, Layered Use of Nonpharmaceutical Interventions (https://stacks.cdc.gov/view/cdc/11425). Several elements remain unchanged from the 2007 guidance, which described recommended NPIs and the supporting rationale and key concepts for the use of these interventions during influenza pandemics. NPIs can be phased in, or layered, on the basis of pandemic severity and local transmission patterns over time. Categories of NPIs include personal protective measures for everyday use (e.g., voluntary home isolation of ill persons, respiratory etiquette, and hand hygiene); personal protective measures reserved for influenza pandemics (e.g., voluntary home quarantine of exposed household members and use of face masks in community settings when ill); community measures aimed at increasing social distancing (e.g., school closures and dismissals, social distancing in workplaces, and postponing or cancelling mass gatherings); and environmental measures (e.g., routine cleaning of frequently touched surfaces).Several new elements have been incorporated into the 2017 guidelines. First, to support updated recommendations on the use of NPIs, the latest scientific evidence available since the influenza A (H1N1)pdm09 pandemic has been added. Second, a summary of lessons learned from the 2009 H1N1 pandemic response is presented to underscore the importance of broad and flexible prepandemic planning. Third, a new section on community engagement has been included to highlight that the timely and effective use of NPIs depends on community acceptance and active participation. Fourth, to provide new or updated pandemic assessment and planning tools, the novel influenza virus pandemic intervals tool, the Influenza Risk Assessment Tool, the Pandemic Severity Assessment Framework, and a set of prepandemic planning scenarios are described. Finally, to facilitate implementation of the updated guidelines and to assist states and localities with prepandemic planning and decision-making, this report links to six supplemental prepandemic NPI planning guides for different community settings that are available online (https://www.cdc.gov/nonpharmaceutical-interventions).

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.