Descriptive epidemiology of school outbreaks of seasonal influenza B during 2012/2013 in the Thames Valley, United Kingdom

McCann, Lucy; Suchánek, Ondřej; McCarthy, Noel D.; Mannes, Trish

doi:10.1016/j.puhe.2014.08.016

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Therefore, the total loss of productivity per child with symptoms consistent with ILI was approximately 7.5 days off both work and school. McCann et al investigated 3 outbreaks of laboratory‐confirmed influenza B in schools in the Thames Valley during the 2012‐2013 influenza season and reported that the mean length of absence from each school was 3.5, 2.6, and 2.8 days, respectively, similar to our findings.…”

Section: Discussionsupporting

confidence: 88%

“…Our estimate of the total pediatric burden of disease for the 2 influenza seasons of 105.3 QALYs (95% CI: 77.7‐139.0) only includes those primary schools that reported suspected ILI outbreaks and not all school‐based ILI infections during the 2 influenza seasons. We assumed a conservative overall attack rate of 39.1% from our sample in comparison to a mean attack rate of 53.0% from McCann et al Also, we only considered the impact of infection associated with school transmission by excluding the potential secondary transmission of ILI to household members.…”

Section: Discussionmentioning

confidence: 99%

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Social and Economic Impacts of School Influenza Outbreaks in England: Survey of Caregivers

Thorrington

Balasegaram

Cleary

et al. 2017

Journal of School Health

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Social and Economic Impacts of School Influenza Outbreaks in England: Survey of Caregivers

Thorrington

Balasegaram

Cleary

et al. 2017

Journal of School Health

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“…Influenza B virus was believed to have a slower rate of antigenic evolution than influenza A,6 and to cause milder illness than influenza A in the past 7. Like influenza A, influenza B has caused localised outbreaks, including in mass gatherings 2, 8, 9, 10, 11. Moreover, influenza B infection may be underidentified compared with influenza A in healthcare settings,12 so the true burden of influenza B may be underestimated.…”

Section: Introductionmentioning

confidence: 99%

Epidemiology of influenza B in Australia: 2001‐2014 influenza seasons

Moa

Muscatello

Turner

et al. 2016

Influenza Resp Viruses

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BackgroundInfluenza B is characterised by two antigenic lineages: B/Victoria and B/Yamagata. These lineages circulate together with influenza A during influenza seasons, with varying incidence from year to year and by geographic region.ObjectiveTo determine the epidemiology of influenza B relative to influenza A in Australia.MethodsLaboratory‐confirmed influenza notifications between 2001 and 2014 in Australia were obtained from the Australian National Notifiable Diseases Surveillance System.ResultsA total of 278 485 laboratory‐confirmed influenza cases were notified during the study period, comprising influenza A (82.2%), B (17.1%) and ‘other and untyped’ (0.7%). The proportion of notifications that were influenza B was highest in five‐ to nine‐year‐olds (27.5%) and lowest in persons aged 85 years and over (11.5%). Of all B notifications with lineage determined, 77.1% were B/Victoria and 22.9% were B/Yamagata infections. Mismatches between the dominant B lineage in a season and the trivalent vaccine B lineage occurred in over one‐third of seasons during the study years. In general, influenza B notifications peaked later than influenza A notifications.ConclusionThe proportion of circulating influenza B in Australia during 2001‐2014 was slightly lower than the global average and was dominated by B/Victoria. Compared with influenza A, influenza B infection was more common among older children and young adults and less common in the very elderly. Influenza B lineage mismatch with the trivalent vaccine occurred about one‐third of the time.

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“…Further details on the model, including the equations and the parameter values are provided in Appendix A. The model was validated in several well-monitored influenza outbreaks in schools -an outbreak of 2009 pandemic influenza in a UK boarding school [43] and a US elementary school [44] , as well as a seasonal influenza outbreak in a UK school in 2015 [45] .…”

Section: Methodsmentioning

confidence: 99%

“…The model was validated on outbreaks of influenza and COVID-19 in schools and shown to match the peak and duration of the outbreak curves, and the overall attack rates of the student population. The validation data was from two outbreaks of pandemic influenza 39,40 , one outbreak of seasonal influenza 41 , and one outbreak of COVID-19 42 . Model parameter ranges were derived from published sources and by calibration to data using a stochastic optimization algorithm.…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of Isolation Policies in Schools: Evidence from a Mathematical Model of Influenza and COVID-19

Burns

Gutfraind

2020

Preprint

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Background: Controlling the transmission of respiratory infections such as influenza andCOVID-19 is a critical public health priority. Non-pharmaceutical intervention policies such as community quarantines, closures and travel bans are often implemented in emergencies but many of them are disruptive and difficult to maintain for extended periods of time. A promising alternative recommended by the CDC for influenza is requiring individuals showing fever symptoms to remain isolated at home until they are fever-free for at least one day, but there is limited evidence to support the effectiveness of such symptom-based isolation policies. Methods:Here we introduce a computational model of symptom-based isolation that accounts for the timing of symptoms, viral shedding and the population structure. It was validated on outbreaks of influenza in schools and modified to account for COVID-19. It was then used to estimate the outbreak curves and the attack rates (the proportion of the population infected) under one or more days of fever-based isolation.Results: Using the model we find evidence that symptom-based isolation policies could reduce the attack rates of both influenza and COVID-19 outbreaks, and flatten the outbreak curves.Specifically, we found that across a range of influenza scenarios, a CDC-recommended policy of one day isolation following fever can reduce the attack rate from 27% of the population to 12%, : medRxiv preprint implementing one day post-fever isolation would reduce the attack rate from 79% to 71%, and there is possible benefit from isolation for six days. In both influenza and COVID-19, the policies are predicted to reduce the peak number of infected but not shorten the outbreak duration.Conclusions : Symptom-based isolation could be an important tool to control influenza and COVID-19 outbreaks in schools, and potentially other settings. We recommend that schools implement a post-fever isolation policy of two days for influenza and six days for COVID-19.

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Descriptive epidemiology of school outbreaks of seasonal influenza B during 2012/2013 in the Thames Valley, United Kingdom

Cited by 4 publications

References 9 publications

Social and Economic Impacts of School Influenza Outbreaks in England: Survey of Caregivers

Social and Economic Impacts of School Influenza Outbreaks in England: Survey of Caregivers

Epidemiology of influenza B in Australia: 2001‐2014 influenza seasons

Effectiveness of Isolation Policies in Schools: Evidence from a Mathematical Model of Influenza and COVID-19

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