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Conceived and designed the experiments: LLY. Performed the experiments: LLY. Analyzed the data: LLY J-MC C-YH. Contributed reagents/materials/analysis tools: LLY. Wrote the paper: LLY. Other: Initiated the investigations: LLY. Proposed preliminary study design: LLY. Wrote protocol: LLY. Obtained funding: LLY. Recruited subjects: LLY. Organized the team: LLY. Coordinated study execution: LLY. Contributed to the study design on dosage regimen: LLY J-YL K-SL. Screened pharmaceutical companies and determined the formulation: LLY C-TC. Initiated and developed electronic data capture system: LLY. Collected, managed, and analyzed data: LLY. Had the idea of sealing capsules to mask the aroma: J-YL. Performed Ultrasound examination: J-YL Y-SL. Performed blood draw: J-YL Y-SL. Determined the classification of dysmenorrhoea: J-YL. Prescribed and handed out ibuprofen: J-YL Y-SL. Attended adverse reactions: J-YL K-SL. Revised online diary: J-YL K-SL Y-SL T-FT. Designed repeated measurement analyses: K-YL J-MC. Performed repeated measurement analyses: J-MC. Wrote statistical report: J-MC. Prescribed and handed out the study products: L-HW T-FT. Wrote the report: LLY.

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In addition, we obtained from the National Public Health Laboratory of Singapore age-stratified weekly data on the number of samples submitted and the number testing positive by influenza PCR (Lab-ILI); these were from patients presenting with a similar case definition of ILI to sentinel primary care clinics submitted as part of the routine national influenza surveillance program.

Data derived from clinic-based indicators of epidemic activity from epidemiological week 25 to 40 (21 June 2009 to 10 October 2009). A) GP-ILI data, expressed as ILI consults per GP per week. B) Lab-ILI data, expressed as weekly proportion of ILI samples positive for pdmH1N1. C) Composite indicator of GP*Lab-ILI (by epidemiological week) which gives the estimated ILI consults per GP per week attributable to pdmH1N1 influenza.

Comparison of school-based indicators of epidemic activity with GP-ILI activity expressed as consults per GP per week. A and B) Sch-LCC: Notifications of laboratory confirmed pdmH1N1; C and D) Sch-DTM: Daily temperature monitoring system; E and F) Sch-FRI: School-based FRI reporting, in primary and secondary schools respectively. Red and purple lines denote data from school-based indicators, while blue and green lines give the GP-ILI activity for primary and secondary schools respectively.

Distribution of Sch-FRI episodes by schools and classrooms from weeks 26 to 34. A) School level rates of FRI episodes per 100 children; error bars denote 95% confidence intervals from a Poisson distribution. B) Distribution of classroom level rates of FRI episodes per 100 children for 124 primary and 157 secondary school classrooms, in light and dark grey respectively. Dashed line gives the expected distribution based on the combined average of 9.2 FRI episodes per classroom.

For the same reasons, we found when comparing incidence rates between the systems that laboratory confirmed pdmH1N1 cases (Sch-LCC) likely detected less than 1% of all estimated infections. Since our clinic-based ILI data started only from week 25, we are unable to estimate the fraction of infections detected by laboratory confirmed cases notified earlier during the epidemic, which may have been substantially higher. However, we note that studies from other developed countries which attempt to estimate infections either by symptoms or serology likewise suggest that only a small proportion of infections are confirmed[25-28]. Based on the comparison with Sch-FRI, temperature monitoring twice a day (Sch-DTM) may also only have identified less than one fifth of febrile respiratory illness episodes, and by extrapolation a smaller fraction of infections. Many influenza infections never result in fever[29,30], and those who do become febrile may not have a fever at the time of monitoring, may refrain from attending school in the first place, may take antipyretics, or may have an elevated temperature that nevertheless falls below the defined threshold; any of these circumstances would result in cases not being identified by the monitoring system. On the other hand, our novel teacher led febrile respiratory illness reporting system (Sch-FRI) covering six schools distributed across the country obtained incidence rates consistent with those observed in some school-based outbreaks where syndromic case definitions of self-reported fever and respiratory symptoms were also used[31-33]. While data specific to pediatric populations is lacking, other studies show that symptoms occur in two thirds to three quarters, and febrile illness in about half of serologically detected infections[18,29,34]. Since our ratio of illness episodes (Sch-FRI-adj) to infections was around 0.6, we suggest that self-reported FRI had detected a substantial proportion of symptomatic infections, and hence may be sufficiently sensitive as a means of detecting clusters of transmission in contrast to the other two indicators (Sch-LCC and Sch-DTM) evaluated, which may be limited in their sensitivity for triggering investigations and interventions.

A surveillance system built upon a small group of schools, as in the Sch-FRI system described here, would not allow central educational authorities to instigate responsive school closures in schools which are not enrolled in the network. However, self-reported ILI has been used successfully to investigate school-based outbreaks[9,33]; others have also used self-reported ILI to assess the burden of pdmH1N1 in the community and the proportions which seek care[24,35]. We believe that the school-based FRI reporting we describe offers some advantages over clinic-based ILI reporting: (i) it can be rapidly implemented in a centralized educational system, as in Singapore, (ii) it is not dependent on health-seeking behavior and can potentially work in areas with poor primary care coverage, (iii) it has clear denominators of the population at risk, and (iv) it does not require additional laboratory testing or serological studies. On the other hand, such systems face several challenges, including how to monitor epidemics during school holidays, the representativeness of participating schools (particularly in rural areas where transmission may be less uniform than in highly urbanized Singapore), mitigating the burden of data collection, and integrating such surveillance with data on adults and pre-school children. However, in spite of these limitations, such a system can be a useful adjunct to other more established clinic-based systems, since it is not dependent on primary care coverage or health-seeking behavior, and allows estimates of infection rates following adequate adjustment for the contribution of other causes of febrile respiratory illness and the proportion of infections that do not present with fever. Our analysis of the variation in FRI rates also suggests that FRI reporting has some potential for identifying localized transmission. We noted a wide difference in FRI rates by classrooms, with more than 10-fold difference in rates between the 5th and 95th percentile (5 vs 58 episodes per 100 children). In spite of this, FRI rates aggregated at the level of schools were relatively similar. We suggest that this apparent disconnect could be explained if we consider influenza incidence at school level as an aggregate of semi-independent self-sustaining clusters of transmission at the class-room level, which produces a wide range of cluster sizes distributed around an inherent mean. When the schools are a sufficiently large collection of classrooms (as was the case in our study, where the 6 schools had between 33 to 63 classrooms, with a median class size of 31 students and inter-quartile range from 29 to 39), then the school level FRI rate reflects the average size of a transmission cluster in the classroom setting. Cauchemez et al. have demonstrated that, within the school environment, classroom level transmission dominates[4], and our study adds to the emerging evidence that this is indeed the case. Notably, in the Singapore school system, students mostly interact within the same class, with most classes conducted within the same room throughout the day for lessons, and this may have accentuated the effect. Additional studies will be needed to clarify the pattern of influenza transmission within schools, as this will have substantial implications on control measures, since execution of closures and interventions at classroom level, if effective, would be far less disruptive than equivalent measures at the level of entire schools or even all schools within geographic areas. However, if there is intent to intervene using such data, then febrile respiratory illness may have to be monitored in real-time and followed-up by confirmatory testing of students identified, which may be logistically challenging since some students would be absent from school at the time of their illness; this may also be expensive if implemented at a national level. There may also be issues with variations in data quality if deployed on a wider scale or for longer periods, and as such FRI reporting may function best either when used for short periods such as for detecting transmission clusters during severe epidemics, or in sentinel schools with dedicated support staff to ensure that it is properly collected when used for long-term surveillance of influenza activity. Finally, modeling studies should be attempted to suggest appropriate triggers for interventions (such as a certain number of FRI episodes within a particular time frame), and the potential effect of any interventions on reducing influenza transmission. 041b061a72