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      Calendar effects to forecast influenza seasonality: A case study in Milwaukee, WI

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          Abstract

          Objective In the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance. Introduction Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure [1,2]. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality [3]. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections [4]. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread [5] demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete. Methods The City of Milwaukee Health Department Laboratory (MHDL), Wisconsin routinely collect tests from residents of metropolitan areas and vicinities of the Marquette University (MU). We obtained weekly counts of total tested, all influenza positives, positives for influenza A and B, from MHDL between 5/16/04- 3/7/09 (before the surge of tests associated with “swine flu”). Cases for <1 and 1-4 age groups were combined. Meteorological data are routinely collected by a monitoring station at the General Mitchell International airport located 7.5 miles from Milwaukee. Daily dewpoint values representing the perceived ambient temperature corrected for the air moisture content were downloaded from the open source website [6] and aggregated to weekly averages with Sunday designating the beginning of each week. School holidays were obtained from academic calendars on the MU website with holiday weeks defined as having one or more school holiday observed [7]. Selected social events were retrieved from a public website [8]. As part of exploratory analysis, average cases per week (c/w) for each outcome for school holiday and non-holiday weeks were compared using a non-parametric the Mann–Whitney U-test. We analyzed the association between weekly cases and holiday effects using negative binomial regression with sets of indicator variables for non-overlapping school holidays and social events and with adjustments for weather fluctuations with harmonic terms (Model 1). Results are presented as Relative Risk (RR) estimates along with their confidence intervals (95%CI). Further analyses examined seasonal signatures (lead-lag structures) using a segmented regression approach for weekly counts and rates 5 academic weeks (aw) before, 2-6 weeks during, and 5 weeks after select holidays (Model 2). Results Over 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from 5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza A (73 tests were not subtyped) and 78 (16%) cases of influenza B. Figure 1 shows the time series of weekly counts of influenza tests and percent positives with superimposed information on school holiday occurrences. Overall, during 135 weeks of the school period the average number of tests was two times higher as compared to those during 116 holiday weeks (11.9±10.3 vs 5.8±6.5 c/w, p<0.001). Similarly, the average weekly number of positive tests was higher in non-holiday than during holiday periods (2.9±5.7 vs 0.7±2.6 c/w, p<0.001). The reduction in tests during holidays was confirmed by the regression model (RR=0.71; 95%CI=[0.60-0.86]). The reduction in weekly tests was most pronounced during the Winter Break (15-19 aw) for all age groups (4.8±3.0 c/w, p<0.001; RR=0.3; 95%CI=[0.23-0.41]) and especially for school-aged children, young adults and adults (RR=0.14; 95%CI=[0.09-0.22] and RR=0.32; 95%CI=[0.16-0.62] for 5-24 and 25-44 age groups, respectively). In contrast, during the Spring Break (27-30 aw) the number of tests has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school period, with the most noticeable increase in 5-24 and 25-44 age groups. Spring Break differential effects were primarily due to later peaks in influenza B shown by segmented regression results in Figure 2. The seasonal increase in weekly rates is the steepest after the winter holidays. The effects of the selected sporting and social events were inconclusive. Figure 1 Weekly number tests and % of influenza positives tests and percent positives with superimposed school holiday occurrences in Milwaukee, WI (2004-2009) Figure 2 Weekly number of tests, positive tests, influenza A and B (panels A-D, respectively) for each study year with superimposed Winter and Spring school holiday occurrences in Milwaukee, WI. Average and standard deviation values for four outcomes and their weekly rates (in italic) for five time periods: before (5 weeks), during (2-6 weeks), and after (5 weeks) the Winter and Spring Breaks (time is shown in weeks starting September 1st); superscripts b and c indicate t-test significance for during-after and before-after comparisons, respectively. Conclusions The differential effects of calendar events on seasonal influenza can be detected by routine surveillance and further explored with respect to lead-lag structures. We recommend incorporating location-specific calendar effects in influenza near-term forecasting models tailored to susceptible age groups to better predict and assess targeted intervention measures.

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          The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

          Background School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified. Methods We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms. Results Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic. Conclusion Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2934-3) contains supplementary material, which is available to authorized users.
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            The Relationship Between School Holidays and Transmission of Influenza in England and Wales

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              Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015

              Objective To assess the effects of winter/summer school breaks on occurrences of influenza-like illness (ILI). Methods We jointly analysed ILI surveillance data with the timing of school breaks in a temperate district in Beijing, China from 2008 to 2015. ILI incidence rate ratios (IRRs) of schoolchildren (5–14 and 15–24 years of age) to adults (25–59 and >60 years of age) were used to measure the age shift of ILI incidence before, during and after the 4-week winter/7-week summer breaks. Serfling-based Poisson regression model with adjustment for unmeasured confounders was built to further assess the effect of winter school breaks. Results ILI incidences were consistently lower during winter breaks than before winter breaks for all age groups. IRRs of younger schoolchildren aged 5–14 to adults were higher during winter school breaks than before breaks, while the opposite was true for the IRRs of older schoolchildren aged 15–24 to adults. Schoolchildren-to-adults IRRs during summer breaks were significantly lower than before or after school breaks (p<0.001). Conclusions Both winter and summer breaks were associated with reductions of ILI incidences among schoolchildren and adults. Our study contributes additional evidence on the effects of school breaks on ILI incidence, suggesting school closure could be effective in controlling influenza transmission in developing countries.
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                Author and article information

                Journal
                Online J Public Health Inform
                Online J Public Health Inform
                OJPHI
                Online Journal of Public Health Informatics
                University of Illinois at Chicago Library
                1947-2579
                30 May 2019
                2019
                : 11
                : 1
                Affiliations
                [1 ]Friedman School of Nutrition Science and Policy, Tufts University, Malden, Massachusetts, United States
                [2 ]Blood Research Center, Milwaukee, Wisconsin, United States
                [3 ]MHDL,Milwaukee, Wisconsin, United States
                Article
                ojphi-11-e231
                10.5210/ojphi.v11i1.9739
                6606152
                ISDS Annual Conference Proceedings 2019

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.

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