Getting Schooled: School Closure, Age Distribution, and Pandemic Mitigation
Bibliographic record
Abstract
Editorials7 February 2012Getting Schooled: School Closure, Age Distribution, and Pandemic MitigationDavid N. Fisman, MD, MPHDavid N. Fisman, MD, MPHFrom University of Toronto, Toronto, Ontario M5T 3M7, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-156-3-201202070-00014 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Despite the gains in antimicrobial therapy and vaccines that have come in the past 100 years (1), epidemics and pandemics (synchronized, global epidemics) remain an important source of morbidity, mortality, and costs in high-, middle-, and low-income countries. Epidemics can be thought of as self-perpetuating, exponential growth processes; because infections are communicable, the more cases you have, the more cases you will get, as long as the population contains susceptible persons to infect.Epidemiologists refer to the key index of this type of growth as the reproductive number of an infectious disease—the number of new (incident) cases created by each ...References1. Armstrong GL, Conn LA, Pinner RW. Trends in infectious disease mortality in the United States during the 20th century. JAMA. 1999;281:61-6. [PMID: 9892452] CrossrefMedlineGoogle Scholar2. Fisman D; Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM). Modelling an influenza pandemic: a guide for the perplexed. CMAJ. 2009;181:171-3. [PMID: 19620267] CrossrefMedlineGoogle Scholar3. Hatchett RJ, Mecher CE, Lipsitch M. Public health interventions and epidemic intensity during the 1918 influenza pandemic. Proc Natl Acad Sci U S A. 2007;104:7582-7. [PMID: 17416679] CrossrefMedlineGoogle Scholar4. Bootsma MC, Ferguson NM. The effect of public health measures on the 1918 influenza pandemic in U.S. cities. Proc Natl Acad Sci U S A. 2007;104:7588-93. [PMID: 17416677] CrossrefMedlineGoogle Scholar5. Presanis AM, De Angelis D, Hagy A, Reed C, Riley S, Cooper BS, et al; New York City Swine Flu Investigation Team. The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Med. 2009;6:1000207. [PMID: 19997612] CrossrefMedlineGoogle Scholar6. Klaiman T, Kraemer JD, Stoto MA. Variability in school closure decisions in response to 2009 H1N1: a qualitative systems improvement analysis. BMC Public Health. 2011;11:73. [PMID: 21284865] CrossrefMedlineGoogle Scholar7. Brownstein JS, Kleinman KP, Mandl KD. Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system. Am J Epidemiol. 2005;162:686-93. [PMID: 16107568] CrossrefMedlineGoogle Scholar8. Galvani AP, Reluga TC, Chapman GB. Long-standing influenza vaccination policy is in accord with individual self-interest but not with the utilitarian optimum. Proc Natl Acad Sci U S A. 2007;104:5692-7. [PMID: 17369367] CrossrefMedlineGoogle Scholar9. Miller MA, Viboud C, Balinska M, Simonsen L. The signature features of influenza pandemics—implications for policy. N Engl J Med. 2009;360:2595-8. [PMID: 19423872] CrossrefMedlineGoogle Scholar10. Greer AL, Tuite A, Fisman DN. Age, influenza pandemics and disease dynamics. Epidemiol Infect. 2010;138:1542-9. [PMID: 20307340] CrossrefMedlineGoogle Scholar11. King JC, Cummings GE, Stoddard J, Readmond BX, Magder LS, Stong M, et al; SchoolMist Study Group. A pilot study of the effectiveness of a school-based influenza vaccination program. Pediatrics. 2005;116:868-73. [PMID: 16322144] CrossrefMedlineGoogle Scholar12. Loeb M, Russell ML, Moss L, Fonseca K, Fox J, Earn DJ, et al. Effect of influenza vaccination of children on infection rates in Hutterite communities: a randomized trial. JAMA. 2010;303:943-50. [PMID: 20215608] CrossrefMedlineGoogle Scholar13. Cauchemez S, Valleron AJ, Boëlle PY, Flahault A, Ferguson NM. Estimating the impact of school closure on influenza transmission from Sentinel data. Nature. 2008;452:750-4. [PMID: 18401408] CrossrefMedlineGoogle Scholar14. Cauchemez S, Ferguson NM, Wachtel C, Tegnell A, Saour G, Duncan B, et al. Closure of schools during an influenza pandemic. Lancet Infect Dis. 2009;9:473-81. [PMID: 19628172] CrossrefMedlineGoogle Scholar15. Heymann AD, Hoch I, Valinsky L, Kokia E, Steinberg DM. School closure may be effective in reducing transmission of respiratory viruses in the community. Epidemiol Infect. 2009;137:1369-76. [PMID: 19351434] CrossrefMedlineGoogle Scholar16. Keogh-Brown MR, Smith RD, Edmunds JW, Beutels P. The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and the Netherlands. Eur J Health Econ. 2010;11:543-54. [PMID: 19997956] CrossrefMedlineGoogle Scholar17. Gojovic MZ, Sander B, Fisman D, Krahn MD, Bauch CT. Modelling mitigation strategies for pandemic (H1N1) 2009. CMAJ. 2009;181:673-80. [PMID: 19825923] CrossrefMedlineGoogle Scholar18. Fisman DN, Savage R, Gubbay J, Achonu C, Akwar H, Farrell DJ, et al. Older age and a reduced likelihood of 2009 H1N1 virus infection [Letter]. N Engl J Med. 2009;361:2000-1. [PMID: 19907052] CrossrefMedlineGoogle Scholar19. Dushoff J, Plotkin JB, Levin SA, Earn DJ. Dynamical resonance can account for seasonality of influenza epidemics. Proc Natl Acad Sci U S A. 2004;101:16915-6. [PMID: 15557003] CrossrefMedlineGoogle Scholar20. Chowell G, Echevarría-Zuno S, Viboud C, Simonsen L, Tamerius J, Miller MA, et al. Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico. PLoS Med. 2011;8:1000436. [PMID: 21629683] CrossrefMedlineGoogle Scholar21. Earn DJ, He D, Loeb MB, Fonseca K, Lee BE, Dushoff J. Effects of school closure on incidence of pandemic influenza in Alberta, Canada. Ann Intern Med. 2011;156:173-81. LinkGoogle Scholar22. Brown ST, Tai JH, Bailey RR, Cooley PC, Wheaton WD, Potter MA, et al. Would school closure for the 2009 H1N1 influenza epidemic have been worth the cost?: a computational simulation of Pennsylvania. BMC Public Health. 2011;11:353. [PMID: 21599920] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: David N. Fisman, MD, MPHAffiliations: From University of Toronto, Toronto, Ontario M5T 3M7, Canada.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-2637.Corresponding Author: David N. Fisman, MD, MPH, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Room 678, Toronto, Ontario M5T 3M7, Canada; e-mail, david.fisman@utoronto.ca. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEffects of School Closure on Incidence of Pandemic Influenza in Alberta, Canada David J.D. Earn , Daihai He , Mark B. Loeb , Kevin Fonseca , Bonita E. Lee , and Jonathan Dushoff Metrics Cited bySocial connections with COVID-19–affected areas increase compliance with mobility restrictions 7 February 2012Volume 156, Issue 3Page: 238-240KeywordsAdolescentsAge groupsChildrenForecastingInfectious disease epidemiologyPrevention, policy, and public healthSchool closuresSocial distancingTemperatureVaccines ePublished: 7 February 2012 Issue Published: 7 February 2012 Copyright & PermissionsCopyright © 2012 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".