Literature DB >> 21153911

Models cannot predict future outbreaks: A/H1N1 virus, the paradigm.

Antoine Nougairède1, Rémi N Charrel, Didier Raoult.   

Abstract

Evolution of the industrialized society had led to a risk management policy in many domains. Assessment of health care risk in the case of infectious diseases often includes mathematical models. Results of modelling were used in France to design emergency plans against flu pandemic. We believe that models cannot predict the features of the future outbreaks because the intrinsic properties of an emergent pathogen and the ecosystem in which it is developing are very complex. Of course, prediction of future outbreaks is not possible without using models, but we think that it is an illusion to presently believe that an emerging phenomenon can be anticipated by using only prediction from models. The recent pandemic caused by the novel A/H1N1 virus has confirmed the unpredictability of infectious diseases. The rapid evolution in several domains such as antimicrobial therapeutics, vaccine and hygiene conditions make comparison with past pandemics hard. The adherence of populations to prevention measures and immunisation campaigns are unpredictable. In addition, the presentation of pessimistic models is deleterious. They impress governments and provoke fears. There is a striking necessity to develop the number and the capacities of sentinel centres to take and adapt decisions based on timely available scientific information.

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Year:  2010        PMID: 21153911     DOI: 10.1007/s10654-010-9533-6

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  28 in total

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5.  Central-nervous-system demyelination after immunisation with recombinant hepatitis B vaccine.

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6.  Effects of a large-scale intervention with influenza and 23-valent pneumococcal vaccines in adults aged 65 years or older: a prospective study.

Authors:  B Christenson; P Lundbergh; J Hedlund; A Ortqvist
Journal:  Lancet       Date:  2001-03-31       Impact factor: 79.321

7.  The Japanese experience with vaccinating schoolchildren against influenza.

Authors:  T A Reichert; N Sugaya; D S Fedson; W P Glezen; L Simonsen; M Tashiro
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8.  Proper handwashing promotes wellness in child care.

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9.  Effectiveness of influenza vaccine in the community-dwelling elderly.

Authors:  Kristin L Nichol; James D Nordin; David B Nelson; John P Mullooly; Eelko Hak
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10.  Severity of pneumonia due to new H1N1 influenza virus in ferrets is intermediate between that due to seasonal H1N1 virus and highly pathogenic avian influenza H5N1 virus.

Authors:  Judith M A van den Brand; Koert J Stittelaar; Geert van Amerongen; Guus F Rimmelzwaan; James Simon; Emmie de Wit; Vincent Munster; Theo Bestebroer; Ron A M Fouchier; Thijs Kuiken; Albert D M E Osterhaus
Journal:  J Infect Dis       Date:  2010-04-01       Impact factor: 5.226

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  8 in total

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2.  Molecular, epidemiological, and clinical complexities of predicting patterns of infectious diseases.

Authors:  Didier Raoult
Journal:  Front Microbiol       Date:  2011-02-11       Impact factor: 5.640

3.  Influenza-attributable deaths in south-eastern France (1999 to 2010): mortality predictions were undependable.

Authors:  Simon-Djamel Thiberville; Jean Gaudart; Didier Raoult; Remi N Charrel
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4.  Adaptive management and the value of information: learning via intervention in epidemiology.

Authors:  Katriona Shea; Michael J Tildesley; Michael C Runge; Christopher J Fonnesbeck; Matthew J Ferrari
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6.  Predictive validation of an influenza spread model.

Authors:  Ayaz Hyder; David L Buckeridge; Brian Leung
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7.  EPIMIC: A Simple Homemade Computer Program for Real-Time EPIdemiological Surveillance and Alert Based on MICrobiological Data.

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Journal:  PLoS One       Date:  2015-12-14       Impact factor: 3.240

Review 8.  Modelling in infectious diseases: between haphazard and hazard.

Authors:  A Neuberger; M Paul; A Nizar; D Raoult
Journal:  Clin Microbiol Infect       Date:  2013-07-23       Impact factor: 8.067

  8 in total

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