Literature DB >> 21727183

The use of mathematical models to inform influenza pandemic preparedness and response.

Joseph T Wu1, Benjamin J Cowling.   

Abstract

Influenza pandemics have occurred throughout history and were associated with substantial excess mortality and morbidity. Mathematical models of infectious diseases permit quantitative description of epidemic processes based on the underlying biological mechanisms. Mathematical models have been widely used in the past decade to aid pandemic planning by allowing detailed predictions of the speed of spread of an influenza pandemic and the likely effectiveness of alternative control strategies. During the initial waves of the 2009 influenza pandemic, mathematical models were used to track the spread of the virus, predict the time course of the pandemic and assess the likely impact of large-scale vaccination. While mathematical modeling has made substantial contributions to influenza pandemic preparedness, its use as a realtime tool for pandemic control is currently limited by the lack of essential surveillance information such as serological data. Mathematical modeling provided a useful framework for analyzing and interpreting surveillance data during the 2009 influenza pandemic, for highlighting limitations in existing pandemic surveillance systems, and for guiding how these systems should be strengthened in order to cope with future epidemics of influenza or other emerging infectious diseases.

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Year:  2011        PMID: 21727183      PMCID: PMC3178755          DOI: 10.1258/ebm.2010.010271

Source DB:  PubMed          Journal:  Exp Biol Med (Maywood)        ISSN: 1535-3699


  70 in total

1.  Assessing the impact of airline travel on the geographic spread of pandemic influenza.

Authors:  Rebecca F Grais; J Hugh Ellis; Gregory E Glass
Journal:  Eur J Epidemiol       Date:  2003       Impact factor: 8.082

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Journal:  Epidemiol Infect       Date:  2007-04-20       Impact factor: 2.451

3.  How generation intervals shape the relationship between growth rates and reproductive numbers.

Authors:  J Wallinga; M Lipsitch
Journal:  Proc Biol Sci       Date:  2007-02-22       Impact factor: 5.349

Review 4.  How to maintain surveillance for novel influenza A H1N1 when there are too many cases to count.

Authors:  Marc Lipsitch; Frederick G Hayden; Benjamin J Cowling; Gabriel M Leung
Journal:  Lancet       Date:  2009-08-11       Impact factor: 79.321

5.  Comparative epidemiology of pandemic and seasonal influenza A in households.

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Journal:  N Engl J Med       Date:  2010-06-10       Impact factor: 91.245

6.  Strategy for distribution of influenza vaccine to high-risk groups and children.

Authors:  Ira M Longini; M Elizabeth Halloran
Journal:  Am J Epidemiol       Date:  2005-02-15       Impact factor: 4.897

7.  Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic.

Authors:  Gavin J D Smith; Dhanasekaran Vijaykrishna; Justin Bahl; Samantha J Lycett; Michael Worobey; Oliver G Pybus; Siu Kit Ma; Chung Lam Cheung; Jayna Raghwani; Samir Bhatt; J S Malik Peiris; Yi Guan; Andrew Rambaut
Journal:  Nature       Date:  2009-06-25       Impact factor: 49.962

8.  Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore.

Authors:  Jimmy Boon Som Ong; Mark I-Cheng Chen; Alex R Cook; Huey Chyi Lee; Vernon J Lee; Raymond Tzer Pin Lin; Paul Ananth Tambyah; Lee Gan Goh
Journal:  PLoS One       Date:  2010-04-14       Impact factor: 3.240

9.  Reducing the impact of the next influenza pandemic using household-based public health interventions.

Authors:  Joseph T Wu; Steven Riley; Christophe Fraser; Gabriel M Leung
Journal:  PLoS Med       Date:  2006-09       Impact factor: 11.069

10.  Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA.

Authors:  Laura Forsberg White; Jacco Wallinga; Lyn Finelli; Carrie Reed; Steven Riley; Marc Lipsitch; Marcello Pagano
Journal:  Influenza Other Respir Viruses       Date:  2009-11       Impact factor: 4.380

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  16 in total

Review 1.  Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential.

Authors:  Manoj Gambhir; Catherine Bozio; Justin J O'Hagan; Amra Uzicanin; Lucinda E Johnson; Matthew Biggerstaff; David L Swerdlow
Journal:  Clin Infect Dis       Date:  2015-05-01       Impact factor: 9.079

2.  Indemics: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling.

Authors:  Keith R Bisset; Jiangzhuo Chen; Suruchi Deodhar; Xizhou Feng; Yifei Ma; Madhav V Marathe
Journal:  ACM Trans Model Comput Simul       Date:  2014-01       Impact factor: 1.075

Review 3.  Deviations in influenza seasonality: odd coincidence or obscure consequence?

Authors:  M Moorthy; D Castronovo; A Abraham; S Bhattacharyya; S Gradus; J Gorski; Y N Naumov; N H Fefferman; E N Naumova
Journal:  Clin Microbiol Infect       Date:  2012-10       Impact factor: 8.067

4.  Physical radiofrequency adjuvant enhances immune responses to influenza H5N1 vaccination.

Authors:  Zhuofan Li; Ki-Hye Kim; Noopur Bhatnagar; Bo Ryoung Park; Subbiah Jeeva; Yu-Jin Jung; Jannatul Raha; Sang-Moo Kang; Xinyuan Chen
Journal:  FASEB J       Date:  2022-03       Impact factor: 5.191

5.  Assessment of malaria incidence using the Richards model in Arunachal Pradesh, India.

Authors:  M Srinivasa Rao; U Suryanaryana Murty; K Madhusudhan Rao; N Kartik; G Preeyantee; N Balakrishna
Journal:  Epidemiol Infect       Date:  2014-01-07       Impact factor: 4.434

6.  The Effectiveness of Age-Specific Isolation Policies on Epidemics of Influenza A (H1N1) in a Large City in Central South China.

Authors:  Ruchun Liu; Ross Ka-kit Leung; Tianmu Chen; Xixing Zhang; Faming Chen; Shuilian Chen; Jin Zhao
Journal:  PLoS One       Date:  2015-07-10       Impact factor: 3.240

Review 7.  Modeling infectious disease dynamics in the complex landscape of global health.

Authors:  Hans Heesterbeek; Roy M Anderson; Viggo Andreasen; Shweta Bansal; Daniela De Angelis; Chris Dye; Ken T D Eames; W John Edmunds; Simon D W Frost; Sebastian Funk; T Deirdre Hollingsworth; Thomas House; Valerie Isham; Petra Klepac; Justin Lessler; James O Lloyd-Smith; C Jessica E Metcalf; Denis Mollison; Lorenzo Pellis; Juliet R C Pulliam; Mick G Roberts; Cecile Viboud
Journal:  Science       Date:  2015-03-13       Impact factor: 47.728

8.  Understanding small Chinese cities as COVID-19 hotspots with an urban epidemic hazard index.

Authors:  Tianyi Li; Jiawen Luo; Cunrui Huang
Journal:  Sci Rep       Date:  2021-07-19       Impact factor: 4.379

9.  Anticipating the prevalence of avian influenza subtypes H9 and H5 in live-bird markets.

Authors:  Kim M Pepin; Jia Wang; Colleen T Webb; Jennifer A Hoeting; Mary Poss; Peter J Hudson; Wenshan Hong; Huachen Zhu; Yi Guan; Steven Riley
Journal:  PLoS One       Date:  2013-02-07       Impact factor: 3.240

10.  Investigation of key interventions for shigellosis outbreak control in China.

Authors:  Tianmu Chen; Ross Ka-Kit Leung; Zi Zhou; Ruchun Liu; Xixing Zhang; Lijie Zhang
Journal:  PLoS One       Date:  2014-04-15       Impact factor: 3.240

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