Literature DB >> 20029670

Modeling the critical care demand and antibiotics resources needed during the Fall 2009 wave of influenza A(H1N1) pandemic.

Duygu Balcan1, Vittoria Colizza, Andrew C Singer, Christos Chouaid, Hao Hu, Bruno Gonçalves, Paolo Bajardi, Chiara Poletto, Jose J Ramasco, Nicola Perra, Michele Tizzoni, Daniela Paolotti, Wouter Van den Broeck, Alainjacques Valleron, Alessandro Vespignani.   

Abstract

While the H1N1 pandemic is reaching high levels of influenza activity in the Northern Hemisphere, the attention focuses on the ability of national health systems to respond to the expected massive influx of additional patients. Given the limited capacity of health care providers and hospitals and the limited supplies of antibiotics, it is important to predict the potential demand on critical care to assist planning for the management of resources and plan for additional stockpiling. We develop a disease model that considers the development of influenza-associated complications and incorporate it into a global epidemic model to assess the expected surge in critical care demands due to viral and bacterial pneumonia. Based on the most recent estimates of complication rates, we predict the expected peak number of intensive care unit beds and the stockpile of antibiotic courses needed for the current pandemic wave. The effects of dynamic vaccination campaigns, and of variations of the relative proportion of bacterial co-infection in complications and different length of staying in the intensive care unit are explored.

Entities:  

Year:  2009        PMID: 20029670      PMCID: PMC2792767          DOI: 10.1371/currents.rrn1133

Source DB:  PubMed          Journal:  PLoS Curr        ISSN: 2157-3999


  40 in total

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5.  Mitigation strategies for pandemic influenza in the United States.

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6.  Forecast and control of epidemics in a globalized world.

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7.  Delaying the international spread of pandemic influenza.

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8.  Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain.

Authors:  Jordi Rello; Alejandro Rodríguez; Pedro Ibañez; Lorenzo Socias; Javier Cebrian; Asunción Marques; José Guerrero; Sergio Ruiz-Santana; Enrique Marquez; Frutos Del Nogal-Saez; Francisco Alvarez-Lerma; Sergio Martínez; Miquel Ferrer; Manuel Avellanas; Rosa Granada; Enrique Maraví-Poma; Patricia Albert; Rafael Sierra; Loreto Vidaur; Patricia Ortiz; Isidro Prieto del Portillo; Beatriz Galván; Cristóbal León-Gil
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Journal:  Emerg Infect Dis       Date:  2008-08       Impact factor: 6.883

10.  Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study.

Authors:  Vittoria Colizza; Alain Barrat; Marc Barthélemy; Alessandro Vespignani
Journal:  BMC Med       Date:  2007-11-21       Impact factor: 8.775

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  12 in total

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Journal:  Milbank Q       Date:  2013-03       Impact factor: 4.911

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Journal:  Epidemics       Date:  2018-02-24       Impact factor: 4.396

Review 3.  The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale.

Authors:  Wouter Van den Broeck; Corrado Gioannini; Bruno Gonçalves; Marco Quaggiotto; Vittoria Colizza; Alessandro Vespignani
Journal:  BMC Infect Dis       Date:  2011-02-02       Impact factor: 3.090

4.  Assessing the ecotoxicologic hazards of a pandemic influenza medical response.

Authors:  Andrew C Singer; Vittoria Colizza; Heike Schmitt; Johanna Andrews; Duygu Balcan; Wei E Huang; Virginie D J Keller; Alessandro Vespignani; Richard J Williams
Journal:  Environ Health Perspect       Date:  2011-03-02       Impact factor: 9.031

5.  Health system resource gaps and associated mortality from pandemic influenza across six Asian territories.

Authors:  James W Rudge; Piya Hanvoravongchai; Ralf Krumkamp; Irwin Chavez; Wiku Adisasmito; Pham Ngoc Chau; Bounlay Phommasak; Weerasak Putthasri; Chin-Shui Shih; Mart Stein; Aura Timen; Sok Touch; Ralf Reintjes; Richard Coker
Journal:  PLoS One       Date:  2012-02-21       Impact factor: 3.240

6.  Modeling for Estimating Influenza Patients from ILI Surveillance Data in Korea.

Authors:  Joo-Sun Lee; Sun-Hee Park; Jin-Woong Moon; Jacob Lee; Yong Gyu Park; Yong Kyun Roh
Journal:  Osong Public Health Res Perspect       Date:  2011-08-04

7.  Intra- and inter-pandemic variations of antiviral, antibiotics and decongestants in wastewater treatment plants and receiving rivers.

Authors:  Andrew C Singer; Josef D Järhult; Roman Grabic; Ghazanfar A Khan; Richard H Lindberg; Ganna Fedorova; Jerker Fick; Michael J Bowes; Björn Olsen; Hanna Söderström
Journal:  PLoS One       Date:  2014-09-25       Impact factor: 3.240

8.  [New flu (H1n1): phantom or intensive medicine super-GAU -- a view from the Austrian reality ].

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Journal:  Wien Klin Wochenschr       Date:  2010-01       Impact factor: 1.704

9.  Severe virus influenza A H1N1 related pneumonia and community-acquired pneumonia: differences in the evolution.

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Journal:  Rev Bras Ter Intensiva       Date:  2013 Apr-Jun

10.  Digitizable therapeutics for decentralized mitigation of global pandemics.

Authors:  Adar Hacohen; Reuven Cohen; Sol Efroni; Baruch Barzel; Ido Bachelet
Journal:  Sci Rep       Date:  2019-10-04       Impact factor: 4.379

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