A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

BMC Public Health

Research Article

Sharad Malavade¹  Ricardo Izurieta¹  Tapas K Das²  Alex A Savachkin²  Diana M Prieto³  Andres Uribe⁴ 

[1] College of Public Health, University of South Florida, 33620, Tampa, FL, USA;Department of Industrial and Management Systems Engineering, University of South Florida, 33620, Tampa, FL, USA;Department of Industrial and Manufacturing Engineering, Western Michigan University, 49008, Kalamazoo, MI, USA;Department of Radiation Oncology, University of California - San Diego, 92093-0843, La Jolla, CA, USA;

关键词: Mitigation Strategy;    Pandemic Influenza;    Reproduction Number;    School Closure;    Antiviral Prophylaxis;   

DOI  :  10.1186/1471-2458-12-251

 received in 2011-05-19, accepted in 2012-03-30,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundIn recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.MethodsWe surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.ResultsWhile examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.ConclusionsTo adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

【 授权许可】

Unknown   
© Prieto et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.