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      The Model Repository of the Models of Infectious Disease Agent Study

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          Abstract

          The model repository (MREP) is a relational database management system (RDBMS) developed under the auspices of models of infectious disease agent study (MIDAS). The purpose of the MREP is to organize and catalog the models, results, and suggestions for using the MIDAS and to store them in a way to allow users to run models from an access-controlled disease MREP. The MREP contains source and object code of disease models developed by infectious disease modelers and tested in a production environment. Different versions of models used to describe various aspects of the same disease are housed in the repository. Models are linked to their developers and different versions of the codes are tied to Subversion, a version control tool. An additional element of the MREP will be to house, manage, and control access to a disease model results warehouse, which consists of output generated by the models contained in the MREP. The result tables and files are linked to the version of the model and the input parameters that collectively generated the results. The result tables are warehoused in a relational database that permits them to be easily identified, categorized, and downloaded.

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          Mitigation strategies for pandemic influenza in the United States.

          Timothy Germann, Kai Kadau, Ira Longini … (2006)
          Recent human deaths due to infection by highly pathogenic (H5N1) avian influenza A virus have raised the specter of a devastating pandemic like that of 1917-1918, should this avian virus evolve to become readily transmissible among humans. We introduce and use a large-scale stochastic simulation model to investigate the spread of a pandemic strain of influenza virus through the U.S. population of 281 million individuals for R(0) (the basic reproductive number) from 1.6 to 2.4. We model the impact that a variety of levels and combinations of influenza antiviral agents, vaccines, and modified social mobility (including school closure and travel restrictions) have on the timing and magnitude of this spread. Our simulations demonstrate that, in a highly mobile population, restricting travel after an outbreak is detected is likely to delay slightly the time course of the outbreak without impacting the eventual number ill. For R(0) < 1.9, our model suggests that the rapid production and distribution of vaccines, even if poorly matched to circulating strains, could significantly slow disease spread and limit the number ill to <10% of the population, particularly if children are preferentially vaccinated. Alternatively, the aggressive deployment of several million courses of influenza antiviral agents in a targeted prophylaxis strategy may contain a nascent outbreak with low R(0), provided adequate contact tracing and distribution capacities exist. For higher R(0), we predict that multiple strategies in combination (involving both social and medical interventions) will be required to achieve similar limits on illness rates.
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            Transmissibility of 1918 pandemic influenza

            Christina E Mills, James M. Robins, Marc Lipsitch (2004)
            The 1918 influenza pandemic killed 20–40 million people worldwide 1 , and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case 2 . Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases 2 . In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required. Supplementary information The online version of this article (doi:10.1038/nature03063) contains supplementary material, which is available to authorized users.
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              Modeling targeted layered containment of an influenza pandemic in the United States.

              M. Elizabeth Halloran, Neil M Ferguson, Stephen Eubank … (2008)
              Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with approximately 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.
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                Author and article information

                Contributors
                Phillip C. Cooley
                D. Roberts
                V. D. Bakalov
                S. Bikmal
                S. Cantor
                T. Costandine
                L. Ganapathi
                B. J. Golla
                G. Grubbs
                C. Hollingsworth
                S. Li
                Y. Qin
                William Savage
                D. Simoni
                E. Solano
                D. Wagener
                Journal
                Journal ID (nlm-ta): IEEE Trans Inf Technol Biomed
                Journal ID (iso-abbrev): IEEE Trans Inf Technol Biomed
                Journal ID (publisher-id): 0047700
                Journal ID (coden): ITIBFX
                Journal ID (publisher-id): TITB
                Title: Ieee Transactions on Information Technology in Biomedicine
                Publisher: IEEE
                ISSN (Print): 1089-7771
                ISSN (Electronic): 1558-0032
                Publication date Collection: 10 July 2008
                Publication date (Electronic): 10 July 2008
                Volume: 12
                Issue: 4
                Pages: 513-522
                Affiliations
                [1 ] divisionRTI International; Research Triangle ParkNC 27709 USA
                [2 ] institutionOptimacy Corporation; Colorado SpringsCO 80905 USA
                Article
                DOI: 10.1109/TITB.2007.910354
                PMC ID: 2741407
                PubMed ID: 18632331
                SO-VID: 93f1864a-9e3f-4ffa-85ff-b82c63d3b787
                Copyright statement: Copyright @ 2008
                License:

                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

                History
                Date received : 26 February 2007
                Date revision received : 28 February 2007
                Date revision received : 27 September 2007
                Page count
                Figures: 2, Tables: 0, Equations: 3, References: 25, Pages: 10
                Funding
                Funded by: RTI International;
                This work was supported by the RTI International.
                Categories
                Subject: Article

                Keywords: data management,data warehouse,epidemiological disease models,model repository (mrep),relational database
                Data availability:
                Keywords: data management, data warehouse, epidemiological disease models, model repository (mrep), relational database

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