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      Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0–19, 20–64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0–19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20–64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0–19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0–19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.

          Author summary

          The study objective is to estimate the epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago, to assist in vaccine intervention priorities. Population dynamics play an important role in influenza pandemic planning and response. To optimally allocate limited vaccine resources, it is important to inform decision makers and public health officials about both the direct benefit among vaccinated population and the indirect benefit among non-vaccinated population. This study adds to the evidence of prior studies by using a detailed agent-based model for estimating the direct and indirect benefits of epidemiological and economic impact of vaccine-based interventions. This study can be extended to analyze for a range of vaccine compliance and efficacy values at different attack rates of influenza pandemics in different rural and urban areas of the United States and at the country level, to infer objective prioritization criteria for influenza vaccine interventions among different risk and age groups.

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          Most cited references 39

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          Mitigation strategies for pandemic influenza in the United States.

          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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            Modeling targeted layered containment of an influenza pandemic in the United States.

            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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              The economic impact of pandemic influenza in the United States: priorities for intervention.

               M. Meltzer,  N Cox,  K. Fukuda (1999)
              We estimated the possible effects of the next influenza pandemic in the United States and analyzed the economic impact of vaccine-based interventions. Using death rates, hospitalization data, and outpatient visits, we estimated 89,000 to 207,000 deaths; 314,000 to 734,000 hospitalizations; 18 to 42 million outpatient visits; and 20 to 47 million additional illnesses. Patients at high risk (15% of the population) would account for approximately 84% of all deaths. The estimated economic impact would be US$71.3 to $166.5 billion, excluding disruptions to commerce and society. At $21 per vaccinee, we project a net savings to society if persons in all age groups are vaccinated. At $62 per vaccinee and at gross attack rates of 25%, we project net losses if persons not at high risk for complications are vaccinated. Vaccinating 60% of the population would generate the highest economic returns but may not be possible within the time required for vaccine effectiveness, especially if two doses of vaccine are required.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                1 June 2017
                June 2017
                : 13
                : 6
                Affiliations
                [1 ]Department of Population Health Sciences, Virginia Tech, Blacksburg, VA, United States of America
                [2 ]Network Dynamics and Simulation Science Lab, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America
                London School of Hygiene & Tropical Medicine, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: KMA AM.

                • Data curation: ND.

                • Formal analysis: ND.

                • Funding acquisition: AM KMA.

                • Investigation: ND AM BLL SS SGE KMA.

                • Methodology: ND AM BLL SS SGE KMA.

                • Project administration: AM KMA.

                • Resources: ND AM BLL SS SGE KMA.

                • Software: ND.

                • Supervision: KMA.

                • Validation: ND.

                • Visualization: ND.

                • Writing – original draft: ND KMA.

                • Writing – review & editing: ND AM BLL SS SGE KMA.

                Article
                PCOMPBIOL-D-16-01440
                10.1371/journal.pcbi.1005521
                5453424
                28570660
                © 2017 Dorratoltaj et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 10, Tables: 9, Pages: 25
                Product
                Funding
                Funded by: NIH NIGMS
                Award ID: R01GM109718
                Award Recipient :
                Funded by: NSF-ICES
                Award ID: 1216000
                Award Recipient :
                Funded by: NSF NRT-DESE
                Award ID: 1545362
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000774, Defense Threat Reduction Agency;
                Award ID: HDTRA1-11-1-0016
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000774, Defense Threat Reduction Agency;
                Award ID: HDTRA1-11-D-0016-0001
                Award Recipient :
                This study is supported by NIH NIGMS R01GM109718, NSF-ICES 1216000, NSF NRT-DESE 1545362, DTRA HDTRA1-11-1-0016, and DTRA HDTRA1-11-D-0016-0001. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Infectious Diseases
                Viral Diseases
                Influenza
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Biology and Life Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Public and Occupational Health
                Preventive Medicine
                Vaccination and Immunization
                People and Places
                Population Groupings
                Age Groups
                Social Sciences
                Economics
                Economic Analysis
                Economic Impact Analysis
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Epidemiology
                People and Places
                Population Groupings
                Age Groups
                Children
                People and Places
                Population Groupings
                Families
                Children
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology

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