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DESCRIPTION (provided by applicant): Vaccination protects not only the people who are vaccinated, but others in the community who are thereby less likely to catch the disease. Conversely, when people choose not to get vaccinated, they both take on a risk to themselves and put others in their family and community at risk. We will develop strategies for flu vaccination, taking into account how people choose whether to be vaccinated, the effect of their choices on others in their community and the cost-effectiveness of programs to improve vaccination. The implementation of optimal vaccination policies is governed by how individuals make decisions about vaccination. When vaccination is voluntary, medical decisions are made by individuals. Collectively, individual decisions determine the level of population immunity and thus the magnitude of subsequent epidemics and the ultimate cost-effectiveness of vaccination efforts. While children are responsible for the majority of influenza transmission, it is the elderly that suffer the most associated morbidity and mortality. Consequently, the utility of vaccine allocation to children is greater for the population than it is for the children themselves. The long-term goal of the proposed research, which we will address in three Specific Aims, is to define policies that encourage adherence to optimal vaccination strategies in the community-wide control of influenza. (1) To evaluate the repercussions of individual-level decisions on the population's adherence to different strategies, we will construct the first model of the evolutionary epidemiology of influenza to be integrated with a game-theoretic model of age-specific demand for vaccination. We will apply Markov decision theory to calculate utilities and develop an algorithm for determining Nash equilibria of vaccination behavior governed by self-interest, in addition to utilitarian vaccine allocation strategies. (2) We will identify factors that have the greatest impact on aligning individual-optimal and group-optimal vaccination. To determine the likely influence of improving dissemination of information about influenza and its vaccine, we will calculate decision utilities from actual infection versus vaccine risks, and compare these with perceived costs and benefits derived from our psychological data. We will also evaluate incentive structures, such as subsidies, to encourage conscientious international surveillance, intervention and reporting of an avian influenza outbreak. (3) We will conduct questionnaire and experimental game studies to examine how an individual's vaccination decisions are influenced by aggregate vaccination behavior. We will use our psychological data to parameterize our model and verify its predictions. This fundamentally interdisciplinary proposal combines mathematical modeling with psychological data collection and parameterization, economics with epidemiology, and individual decision making with population-level public health outcomes.
期刊论文(6)
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会议论文
DOI: 10.1177/0272989x10374112
发表时间: 2010-07
期刊: Medical decision making : an international journal of the Society for Medical Decision Making
影响因子: --
作者: [Shim E, Chapman GB, Galvani AP]
通讯作者: Galvani AP
DOI: 10.1016/j.mbs.2011.01.003
发表时间: 2011-04
期刊: Mathematical biosciences
影响因子: 4.3
作者: [Reluga TC, Galvani AP]
通讯作者: Galvani AP
Impact of program scale and indirect effects on the cost-effectiveness of vaccination programs.
计划规模的影响和对疫苗接种计划成本效益的间接影响。
DOI: 10.1177/0272989x12441397
发表时间: 2012
期刊: Medical decision making : an international journal of the Society for Medical Decision Making
影响因子: --
作者: [Ibuka,Yoko, Paltiel,ADavid, Galvani,AlisonP]
通讯作者: Galvani,AlisonP
How do people value life?
人们如何珍视生命?
DOI: 10.1177/0956797609357707
发表时间: 2010
期刊: Psychological science
影响因子: 8.2
作者: [Li,Meng, Vietri,Jeffrey, Galvani,AlisonP, Chapman,GretchenB]
通讯作者: Chapman,GretchenB
Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data
  • 批准号:
    10399134
  • 项目类别:
  • 资助金额:
    $11.45万
  • 财政年份:
    2020
  • 负责人:
    ALISON P GALVANI
  • 依托单位:
Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data
  • 批准号:
    10571939
  • 项目类别:
  • 资助金额:
    $57.5万
  • 财政年份:
    2020
  • 负责人:
    ALISON P GALVANI
  • 依托单位:
Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data
  • 批准号:
    10113533
  • 项目类别:
  • 资助金额:
    $59.6万
  • 财政年份:
    2020
  • 负责人:
    ALISON P GALVANI
  • 依托单位:
Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data
  • 批准号:
    10341179
  • 项目类别:
  • 资助金额:
    $57.5万
  • 财政年份:
    2020
  • 负责人:
    ALISON P GALVANI
  • 依托单位:
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