Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
批准号:
10435483
负责人:
Wan Yang
金额:
$51.03万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-07-01 至 2025-06-30
关键词:
AdultAffectAgeAge DistributionAmericasAreaBayesian AnalysisBayesian ModelingCase StudyCause of DeathChildChild MortalityChinaChineseCitiesCommunicable DiseasesComplexDataData SetDemographic FactorsDevelopmentDiseaseDisease OutbreaksEmerging Communicable DiseasesEpidemicEvidence based interventionFrequenciesFutureHospitalsIndustrializationInfantInfectionInternationalInterventionMass VaccinationsMaternally-Acquired ImmunityMeaslesMeasuresMethodsMigrantMigrant WorkersModelingModernizationNational Institute of Allergy and Infectious DiseaseNewborn InfantOutcomeParentsPatternPoliciesPoliomyelitisPopulationPopulation DynamicsPublic HealthResearchRubellaSmallpoxSouth AmericaStatistical MethodsStructureSystemTestingTravelVaccinatedVaccinationVaccinesVenezuelaWorkbasechronic infectioncombatdemographicsemerging pathogenimprovedinfectious disease modelmathematical modelmigrationnetwork modelsnovelrural areasurveillance datasynergismtheoriestransmission processyoung adult
中文摘要
摘要
继天花之后,还针对其他几种传染病,包括麻疹、小儿麻痹症和风疹。
为了根除。然而,事实证明,消除这些后一种感染具有挑战性。虽然大规模接种疫苗
已经阻止了地方性麻疹在美洲大部分地区的传播,疫苗接种率与麻疹类似
继续在世界一些地区造成大规模疫情。此外,这些流行病可能会传播到其他
由于全球连通性高和当地疫苗覆盖率降低而导致的区域(例如最近麻疹的增加
美国的病例)。这些观察表明,目前对疾病持续性的理解复杂
人口系统仍然不完善,必须加以改进,以根除感染,如
麻疹。为了提高对疾病持续性的理解,拟议的工作将开发模型-贝叶斯
推理系统使用数学建模和统计方法来识别空间、时间和
在大规模接种疫苗时代,导致麻疹持续传播的人口因素。一个
将测试假设的传播机制范围,包括i)疫苗接种率、ii)
人口统计(如出生率和年龄结构),三)接触模式,四)空间连通性和迁徙,
和/或v)母体免疫力强。此外,该项目将测试潜在的干预措施,其基础是
已确定的传播机制以及对未来麻疹流行动态的预测
通知消除麻疹的工作。通过利用详细的麻疹监测数据,传染病
建模和贝叶斯推理方法,拟议的工作将产生对麻疹的新理解
在现代人群中的传播动态,并提供模型引导的干预策略。项目成果
还可为其他旨在根除的感染(如风疹)的控制战略提供信息。此外,
这里开发的模型推理系统可以适用于研究广泛的(重新)出现的传染性疾病
疾病。
英文摘要
Abstract
Following smallpox, several other infectious diseases including measles, polio and rubella have been targeted
for eradication. However, elimination of these latter infections has proven challenging. While mass vaccination
has halted endemic measles transmission in most of the Americas, with similar high vaccination rates, measles
continues to cause large epidemics in some parts of the world. Further, these epidemics can spread to other
regions due to high global connectivity and reduced local vaccine coverage (e.g. the recent increases in measles
cases in the US). These observations indicate that current understanding of disease persistence in complex
population systems remains incomplete and must be improved to effect eradication of infections such as
measles. To improve this understanding of disease persistence, the proposed work will develop model-Bayesian
inference systems using mathematical modeling and statistical methods to identify the spatial, temporal, and
demographical factors contributing to the persistent transmission of measles in the mass vaccination era. A
range of hypothesized transmission mechanisms will be tested, including changes in i) vaccination rate, ii)
demographics (e.g. birthrates and age structures), iii) contact pattern, iv) spatial connectivity and migration,
and/or v) strength of maternal immunity. Further, the project will test potential intervention measures based on
the identified transmission mechanisms as well as generate predictions of future measles epidemic dynamics to
inform measles elimination efforts. By leveraging detailed measles surveillance data, infectious disease
modeling, and Bayesian inference methods, the proposed work will yield new understanding of measles
transmission dynamics in modern populations and provide model-guided intervention strategies. Project findings
may also inform control strategies for other infections targeted for eradication (e.g. rubella). In addition, the
model-inference systems developed here can be adapted to study a broad range of (re)emerging infectious
diseases.
期刊论文(8)
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DOI:
10.1111/irv.12976
发表时间:
2022-07
期刊:
INFLUENZA AND OTHER RESPIRATORY VIRUSES
影响因子:
4.4
作者:
[Yuan, Haokun, Yeung, Alice, Yang, Wan]
通讯作者:
Yang, Wan
Epidemiological characteristics of the B.1.526 SARS-CoV-2 variant.
B.1.526 SARS-COV-2变体的流行病学特征。
DOI:
10.1126/sciadv.abm0300
发表时间:
2022-01-28
期刊:
Science advances
影响因子:
13.6
作者:
[Yang W, Greene SK, Peterson ER, Li W, Mathes R, Graf L, Lall R, Hughes S, Wang J, Fine A]
通讯作者:
Fine A
DOI:
10.1017/s0950268823001322
发表时间:
2023-08-22
期刊:
EPIDEMIOLOGY AND INFECTION
影响因子:
4.2
作者:
[Chen, Jianjiu, Zhang, Wenyi, Wang, Yong, Yang, Wan]
通讯作者:
Yang, Wan
Transmission dynamics of and insights from the 2018-2019 measles outbreak in New York City: A modeling study.
纽约市 2018-2019 年麻疹疫情的传播动态和见解:一项建模研究。
DOI:
10.1126/sciadv.aaz4037
发表时间:
2020
期刊:
Science advances
影响因子:
13.6
作者:
[Yang,Wan]
通讯作者:
Yang,Wan
DOI:
10.1111/irv.12968
发表时间:
2022-07
期刊:
INFLUENZA AND OTHER RESPIRATORY VIRUSES
影响因子:
4.4
作者:
[Yuan, Haokun, Reynolds, Connor, Ng, Sydney, Yang, Wan]
通讯作者:
Yang, Wan
Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes
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批准号:10645617
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项目类别:
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资助金额:$24.68万
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财政年份:2023
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UNCOVER: underlying novel causes of onset of very early cancer research
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批准号:10675591
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项目类别:
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资助金额:$39.13万
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财政年份:2021
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负责人:Wan Yang
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依托单位:
UNCOVER: underlying novel causes of onset of very early cancer research
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批准号:10482393
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项目类别:
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资助金额:$39.13万
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财政年份:2021
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负责人:Wan Yang
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依托单位:
UNCOVER: underlying novel causes of onset of very early cancer research
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批准号:10303652
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项目类别:
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资助金额:$41.14万
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财政年份:2021
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负责人:Wan Yang
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依托单位:
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
-
批准号:10199927
-
项目类别:
-
资助金额:$49.81万
-
财政年份:2019
-
负责人:Wan Yang
-
依托单位:
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
-
批准号:9795652
-
项目类别:
-
资助金额:$50.78万
-
财政年份:2019
-
负责人:Wan Yang
-
依托单位:
海外基金