COVID-19 transmission in Alaska: geographic and genomic modeling

阿拉斯加的 COVID-19 传播：地理和基因组模型

• 批准号: 10539679
• 负责人: BRIAN M BARNES
• 金额: $ 12.62万
• 依托单位: UNIVERSITY OF ALASKA FAIRBANKS
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-24 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10539679
• 关键词: 2019-nCoV; Accounting; Address; Affect; Air; Alaska; Alaska Native; Alaskan; American Indians; Area; Biological; Birth; COVID-19; COVID-19 pandemic; Censuses; Cessation of life; Characteristics; Collection; Coronavirus; Data; Data Analyses; Data Collection; Data Sources; Dependence; Developing Countries; Development; Diffuse; Disease; Disease Progression; Economic Conditions; Economics; Epidemic; Epidemiology; Exhibits; Foundations; Future; Genetic; Genomics; Geographic Locations; Geography; Goals; Growth; Heterogeneity; Hospitalization; Hospitals; Household; Incidence; Infection; Knowledge; Locales; Location; Mathematics; Methodology; Methods; Modeling; Modernization; Movement; Municipalities; Native-Born; Pattern; Play; Population; Population Density; Prevalence; Process; Public Health; Records; Research; Rural Population; SARS-CoV-2 transmission; Sampling; Services; Socioeconomic Factors; Statistical Data Interpretation; Statistical Models; Structure; Suburban Population; Techniques; Test Result; Time; Transportation; Vaccination; Variant; Virus; Work; base; data modeling; density; design; disease transmission; epidemiologic data; epidemiological model; genomic data; geographic difference; geographic population; health data; improved; innovation; insight; large datasets; member; model development; novel; pandemic coronavirus; pandemic disease; pathogen; predictive modeling; programs; rural area; suburb; theories; tool; transmission process

PROJECT SUMMARY Effective modeling of epidemic transmission depends on both a mathematical description of the most important aspects of disease transmission and progression, and estimation of parameters from data. Collection of genetic sequences of SARS-CoV-2 samples throughout the COVID-19 pandemic has produced an extensive new data source to supplement more traditional public health ones. The geographical population structure in Alaska, characterized by subpopulations with well-defined interconnections, offers a unique opportunity to use such data to develop new models and parameter estimation methods. Moreover, in addition to geographic heterogeneity, the progression of pandemic has involved changes in prevalence of coronavirus variants as well as mitigation strategies, so that parameters have a temporal dependence as well. This project leverages public health and sequencing data from the coronavirus pandemic through three main modeling thrusts. A statistical model of variant spread, which captures both geographic structure and changing variant proportions will be developed and applied to the Alaskan genomic data set. Estimation of transmission rates in and between regions using phylodynamic approaches will be investigated. This includes development of a new statistical approach for rapid estimation of parameters that change through time, as well as a focused effort to study transmission patterns between subpopulations. Finally, a traditional compartment model of COVID-19 in Alaska will be developed, which captures the key geographic features affecting disease dynamics in the state. While initially fitted using public health data such as hospital incidence rates and testing results, it will then be modified to better reflect insights and estimates emerging from the first two thrusts. A goal is to promote and demonstrate improved predictive ability from incorporation of sequencing data into the modeling pipeline. Although the project focuses on modeling and data from Alaska, its methods and conclusions will be more broadly applicable. Areas exhibiting similar population structure of an urban/suburban core and smaller, largely rural regions with well-defined interconnections, are common in less developed countries, but they may lack sufficient data for effective modeling. Insights into disease dynamics arising from this structure will also improve understanding of aspects of epidemic progression in regions with more diffuse interconnections.

项目摘要 流行病传播的有效建模既依赖于最重要的数学描述 疾病传播和进展方面，以及从数据中估计参数。基因的收集 在整个COVID-19大流行期间，SARS-CoV-2样本的序列产生了广泛的新数据， 以补充传统的公共卫生资源。阿拉斯加的地理人口结构， 其特征是具有明确相互联系的亚群， 开发新的模型和参数估计方法。此外，除了地理异质性， 大流行的进展涉及冠状病毒变体流行率的变化以及缓解 策略，因此参数也具有时间依赖性。 该项目通过三个主要方面利用冠状病毒大流行的公共卫生和测序数据 建模推力。一个变异传播的统计模型，它同时捕捉地理结构和变化 变异比例将被开发并应用于阿拉斯加基因组数据集。传输估计 将采用动态方法调查区域内和区域间的发病率。这包括发展 一种新的统计方法，用于快速估计随时间变化的参数， 努力研究亚群之间的传播模式。最后，一个传统的房室模型， 将开发阿拉斯加的COVID-19，它捕获了影响疾病动态的关键地理特征 在状态虽然最初使用医院发病率和检测结果等公共卫生数据进行拟合， 然后将进行修改，以更好地反映从前两个重点出现的见解和估计。目标是 通过将测序数据纳入建模，促进并展示改进的预测能力 渠道. 虽然该项目侧重于阿拉斯加的建模和数据，但其方法和结论将更多 广泛适用。人口结构相似的地区，城市/郊区核心和较小的，主要是 在欠发达国家，具有明确互联的农村地区很常见，但它们可能缺乏 为有效建模提供足够的数据。对这种结构产生的疾病动力学的见解也将得到改善 了解相互联系较为分散的区域的流行病进展情况。