Hardening Software for Rule-based models-Competitive Revision

基于规则的模型的强化软件 - 竞争性修订

• 批准号: 10382135
• 负责人: William S Hlavacek
• 金额: $ 6.42万
• 依托单位: NORTHERN ARIZONA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382135
• 关键词: 2019-nCoV; Address; Advanced Development; Algorithms; Area; Automobile Driving; Awareness; Bayesian Analysis; Behavior; Biological Models; COVID-19; COVID-19 pandemic; Chemicals; Cities; Collaborations; Complement; Computer software; Contact Tracing; Data; Derivation procedure; Detection; Development; Differential Equation; Disease; E-learning; Epidemiology; Event; Evolution; Formulation; Goals; Heterogeneity; Immunity; Immunologic Memory; Incidence; Individual; Infection; Laboratories; Language; Likelihood Functions; Markov Chains; Markov chain Monte Carlo methodology; Measures; Membrane; Methods; Modeling; Monitor; Occupations; Pattern; Performance; Persons; Phosphorylation; Population; Property; Publishing; Pythons; Quarantine; Research Project Grants; SARS-CoV-2 transmission; SARS-CoV-2 variant; Sampling; Signal Transduction; Site; Social Distance; Specific qualifier value; Structural Models; Study models; Symptoms; System; Systems Biology; Testing; Time; Training; Uncertainty; United States; Update; Vaccination; Vaccines; Variant; Virus Shedding; Work; Writing; base; computer cluster; computing resources; cost; curve fitting; data streams; design; epidemiological model; improved; mathematical model; metropolitan; operation; parallel computer; particle; population based; predictive modeling; receptor; recruit; response; severe COVID-19; simulation; simulation software; socioeconomics; software development; tool; transmission process; vaccine development; vaccine-induced immunity

PROJECT SUMMARY/ABSTRACT In this competitive revision application, we are proposing to expand the scope of Research Project 2R01GM111510-05 by adding a new sub-aim to Specific Aim 3. As originally formulated, the goal of Aim 3 was to apply new features of PyBioNetFit (PyBNF) in modeling studies of immunoreceptor signaling. This activity now becomes Aim 3a. The new sub-aim, Aim 3b, will be focused on data-driven modeling of the effects of vac- cination and immunity-evading SARS-CoV-2. The modeling of Aim 3b will complement Aims 1 and 2 by driving improvements of PyBNF that will be broadly useful for epidemiological modelers. Aim 3b addresses a need for situational awareness, i.e., an ability to monitor for signs of new surges in incidence of severe COVID-19. Aim 3b also addresses a need to monitor for waning of natural and vaccine-induced immunity and emergence of new strains of SARS-CoV-2 that are capable of evading vaccine-induced immunity. This work will extend our recently published COVID-19 forecasting efforts in which we used mathematical models for region-specific COVID-19 epidemics to make accurate short-term predictions of COVID-19 case detection. In this work, we focused on making predictions for metropolitan areas, which are defined on the basis of socioeconomic coher- ence. We have found that metropolitan areas are more uniformly impacted by COVID-19 than states. Most forecasting to date has focused on making state-level predictions vs. predictions for cities and their sur- rounding metropolitan areas. We plan to extend our existing models to account for vaccination in the 15 most populous metropolitan statistical areas (MSAs) in the United States. After new versions of these region- specific models are formulated, we will begin to update model parameterizations daily using Bayesian infer- ence. Daily updates are important for maintaining prediction accuracy and for modifying the models to account for changes in social-distancing behaviors. Our daily inferences will include quantification of forecast uncertain- ties, so as to allow for detection of surges and confident rapid responses. The model structure that we are us- ing as the basis for our forecasts is a deterministic compartmental model that extends the classic SEIR model, which consists of four ordinary differential equations (ODEs) for the dynamics of susceptible (S), exposed (E), infected (I), and removed (R) populations. Our extended model accounts for a) the variable time from infection to onset of symptoms, which is non-exponentially distributed; b) shedding of virus by asymptomatic individuals; c) mild and severe forms of symptomatic disease; d) quarantine driven by testing and contact tracing; and e) widespread implementation of time-varying social-distancing measures. Here, we are proposing to extend the model further to account for vaccination, including vaccines that require booster shots and the time required for development of vaccine-induced immunity. We will also develop models in which persons with immunity be- come susceptible gradually over time to currently circulating variants of SARS-CoV-2 and models that account for emergence of immunity-evading variants.

项目摘要/摘要 在此竞争性修订应用中，我们建议扩大研究范围 2R01GM111510-05通过将新的子AIM添加到特定目标3中。原始提出，目标3的目标是 在免疫受体信号传导的建模研究中应用PybionetFit（Pybnf）的新特征。这项活动 现在成为目标3A。新的子AIM AIM 3B将集中于数据驱动的vaction-aim-aim。 cination和免疫蒸发的SARS-COV-2。 AIM 3B的建模将通过开车来补充目标1和2 Pybnf的改进将对流行病学建模者广泛有用。 AIM 3B解决了需求 情境意识，即，有能力监测严重的Covid-19发病率的新潮流迹象。目的 3B还解决了监测自然和疫苗诱导的免疫力和出现的需求 能够逃避疫苗诱导的免疫力的SARS-COV-2的新菌株。这项工作将扩大我们的 最近出版的Covid-19预测工作，我们在其中使用数学模型来特定地区 COVID-19的流行病是对COVID-19病例检测的准确短期预测。在这项工作中，我们 专注于对大都市地区进行预测，这些预测是根据社会经济相关定义的 ence。我们发现，大都市地区比州更统一地受到了Covid-19的影响。最多 迄今为止的预测集中在做出州级预测与城市的预测及其预测 大都会区。我们计划扩展现有模型以说明15 美国大多数人口的大都市统计区（MSA）。在这些区域的新版本之后 - 制定了特定模型，我们将使用贝叶斯推论每天开始每天更新模型参数化。 ence。每日更新对于维持预测准确性和修改模型的说明很重要 用于改变社会竞争行为的变化。我们的每日推论将包括量化预测不确定 - 联系，以便发现潮流和自信的快速响应。我们是我们的模型结构 作为我们预测的基础，是一个确定性的隔室模型，扩展了经典的SEIR模型， 由四个普通微分方程（OD）组成，用于易感的动力学，暴露（e）， 感染（i），并删除（R）种群。我们的扩展模型占A）感染的变化时间 为了发作，症状是非指数分布的； b）无症状个体脱落病毒； c）轻度和严重的有症状疾病； d）由测试和接触跟踪驱动的隔离；和e） 广泛实施时变的社会统治措施。在这里，我们提议扩展 进一步考虑疫苗接种的模型，包括需要加强射击的疫苗和 疫苗诱导的免疫力的发展。我们还将开发模型，其中具有免疫力的人 - 随着时间的流逝，逐渐易于易受感染的SARS-COV-2和型号 为了出现免疫效应变体。