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.

项目概要/摘要 在本次竞争性修订申请中，我们建议扩大研究项目的范围 2R01GM111510-05，在具体目标 3 中添加一个新的子目标。按照最初制定的那样，目标 3 的目标是 将 PyBioNetFit (PyBNF) 的新功能应用于免疫受体信号传导的建模研究。本次活动 现在变成了目标 3a。新的子目标 Aim 3b 将重点关注真空影响的数据驱动建模 炎症和逃避免疫的 SARS-CoV-2。 Aim 3b 的建模将通过驱动来补充 Aims 1 和 2 PyBNF 的改进将对流行病学建模者广泛有用。目标 3b 解决了以下需求： 态势感知，即监测严重 COVID-19 发病率新激增迹象的能力。目的 3b 还解决了监测自然免疫力和疫苗诱导免疫力减弱以及新冠病毒出现的需要 能够逃避疫苗诱导免疫的 SARS-CoV-2 新毒株。这项工作将扩展我们的 最近发布了 COVID-19 预测工作，其中我们使用了针对特定区域的数学模型 COVID-19 流行病对 COVID-19 病例检测做出准确的短期预测。在这项工作中，我们 重点是对大都市地区进行预测，这些地区是根据社会经济一致性来定义的 恩斯。我们发现，大都市地区受 COVID-19 影响的程度比各州更为普遍。最多 迄今为止的预测主要侧重于州级预测与城市及其周边地区的预测 环绕大都市区。我们计划扩展现有模型以考虑 15 年的疫苗接种问题 美国人口最多的大都市统计区 (MSA)。这些区域的新版本之后- 具体模型制定后，我们将开始使用贝叶斯推断每天更新模型参数化 恩斯。每日更新对于保持预测准确性和修改模型以适应情况非常重要 改变社交距离行为。我们的日常推论将包括预测不确定性的量化 关系，以便能够检测浪涌并进行可靠的快速响应。我们使用的模型结构- 作为我们预测的基础的是确定性分区模型，它扩展了经典的 SEIR 模型， 它由四个常微分方程 (ODE) 组成，用于敏感 (S)、暴露 (E)、 感染 (I) 和移除 (R) 种群。我们的扩展模型考虑了 a) 感染后的可变时间 症状发作，呈非指数分布； b) 无症状个体传播病毒； c) 轻度和重度症状性疾病； d) 由检测和接触者追踪驱动的隔离；和 e) 广泛实施随时间变化的社交距离措施。在此，我们建议延长 模型进一步考虑了疫苗接种，包括需要加强注射的疫苗以及注射所需的时间 疫苗诱导免疫力的发展。我们还将开发具有免疫力的人的模型 随着时间的推移，对当前流行的 SARS-CoV-2 变体和解释模型逐渐敏感 逃避免疫变异的出现。