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.

项目总结/摘要 在此竞争性修订申请中，我们建议扩大研究项目的范围 2 R 01 GM 111510 -05，在具体目标3中增加新的子目标。按照最初的表述，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。这项活动 现在变成了目标3a。新的子目标目标3b将侧重于对真空吸尘器的影响进行数据驱动的建模， 免疫逃避SARS-CoV-2。目标3b的建模将补充目标1和目标2， PyBNF的改进将广泛适用于流行病学建模者。目标3b涉及以下需要： 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还解决了监测天然和疫苗诱导的免疫力的减弱以及 新的SARS-CoV-2菌株能够逃避疫苗诱导的免疫力。这项工作将扩大我们的 最近发表的COVID-19预测工作，我们使用数学模型对特定地区进行预测， COVID-19疫情，以便对COVID-19病例检测做出准确的短期预测。本工作 专注于对大都市地区进行预测，这些地区是根据社会经济联系定义的， 恩塞。我们发现，大都市地区比各州更均匀地受到COVID-19的影响。最 到目前为止，预测的重点是进行州一级的预测，而不是对城市及其周边地区的预测。 环绕着大都市我们计划扩展现有的模型，以考虑到15个国家的疫苗接种情况。 美国人口最多的大都市统计区（MSAs）。在这些地区的新版本之后- 制定具体的模型，我们将开始每天使用贝叶斯推断更新模型参数化- 恩塞。每日更新对于保持预测准确性和修改模型以考虑 社交距离行为的改变。我们的日常推理将包括预测不确定性的量化- 联系，以便能够检测激增和有信心的快速反应。我们就是我们的模型结构- 作为我们预测的基础是一个确定性的房室模型，它扩展了经典的SEIR模型， 它由四个常微分方程（ODE）组成，分别用于敏感（S），暴露（E）， 感染的（I）和去除的（R）群体。我们的扩展模型考虑了a）从感染开始的可变时间 至症状发作，其为非指数分布; B）无症状个体的病毒脱落; c）轻微和严重形式的有症状疾病; d）通过检测和接触者追踪进行检疫;以及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 模型进一步考虑疫苗接种，包括需要加强注射的疫苗和接种所需的时间。 疫苗诱导免疫的发展。我们还将开发模型，使具有免疫力的人- 随着时间的推移，逐渐容易受到目前流行的SARS-CoV-2变体和模型的影响， 免疫逃避变异体的出现。