Hardening Software for Rule-based models-Competitive Revision

项目摘要/摘要 在这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中增加一个新的子目标。 将PyBioNetFit(PyBNF)的新特性应用于免疫受体信号传导的建模研究。本练习 现在成为目标3a。新的次级目标，目标3b，将重点放在以数据为驱动的VAc影响建模上。 免疫和免疫--逃避SARS-CoV-2。目标3b的建模将通过驱动来补充目标1和2 对PyBNF的改进，这将对流行病学建模人员广泛有用。目标3b满足了以下需求 情景感知，即监测严重新冠肺炎发病率新激增迹象的能力。目标 3B还涉及需要监测自然免疫和疫苗诱导的免疫减弱以及出现 能够逃避疫苗诱导免疫的新的SARS-CoV-2毒株。这项工作将扩展我们的 最近发布的新冠肺炎预测成果中，我们使用了针对特定地区的数学模型 对新冠肺炎疫情作出准确的短期预测，发现新冠肺炎病例。在这项工作中，我们 重点是对大都市区进行预测，这些预测是基于社会经济指标- 安斯。我们发现，与各州相比，大都市地区受到新冠肺炎影响的一致性更强。多数 到目前为止，预测的重点是对城市及其郊区进行州级预测与预测。 环绕大都会地区。我们计划扩展我们现有的模型，以考虑到15年的疫苗接种 美国人口最多的大都会统计区(MSA)。在这些区域的新版本之后- 制定了具体的模型，我们将开始每天更新模型参数，使用贝叶斯推断- 安斯。每日更新对于保持预测精度和修改模型以进行核算非常重要 对于社交疏远行为的改变。我们的日常推论将包括对不确定预测的量化- 平局，以便能够检测到激增和自信的快速反应。我们就是我们的模型结构-- 作为我们预测的基础，ING是一个确定性的划分模型，它扩展了经典的SEIR模型， 它由四个常微分方程组(常微分方程组)组成，分别描述易感(S)、暴露(E)、 感染(I)和移除(R)种群。我们的扩展模型考虑了a)感染的可变时间 到症状出现，非指数分布：b)无症状个体的病毒脱落； C)轻微和严重的症状性疾病；d)通过检测和接触者追踪进行隔离；以及e) 广泛实施时变的社会疏远措施。在这里，我们建议延长 进一步考虑疫苗接种的模型，包括需要加强注射的疫苗和所需时间 疫苗诱导免疫的发展。我们还将开发模式，其中有豁免权的人- 随着时间的推移逐渐对目前流行的SARS-CoV-2变种和解释 因为出现了逃避免疫的变种。

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.