评估科罗拉多矿业学院的 COVID-19 监测检测策略：一种随机建模方法
Evaluating COVID-19 Surveillance Testing Strategies at Colorado School of Mines: A Stochastic Modeling Approach
AI总结：
提出面向科罗拉多矿业学院校园的 COVID-19 随机 SEIR 传播模型，利用 ABC 拟合病例数据并评估不同监测检测策略。
摘要：
本研究介绍了一个专为科罗拉多矿业学院校园定制的 COVID-19 传播随机模型，并评估了大学环境中的监测检测策略。我们的模型通过随机转移增强传统 SEIR 框架，考虑了住宿学院疾病传播的独特特征，包括用于检测、检疫和隔离的特定状态。研究采用近似贝叶斯计算（ABC）方法进行参数估计，克服了随机模型固有的复杂性，使模型能够准确拟合校园病例数据。随后，我们在估计参数下研究该模型，以评估可在大学校园实施的不同检测政策的有效性。该框架不仅增进了对 Mines 校园 COVID-19 动态的理解，也为类似环境提供了蓝图，为制定应对传染病的知情策略提供洞见。
英文摘要：
This study introduces a stochastic model of COVID-19 transmission tailored to the Colorado School of Mines campus and evaluates surveillance testing strategies within a university context. Enhancing the conventional SEIR framework with stochastic transitions, our model accounts for the unique characteristics of disease spread in a residential college, including specific states for testing, quarantine, and isolation. Employing an approximate Bayesian computation (ABC) method for parameter estimation, we navigate the complexities inherent in stochastic models, enabling an accurate fit of the model with the campus case data. We then studied our model under the estimated parameters to evaluate the efficacy of different testing policies that could be implemented on a university campus. This framework not only advances understanding of COVID-19 dynamics on the Mines campus but serves as a blueprint for comparable settings, providing insights for informed strategies against infectious diseases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
