RAPID: The Role of Testing in COVID-19 Outbreak Control

2019年冠状病毒病(新冠肺炎)的全球大流行在经济、生计以及最重要的生命方面造成了惊人的损失。与任何新出现的疾病一样，有关传播和免疫等重大疾病特征的许多不确定性仍然存在。由于关于疾病本身的信息不完善和不完整，预测疾病传播和干预措施的影响是一项挑战。此外，准确计算疫情需要了解病例数量，这就需要快速和准确的检测。然而，测试的当前使用和速度在不同地点有很大差异。虽然仅靠检测不能阻止传播，但广泛的检测可以提供更丰富的信息，了解疫情的进展情况，特别是像新冠肺炎这样具有隐藏传播的疾病。此外，了解个人的疾病状况有助于更好地遵守严格的隔离和社会距离措施，以遏制传播。虽然许多模型考虑了干预措施在平坦化曲线方面的作用，但该项目将解决新冠肺炎建模中的一个关键差距：测试战略对评估和干预持续爆发的能力的影响。该项目将开发新的SARS-CoV-2传播数学模型，以及使用测试的干预策略。SARS-CoV-2是新冠肺炎的起因。两名研究生将参与这项研究。该项目将开发新冠肺炎爆发的区隔模型，将检测与其他非药物干预措施如社会距离的使用结合起来。测试能力、准确性和延迟将被内置到这些模型中，以非常类似于实施中的真实世界挑战，这些挑战将因地点而异。PI将分析和模拟这些确定性模型，以扩大对成功减缓疫情的测试要求的理解。新模型的结果将通过类似结构的随机模型和实际数据的模拟来验证。结果将通过一个公众可访问的仪表盘公布，在那里可以输入和审查关于当地测试程序的信息，以确定适合当地的战略。这个可公开访问的仪表盘将帮助政策制定者和决策者在当地测试能力的背景下制定政策，以最好地减缓新冠肺炎的传播。此外，这项工作将提供一个框架，为未来新出现的流行病的干预测试和评估制定优先事项。该奖项是与MPS的多学科活动办公室(OMA)计划共同资助的。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The global pandemic of the 2019 coronavirus disease (COVID-19) has taken a staggering toll in terms of economies, livelihoods, and, most importantly, lives. As with any emerging disease, many uncertainties remain about significant disease characteristics such as transmission and immunity. Predicting disease spread and the impacts of interventions is a challenge with imperfect and incomplete information about the disease itself. Moreover, an accurate accounting of the outbreak requires knowledge about the number of cases, which necessitates fast and accurate testing. However, the current use and speed of testing varies significantly across different locations. While testing alone will not stop transmission, widespread testing gives richer information on how an outbreak is progressing, particularly with a disease such as COVID-19 with hidden transmission. Furthermore, knowledge of person’s disease status leads to better adherence to the strict isolation and social distancing measures used to curb transmission. While many models consider the role of interventions in flattening the curve, this project will address a key gap in modeling of COVID-19: the impact of testing strategies on the ability to assess and intervene against the ongoing outbreak. This project will develop new mathematical models of transmission of SARS-CoV-2, the cause of COVID-19 along with intervention strategies using testing. Two graduate students will be involved in the research.This project will develop compartmental models of the COVID-19 outbreak incorporating the use of testing in conjunction with other non-pharmaceutical interventions such as social distancing. Testing capacity, accuracy, and delays will be built into these models to closely resemble real-world challenges in implementation, which will differ by location. The PI will analyze and simulate these deterministic models to expand understanding of the requirements of testing for successful slowing of the outbreak. The results from the novel models will be validated with simulations from similarly structured stochastic models and actual data. Results will be presented through a publicly accessible dashboard where information on local testing procedures can be entered and examined to determine locally appropriate strategies. This publicly accessible dashboard will help policy- and decision-makers develop policies in the context of local testing capacity to best slow the spread of COVID-19. In addition, this work will provide a framework for developing priorities for testing and evaluation of intervention in future emerging epidemics.This award is co-funded with the Office of Multidisciplinary Activities (OMA) program of MPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.