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

2019冠状病毒病（COVID-19）的全球大流行对经济、生计以及最重要的生命造成了惊人的损失。与任何新出现的疾病一样，在传播和免疫等重大疾病特征方面仍存在许多不确定性。在有关疾病本身的信息不完善和不完整的情况下，预测疾病传播和干预措施的影响是一项挑战。此外，准确统计疫情需要了解病例数量，这就需要进行快速和准确的检测。然而，目前的使用和测试速度在不同的地点有很大的差异。虽然单靠检测无法阻止传播，但广泛的检测可以提供更丰富的信息，说明疫情的进展情况，特别是像COVID-19这样具有隐蔽传播的疾病。此外，了解个人的疾病状况有助于更好地遵守用于遏制传播的严格隔离和社交距离措施。虽然许多模型都考虑了干预措施在拉平曲线方面的作用，但该项目将解决COVID-19建模中的一个关键空白：测试策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，该模型是COVID-19沿着的原因，并使用测试进行干预策略。两名研究生将参与该研究。该项目将开发COVID-19爆发的房室模型，将检测与其他非药物干预措施（如社交距离）结合使用。测试能力、准确性和延迟将被内置到这些模型中，以接近实施中的现实挑战，这将因地点而异。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进行验证。结果将通过一个可公开访问的仪表板展示，可以输入和检查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板将帮助政策制定者和决策者在当地检测能力的背景下制定政策，以最好地减缓COVID-19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措施的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，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.