Department of Mathematics, Colgate University, Hamilton, NY, USA.
Center for Public Health Modeling and Response, Department of Public Health Sciences, Clemson University, 517 Edwards Hall, Clemson, SC, 29634, USA.
Sci Rep. 2024 Mar 27;14(1):7221. doi: 10.1038/s41598-024-57488-y.
Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.
在传染病爆发期间，机构有能力实施针对性更强的缓解措施。然而，由于局部模型输入参数的不确定性，宏观预测模型在指导机构决策方面效率低下。我们提出了一个机构级建模工具包，用于在整个新冠疫情期间为克莱姆森大学的预测、资源采购和分配以及政策实施提供信息。通过实时估计基于机构数据的疾病监测和流行病学措施，我们认为这种方法有助于最小化更广泛文献中提出的输入参数的不确定性，并提高预测准确性。我们通过克莱姆森大学和其他大学在奥密克戎 BA.1 和 BA.4/BA.5 变体激增期间的案例研究证明了这一点。我们工具包的输入参数易于适应未来卫生紧急情况下的其他机构环境。这种方法具有通过提高机构做出数据驱动决策的能力来改善公共卫生应对的潜力，这些决策更好地优先考虑其社区的健康和安全，同时最小化运营中断。
