School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA; Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA.
Ann Epidemiol. 2023 Jan;77:44-52. doi: 10.1016/j.annepidem.2022.10.013. Epub 2022 Nov 7.
Nursing homes and long-term care facilities have experienced severe outbreaks and elevated mortality rates of COVID-19. When available, vaccination at-scale has helped drive a rapid reduction in severe cases. However, vaccination coverage remains incomplete among residents and staff, such that additional mitigation and prevention strategies are needed to reduce the ongoing risk of transmission. One such strategy is that of "shield immunity", in which immune individuals modulate their contact rates and shield uninfected individuals from potentially risky interactions.
Here, we adapt shield immunity principles to a network context, by using computational models to evaluate how restructured interactions between staff and residents affect SARS-CoV-2 epidemic dynamics.
First, we identify a mitigation rewiring strategy that reassigns immune healthcare workers to infected residents, significantly reducing outbreak sizes given weekly testing and rewiring (48% reduction in the outbreak size). Second, we identify a preventative prewiring strategy in which susceptible healthcare workers are assigned to immunized residents. This preventative strategy reduces the risk and size of an outbreak via the inadvertent introduction of an infectious healthcare worker in a partially immunized population (44% reduction in the epidemic size). These mitigation levels derived from network-based interventions are similar to those derived from isolating infectious healthcare workers.
This modeling-based assessment of shield immunity provides further support for leveraging infection and immune status in network-based interventions to control and prevent the spread of COVID-19.
养老院和长期护理机构经历了 COVID-19 的严重爆发和死亡率上升。在有疫苗供应的情况下，大规模接种有助于迅速减少重症病例。然而，居民和工作人员的疫苗接种覆盖率仍然不完整，因此需要采取额外的缓解和预防策略，以降低持续传播的风险。其中一种策略是“盾牌免疫”，即免疫个体调节其接触率，并保护未感染者免受潜在危险的接触。
在这里，我们将盾牌免疫原理应用于网络环境中，通过使用计算模型来评估工作人员和居民之间的互动关系如何改变，从而影响 SARS-CoV-2 的流行动力学。
首先，我们确定了一种减轻疫情的重连策略，即重新分配免疫医疗工作者到感染居民，这在每周进行检测和重连的情况下，显著减少了疫情规模（疫情规模减少 48%）。其次，我们确定了一种预防性的预连策略，即易感的医疗工作者被分配给免疫的居民。这种预防策略通过在部分免疫人群中引入传染性医疗工作者，降低了疫情的风险和规模（疫情规模减少 44%）。这些基于网络干预的减轻措施与隔离传染性医疗工作者所带来的效果相似。
这项基于模型的盾牌免疫评估进一步支持利用感染和免疫状态进行网络干预，以控制和预防 COVID-19 的传播。
