Bandyopadhyay Arnab, Schips Marta, Mitra Tanmay, Khailaie Sahamoddin, Binder Sebastian C, Meyer-Hermann Michael
Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany.
Commun Med (Lond). 2022 Jun 27;2:75. doi: 10.1038/s43856-022-00139-y. eCollection 2022.
During the first wave of COVID-19, hospital and intensive care unit beds got overwhelmed in Italy leading to an increased death burden. Based on data from Italian regions, we disentangled the impact of various factors contributing to the bottleneck situation of healthcare facilities, not well addressed in classical SEIR-like models. A particular emphasis was set on the undetected fraction (dark figure), on the dynamically changing hospital capacity, and on different testing, contact tracing, quarantine strategies.
We first estimated the dark figure for different Italian regions. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread, the model was optimized to fit data (infected, hospitalized, ICU, dead) published by the Italian Civil Protection.
We show that testing influenced the infection dynamics by isolation of newly detected cases and subsequent interruption of infection chains. The time-varying reproduction number ( ) in high testing regions decreased to <1 earlier compared to the low testing regions. While an early test and isolate (TI) scenario resulted in up to ~31% peak reduction of hospital occupancy, the late TI scenario resulted in an overwhelmed healthcare system.
An early TI strategy would have decreased the overall hospital usage drastically and, hence, death toll (∼34% reduction in Lombardia) and could have mitigated the lack of healthcare facilities in the course of the pandemic, but it would not have kept the hospitalization amount within the pre-pandemic hospital limit.
在新冠疫情第一波期间，意大利的医院和重症监护病房床位不堪重负，导致死亡负担加重。基于意大利各地区的数据，我们剖析了导致医疗设施瓶颈状况的各种因素的影响，而经典的类似易感-暴露-感染-康复（SEIR）模型并未很好地解决这些问题。特别强调了未检测到的部分（隐匿数字）、动态变化的医院容量以及不同的检测、接触者追踪和隔离策略。
我们首先估算了意大利不同地区的隐匿数字。利用文献中的参数估计值，或者通过对新冠疫情传播初期阶段进行拟合得出的参数，对模型进行优化，以拟合意大利民防部门公布的数据（感染、住院、重症监护、死亡）。
我们表明，检测通过隔离新发现的病例以及随后中断感染链，影响了感染动态。与低检测率地区相比，高检测率地区的随时间变化的再生数（ ）更早降至<1。虽然早期检测并隔离（TI）方案可使医院占用峰值降低约31%，但晚期TI方案导致医疗系统不堪重负。
早期TI策略本可大幅降低整体医院使用率，从而降低死亡人数（伦巴第大区降低约34%），并可缓解疫情期间医疗设施短缺的问题，但无法将住院人数控制在疫情前的医院容量范围内。
