Cheng Weibin, Zhou Hanchu, Ye Yang, Chen Yifan, Jing Fengshi, Cao Zhidong, Zeng Daniel Dajun, Zhang Qingpeng
Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou 510317, China.
School of Data Science, City University of Hong Kong, Hong Kong 999077, China.
Chaos. 2023 Jan;33(1):013124. doi: 10.1063/5.0123870.
The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking.
在实施针对新冠疫情的非药物干预措施(NPIs)期间,呼吸道传染病(RIDs)易感人群的积累可能会给未来RIDs的爆发带来更大风险。我们研究了取消针对新冠疫情的NPIs后RIDs卷土重来的时间和规模,并评估了对卫生系统的负担。我们提出了基于阈值的控制方法(TCM),以确定数据驱动的解决方案,通过在严重感染人数达到阈值时重新引入NPIs来维持卫生系统的弹性。取消针对新冠疫情的NPIs后,所有RIDs都会爆发,且高峰时间交错。如此大规模的RIDs患者卷土重来将给卫生系统不堪重负带来重大风险。采用严格的NPIs策略,由TCM启动的600例严重感染阈值可确保为所有住院的严重感染患者提供足够的病床。所提出的TCM确定了有效的动态NPIs,这有助于未来NPIs放宽政策的制定。