Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong

Author:

Cheng Weibin12ORCID,Zhou Hanchu23ORCID,Ye Yang2ORCID,Chen Yifan2ORCID,Jing Fengshi4ORCID,Cao Zhidong56ORCID,Zeng Daniel Dajun56,Zhang Qingpeng278ORCID

Affiliation:

1. Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital 1 , Guangzhou 510317, China

2. School of Data Science, City University of Hong Kong 2 , Hong Kong 999077, China

3. School of Traffic and Transportation Engineering, Central South University 3 , Changsha 410075, China

4. UNC Project-China, UNC Global, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 4 , USA

5. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 5 , Beijing 100190, China

6. School of Artificial Intelligence, University of Chinese Academy of Sciences 6 , Beijing 100049, China

7. Shenzhen Research Institute, City University of Hong Kong, Shenzhen, 518057 7 , China

8. Department of Pharmacology and Pharmacy, The LKS Faculty of Medicine, The University of Hong Kong, Hong Kong 999077 8 , China

Abstract

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.

Funder

University Grants Committee

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

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