Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google’s Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.
Skip Nav Destination
Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong
Article navigation
October 2021
Research Article|
October 20 2021
Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong
Hanchu Zhou
;
Hanchu Zhou
a)
1
School of Data Science, City University of Hong Kong
, Hong Kong, China
Search for other works by this author on:
Qingpeng Zhang
;
Qingpeng Zhang
b)
1
School of Data Science, City University of Hong Kong
, Hong Kong, China
b)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Zhidong Cao;
Zhidong Cao
2
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
, Beijing, China
Search for other works by this author on:
Helai Huang;
Helai Huang
3
School of Traffic and Transportation Engineering, Central South University
, Changsha, China
Search for other works by this author on:
Daniel Dajun Zeng
Daniel Dajun Zeng
c)
2
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
, Beijing, China
Search for other works by this author on:
a)
Also at: School of Traffic and Transportation Engineering, Central South University, Changsha, China.
b)Author to whom correspondence should be addressed: [email protected]
c)
Electronic mail: [email protected]
Chaos 31, 101104 (2021)
Article history
Received:
August 08 2021
Accepted:
September 07 2021
Citation
Hanchu Zhou, Qingpeng Zhang, Zhidong Cao, Helai Huang, Daniel Dajun Zeng; Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong. Chaos 1 October 2021; 31 (10): 101104. https://doi.org/10.1063/5.0066086
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Response to music on the nonlinear dynamics of human fetal heart rate fluctuations: A recurrence plot analysis
José Javier Reyes-Lagos, Hugo Mendieta-Zerón, et al.
Reliable detection of directional couplings using cross-vector measures
Martin Brešar, Ralph G. Andrzejak, et al.
Synchronization in spiking neural networks with short and long connections and time delays
Lionel Kusch, Martin Breyton, et al.
Related Content
Travel restrictions during pandemics: A useful strategy?
Chaos (November 2020)
Applying interval stability concept to empirical model of middle Pleistocene transition
Chaos (February 2022)
Generation of surrogate event sequences via joint distribution of successive inter-event intervals
Chaos (December 2019)