Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

Abstract

BackgroundThe nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the population 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 the data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs.MethodsWe develop a data-driven agent-based model for 7.55 million 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 is split into 4,905 500m×500m 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 proposed 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 efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive Monte Carlo simulations.FindingsWithout NPIs, we estimate that there are 128,711 total infections (IQR 23,511-70,310) by the end of the 80-day simulation. The proposed targeted intervention averts 95.85% and 94.13% of baseline infections with only 100 (2.04%) and 50 (1.02%) grids being quarantined, respectively. Mild social distancing without testing results in 16,503 total cases (87.18% infections averted), rapid implementation of full lockdown and testing measures (such as the control measure in Mainland China) performs the best, with only 805 infections (99.37% infections averted). Testing-and-quarantining 10%, 20%, 50% of all symptomatic cases with 24-hour/48-hour avert 89.92%/ 87.78%, 95.47%/ 92.42%, and 97.93%/ 95.61% infections, respectively.InterpretationBig data-driven mobility modeling 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.

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…This social network might also contain layers of households, schools, workplaces or community and disease properties (Aleta et al, 2020; Alqithami, 2021; Altun et al, 2021; Álvarez‐Pomar & Rojas‐Galeano, 2021; Kamerlin & Kasson; 2020; Panovska‐Griffiths et al, 2020; Rockett et al, 2020; Yang et al, 2020). To lend a more realistic context and consequently provide more interventions, six papers also incorporated GIS‐enhanced geospatial data into the simulation platform (Agrawal et al, 2020; Alvarez Castro & Ford, 2021; de Vries & Rambabu, 2021; Gharakhanlou & Hooshangi, 2020; Mahmood et al, 2020; Zhang et al, 2021) and five papers integrated human mobility data (Aleta et al, 2020; Kishore et al, 2021; Sewell & Miller, 2020; Wei et al, 2021; Zhou, Zhang, et al, 2021). To demonstrate the dynamics of the spread through interactions, 158 papers simulated the disease transmission via regular or modified SIR‐ or SEIR‐based ABMs, where the labelled states of individuals are Susceptible, Infected, Recovered, Dead (SIRD) (Alsaeed et al, 2020; Mahmood et al, 2020) or Susceptible, Exposed, Infected, Recovered, Dead (SEIRD) (Benneyan et al, 2021; Gharakhanlou & Hooshangi, 2020).…”