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

2021 ◽  
Author(s):  
Hanchu Zhou ◽  
Qingpeng Zhang ◽  
Zhidong Cao ◽  
Helai Huang ◽  
Daniel Dajun Zeng
Keyword(s):  
Big Data ◽  
Hong Kong ◽  
Control Measure ◽  
Human Mobility ◽  
Local Economy ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Targeted Interventions ◽  
The City

AbstractBackgroundThe 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.

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

2021 ◽  
Vol 31 (10) ◽  
pp. 101104
Author(s):  
Hanchu Zhou ◽  
Qingpeng Zhang ◽  
Zhidong Cao ◽  
Helai Huang ◽  
Daniel Dajun Zeng
Keyword(s):  
Big Data ◽  
Hong Kong ◽  
Data Driven ◽  
Targeted Interventions ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208775 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

scholarly journals Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model

2010 ◽  
Vol 13 (3) ◽  
Author(s):  
Riccardo Boero ◽  
Giangiacomo Bravo ◽  
Marco Castellani ◽  
Flaminio Squazzoni
Keyword(s):  
Experimental Data ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Direct Simulation of the CoVid-19 epidemic

2021 ◽  
Author(s):  
Augusto Cabrera-Becerril ◽  
Pedro Miramontes ◽  
Raúl Peralta
Keyword(s):  
Simulation Model ◽  
Computational Models ◽  
Human Mobility ◽  
High Peak ◽  
Direct Simulation ◽  
Agent Based Model ◽  
Agent Based

AbstractWe introduce an agent-based model to simulate the epidemiological dynamics of COVID-19. Most computational models proposed to study this epidemic do no take into account human mobility. We present a direct simulation model where mobility plays a key role and propose as well four quarantine strategies. The results show that the no-quarantine strategy does lead to a high peak of contagions with no rebound. Quarantined strategies, for their part, show a re-emergence of the epidemic with smaller and softer peaks.

scholarly journals Viral Data

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172097100
Author(s):  
Agnieszka Leszczynski ◽  
Matthew Zook
Keyword(s):  
Big Data ◽  
Hong Kong ◽  
Data Analytics ◽  
Data Driven ◽  
Tracking Data ◽  
Conspiracy Theories ◽  
Viral Spread ◽  
Data Practices ◽  
Historical Moment

We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as granting objective access to truth and attendant practices of tracking, data analytics, algorithmic prediction, and data-driven targeting of individuals and communities. This collection of papers explores how data (and their absences) is figuring in the making of the discourses, lived realities, and systemic inequalities of the uneven impacts of the coronavirus pandemic.

scholarly journals Data-Driven Agent-Based Model of Intra-Urban Activities

2020 ◽  
Author(s):  
Shuhui Gong ◽  
John Cartlidge ◽  
Ruibin Bai ◽  
Yang Yue ◽  
Qingquan Li ◽  
...  
Keyword(s):  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Urban Activities
Sign in / Sign up

Export Citation Format

Share Document