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 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 Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Harnessing Big Data for Systems Pharmacology

2016 ◽  
Author(s):  
Lei Xie ◽  
Eli J. Draizen ◽  
Philip E. Bourne
Keyword(s):  
Big Data ◽  
Biological Networks ◽  
Drug Response ◽  
Conformational Dynamics ◽  
Model Management ◽  
Data Driven ◽  
Systems Pharmacology ◽  
Drug Actions ◽  
Data Driven Modeling ◽  
Big Data Technology

AbstractSystems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

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 Gelatinous Zooplankton‐Mediated Carbon Flows in the Global Oceans: A Data‐Driven Modeling Study

2020 ◽  
Vol 34 (9) ◽  
Author(s):  
Jessica Y. Luo ◽  
Robert H. Condon ◽  
Charles A. Stock ◽  
Carlos M. Duarte ◽  
Cathy H. Lucas ◽  
...  
Keyword(s):  
Gelatinous Zooplankton ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Global Oceans ◽  
Modeling Study

scholarly journals Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lorenzo Mari ◽  
Marino Gatto ◽  
Manuela Ciddio ◽  
Elhadji D. Dia ◽  
Susanne H. Sokolow ◽  
...  
Keyword(s):  
Big Data ◽  
Data Driven ◽  
Data Driven Modeling
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