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 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 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

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 Updated projections for COVID-19 omicron wave in Florida

2022 ◽  
Author(s):  
Thomas J. Hladish ◽  
Alexander N. Pillai ◽  
Ira M. Longini
Keyword(s):  
Simulation Model ◽  
Discrete Time ◽  
Explicit Representation ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Delta Wave ◽  
Detailed Simulation

In this report, we use a detailed simulation model to assess and project the COVID-19 epidemic in Florida. The model is a data-driven, stochastic, discrete-time, agent based model with an explicit representation of people and places. Using the model, we find that the omicron variant wave in Florida is likely to cause many more infections than occurred during the delta variant wave. Due to testing limitations and often mild symptoms, however, we anticipate that omicron infections will be underreported compared to delta. We project that reported cases of COVID-19 will continue to grow significantly and peak in early January 2022, and that the number of reported COVID-19 deaths due to omicron may be 1/3 of the total caused by the delta wave.

scholarly journals An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0161131 ◽  
Author(s):  
Zhenzhen Shi ◽  
Stephen K. Chapes ◽  
David Ben-Arieh ◽  
Chih-Hang Wu
Keyword(s):  
Experimental Data ◽  
Inflammatory Response ◽  
Computational Study ◽  
Large Set ◽  
Agent Based Model ◽  
Agent Based
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