Data-driven mobility models for COVID-19 simulation

Pesavento, John; Chen, Andy; Yu, Rayan; Kim, Joon‐Seok; Kavak, Hamdi; Anderson, Taylor; Züfle, Andreas

doi:10.1145/3423455.3430305

Cited by 23 publications

(5 citation statements)

References 38 publications

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“…Many ABMs for COVID-19 are in the scale of several hundred agents [78][79][80][81][82][83] to tens of thousands of agents [37,84,85]. Fewer studies have over 100,000 agents [86], and only a paucity of studies has a number of agents that is about equal (eg, the model of Hoertel and colleagues [87] used 500,000 agents) or greater than (eg, one million agents in a February 2021 simulation of Bogota) in this study [38,87,88]. Due to this distribution of agent population across studies, the qualifier of large is applied as we get to the scale of 500,000 or more agents [38].…”

Section: Related Work: the Scale Of Agent-based Models For Covid-19mentioning

confidence: 99%

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Li¹,

Giabbanelli²

2021

JMIR Med Inform

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Background In 2020, COVID-19 has claimed more than 300,000 deaths in the United States alone. Although nonpharmaceutical interventions were implemented by federal and state governments in the United States, these efforts have failed to contain the virus. Following the Food and Drug Administration's approval of two COVID-19 vaccines, however, the hope for the return to normalcy has been renewed. This hope rests on an unprecedented nationwide vaccine campaign, which faces many logistical challenges and is also contingent on several factors whose values are currently unknown. Objective We study the effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans. To characterize the possible outcomes most accurately, we also account for the interactions between nonpharmaceutical interventions and vaccines through 6 scenarios that capture a range of possible impacts from nonpharmaceutical interventions. Methods We used large-scale, cloud-based, agent-based simulations by implementing the vaccination campaign using COVASIM, an open-source agent-based model for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity and a multiplicity of contact networks. Several modifications to the parameters and simulation logic were made to better align the model with current evidence. We chose 6 nonpharmaceutical intervention scenarios and applied the vaccination intervention following both the plan proposed by Operation Warp Speed (former Trump administration) and the plan of one million vaccines per day, proposed by the Biden administration. We accounted for unknowns in vaccine efficacies and levels of population compliance by varying both parameters. For each experiment, the cumulative infection growth was fitted to a logistic growth model, and the carrying capacities and the growth rates were recorded. Results For both vaccination plans and all nonpharmaceutical intervention scenarios, the presence of the vaccine intervention considerably lowers the total number of infections when life returns to normal, even when the population compliance to vaccines is as low as 20%. We noted an unintended consequence; given the vaccine availability estimates under both federal plans and the focus on vaccinating individuals by age categories, a significant reduction in nonpharmaceutical interventions results in a counterintuitive situation in which higher vaccine compliance then leads to more total infections. Conclusions Although potent, vaccines alone cannot effectively end the pandemic given the current availability estimates and the adopted vaccination strategy. Nonpharmaceutical interventions need to continue and be enforced to ensure high compliance so that the rate of immunity established by vaccination outpaces that induced by infections.

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Section: Related Work: the Scale Of Agent-based Models For Covid-19mentioning

confidence: 99%

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Li¹,

Giabbanelli²

2021

JMIR Med Inform

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“…5 See (Li, Giabbanelli, et al 2021) and (Giabbanelli and Li 2020) 6 See (Badham et al 2021) and (Castellani and Caiado 2020) 7 See (Pesavento et al 2020)…”

Section: Addressing Data Limitation Across Stages Of the Modeling Processmentioning

confidence: 99%

Opportunities and Challenges in Developing COVID-19 Simulation Models: Lessons from Six Funded Projects

Giabbanelli

Badham

Castellani

et al. 2021

2021 Annual Modeling and Simulation Conference (ANNSIM)

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The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to guide decision makers. However, a year into the COVID-19 pandemic, the computational science literature lacks a proper internal exploration of the modeling journey of researchers around the world, including how they responded to the shared challenges our community faced such as data limitations, model fitting and working with public stakeholders. The current paper is a detailed examination of the internal processes of six research teams, which were funded in several countries to model COVID-19. Each team was asked to reflect on the research question and how they solved their respective modeling challenges, as well as how, looking back, they would do things differently.

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“…To tackle challenges posed by the climate crisis, cities require movement data to address smart traffic management [4,5], shift car use to climate-friendlier options [6], offer broader access to public transport [7], and steer ever-growing mobility options such as e-scooters [3], to name a few. The current pandemic further highlights the usefulness of such data as these form the basis for analyses [8,9] or simulations of pandemic progression [10].…”

Section: Introductionmentioning

confidence: 99%

Towards Standardized Mobility Reports with User-Level Privacy

Kapp¹,

Voigt²,

Mihaljević³

et al. 2022

Preprint

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The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures.There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a standardized mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.

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Data-driven mobility models for COVID-19 simulation

Cited by 23 publications

References 38 publications

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Opportunities and Challenges in Developing COVID-19 Simulation Models: Lessons from Six Funded Projects

Towards Standardized Mobility Reports with User-Level Privacy

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