Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation

Popper, Nikolas; Zechmeister, Melanie; Brunmeir, Dominik; Rippinger, Claire; Weibrecht, Nadine; Urach, Christoph; Bicher, Martin; Schneckenreither, GÃ1⁄4nter; Rauber, Andreas

doi:10.1101/2020.11.07.20227462

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The following section presents a demonstration of such an approach, which uses an agentbased model (ABM) constructed in a particular fashion, and a nontraditional conception of model "parameter space" that represents an initial approximation of a means of characterizing the latent space of unrepresented interactions for a given model. Note that this method of using ABMs, which addresses the generative hierarchical nature of SMT, is distinct from those approaches that use ABMs to generate virtual populations for epidemiological studies, where the trajectories of internal states of the agents is not the primary focus (Popper et al, 2020;Bissett et al, 2021;Truszkowska et al, 2021).…”

Section: Translating Expansiveness Of Representation (As Per the Maximalmentioning

confidence: 99%

Generating synthetic multidimensional molecular time series data for machine learning: considerations

An¹,

Cockrell²

2023

Front. Syst. Biol.

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The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Maximal Entropy Principle. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization.

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Section: Translating Expansiveness Of Representation (As Per the Maximalmentioning

confidence: 99%

Generating synthetic multidimensional molecular time series data for machine learning: considerations

An¹,

Cockrell²

2023

Front. Syst. Biol.

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“…researchers tried to see how infected patients spread in a target region [1], [2], [3], [4], [5], [6]. To observe such an increase of patients in a specific region or area using social simulations, researchers need a synthetic population with attributes of each resident and household composition in the target area or region.…”

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confidence: 99%

Workplace Assignment to Workers in Synthetic Populations in Japan

Murata

Iwase

Harada

2023

IEEE Trans. Comput. Soc. Syst.

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In this article, we assign workplace attributes to each worker in each household in a synthetic population using multiple censuses conducted in Japan. The synthetic population is a set of artificial individual attributes for each resident that is synthesized according to census data. We have synthesized a set of the synthetic populations of Japan. We assign a workplace attribute to each worker to estimate daytime population distribution and develop activity-based models in agent-based or microsimulations. Although statistical information in a residential area or a working place is released by the government and some individual moving data are released by cellphone companies, it is hard to collect the information with home and workplace location of a worker with their family and working information. We employ origin-destination-industry (ODI) statistics to estimate workplaces for workers. Since some attributes in ODI statistics are not available for privacy reasons, we propose a workplace assignment method for all cities, towns, and villages using restricted ODI and OD statistics in Japan. We show how much difference there are between the number of workers using the complete ODI statistics and the number of workers by the proposed workplace assignment method. We show that 88.2% of workers in a city in Japan are assigned to correct cities as workplaces by our proposed method. We also show several maps of daytime population distributions by our proposed method. Synthetic populations with workplace attributes enable real-scale social simulations to design transport or business systems in times of peace or to estimate victims and plan recoveries in times of emergency, such as disasters or pandemics.

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