An agent-based model to assess coronavirus disease 19 spread and health systems burden

Narassima, M.S.; Anbuudayasankar, S.P.; Jammy, Guru Rajesh; Sankarshana, AnanthaPadmanabhan; Pant, Rashmi; Choudhury, Lincoln; Yeldandi, Vijay; Singh, Shubham; John, Denny

doi:10.11591/ijece.v12i4.pp4118-4128

Cited by 3 publications

(2 citation statements)

References 24 publications

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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%

“…As a Japanese team, we distribute the Japanese synthetic population to researchers who are to conduct social simulations for regions in Japan. 1 The first method to synthesize populations based on statistics, called the synthetic reconstruction method (SR method), was proposed by Wilson and Pownall [14]. They reconstruct individual and household data from statistics with some actual samples using an iterative proportional fitting (IPF) procedure [15].…”

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confidence: 99%

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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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confidence: 99%

Workplace Assignment to Workers in Synthetic Populations in Japan

Murata

Iwase

Harada

2023

IEEE Trans. Comput. Soc. Syst.

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Data-driven prognosis of long COVID in patients using machine learning

Parvathy,

Subbanna,

Rao

et al. 2023

INTELLIGENT BIOTECHNOLOGIES OF NATURAL AND SYNTHETIC BIOLOGICALLY ACTIVE SUBSTANCES: XIV Narochanskie Readings

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A Survey of the Various Methods and Models Used to Detect Long COVID

Parvathy

Subbanna

Rao

et al. 2022

2022 6th International Conference on Computing, Communication, Control and Automation (ICCUBEA

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Long-COVID or post-COVID is a phenomenon where people who have recovered from the COVID-19, suffer persisting symptoms for more than 4 weeks after the confirmed case of COVID-19 and they can last for months. Approximately 20% of the people affected by this Coronavirus disease (COVID-19) are suffering from mid and long term effects known as the Long COVID and it can affect multiple organs in the body and this can lead to death. To date, different studies and researches have been undertaken to understand about the Long COVID and make robust estimates on the predicting factors, symptoms and also to assess the various long term effects on the patients affected by it. Based on the available research articles and the papers published in mainstream journals on Long COVID, this survey paper aims at analyzing various methods and Machine learning models used to detect and predict Long COVID, to help clinicians and researchers working on early diagnosis of Long COVID.

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An agent-based model to assess coronavirus disease 19 spread and health systems burden

Cited by 3 publications

References 24 publications

Workplace Assignment to Workers in Synthetic Populations in Japan

Workplace Assignment to Workers in Synthetic Populations in Japan

Data-driven prognosis of long COVID in patients using machine learning

A Survey of the Various Methods and Models Used to Detect Long COVID

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