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JACIII Vol.25 No.6 pp. 931-943
doi: 10.20965/jaciii.2021.p0931
(2021)

Paper:

Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) Based on Multi-Agent Simulation

Yuto Omae*, Jun Toyotani*, Kazuyuki Hara*, Yasuhiro Gon**, and Hirotaka Takahashi***

*College of Industrial Technology, Nihon University
1-2-1 Izumi, Narashino, Chiba 275-8575, Japan

**Nihon University School of Medicine
30-1 Kami, Ooyaguchi, Itabashi, Tokyo 173-8610, Japan

***Research Center for Space Science, Advanced Research Laboratories, Tokyo City University
8-15-1 Todoroki, Setagaya, Tokyo 158-0082, Japan

Received:
October 13, 2020
Accepted:
July 15, 2021
Published:
November 20, 2021
Keywords:
COVID-19, Contact-Confirming Application, multi-agent simulation, SEIR model
Abstract

As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labour and Welfare developed “COVID-19 Contact-Confirming Application (COCOA),” which was released on June 19, 2020. By utilizing COCOA, users can know whether or not they had contact with infected persons. If those who had contact with infected individuals keep staying at home, they may not infect those outside. However, effectiveness decreasing the number of infected individuals depending on the app’s various usage parameters is not clear. If it is clear, we could set the objective value of the app’s usage parameters (e.g., the usage rate of the total populations) and call for installation of the app. Therefore, we develop a multi-agent simulator that can express COVID-19 spreading and usage of the apps, such as COCOA. In this study, we describe the simulator and the effectiveness of the app in various scenarios. The result obtained in this study supports those of previously conducted studies.

The relationship between the app parameters <i>p</i><sup>{1,2,3}</sup><sub>app</sub> and the number of total infected individuals (ppl) at the end of the 45 days simulations (in the case of <i>p</i><sup>3</sup><sub>app</sub> = 100%). The higher the number of infected individuals, the redder. DVP*: Decreasing value of going out probability

The relationship between the app parameters p{1,2,3}app and the number of total infected individuals (ppl) at the end of the 45 days simulations (in the case of p3app = 100%). The higher the number of infected individuals, the redder. DVP*: Decreasing value of going out probability

Cite this article as:
Y. Omae, J. Toyotani, K. Hara, Y. Gon, and H. Takahashi, “Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) Based on Multi-Agent Simulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.6, pp. 931-943, 2021.
Data files:
References
  1. [1] The Japanese government, “Basic policies for novel coronavirus disease control by the Government of Japan (Summary),” https://corona.go.jp/en/news/pdf/basic_policy_20200531.pdf [accessed August 19, 2020]
  2. [2] Ministry of Health, Labour and Welfare of the Japanese government, “Open data of the number of infected individuals in Japan,” https://www.mhlw.go.jp/content/pcr_positive_daily.csv [accessed August 19, 2020]
  3. [3] Ministry of Health, Labour and Welfare of the Japanese government, “Emergency response measures of COVID-19 spread,” https://www.mhlw.go.jp/content/10900000/000612097.pdf (In Japanese) [accessed August 19, 2020]
  4. [4] Ministry of Health, Labour and Welfare of the Japanese government, “Request to install the COVID-19 Contact-Confirming Application,” https://www.mhlw.go.jp/content/10900000/000647649.pdf [accessed August 19, 2020]
  5. [5] COVID-19 Infection Control Team of the Japanese government, “Contact Confirmation Application Privacy Policy,” https://www.mhlw.go.jp/stf/seisakunitsuite/english_pp_00032.html [accessed August 19, 2020]
  6. [6] Ministry of Health, Labour and Welfare of the Japanese government, “Occurring of subclinical pathogen carrier of COVID-19,” https://www.mhlw.go.jp/stf/newpage_09273.html (In Japanese) [accessed August 19, 2020]
  7. [7] C. Rothe et al., “Transmission of 2019-nCoV infection from an asymptomatic contact in Germany,” N. Engl. J. Med., Vol.382, No.10, pp. 970-971, 2020.
  8. [8] COVID-19 Infection Control Team of the Japanese government, “Contact-confirming application trends in each country,” https://cio.go.jp/sites/default/files/uploads/documents/techteam_20200508_02.pdf (In Japanese) [accessed August 19, 2020]
  9. [9] D. Chumachenko, V. Dobriak, M. Mazorchuk, I. Meniailov, and K. Bazilevych, “On agent-based approach to influenza and acute respiratory virus infection simulation,” The 14th Int. Conf. on Adv. Trends in Radioelecrtronics, Telecommun, and Comput. Eng. (TCSET), pp. 192-195, 2018.
  10. [10] F. Yang, Q. Yang, X. Liu, and P. Wang, “SIS evolutionary game model and multi-agent simulation of an infectious disease emergency,” Technol. and Health Care, Vol.23, No.s2, pp. S603-S613, 2015.
  11. [11] J. B. Dunham, “An agent-based spatially explicit epidemiological model in MASON,” J. Artif. Soc. Social Simul., Vol.9, No.1, 2005.
  12. [12] H. Hirose, “Pandemic simulations by MADE: A combination of multi-agent and differential equations, with Novel Influenza A (H1N1) Case,” Inf., Vol.16, No.7(B), pp. 5365-5389, 2013.
  13. [13] C. Hou et al., “The effectiveness of quarantine of Wuhan City against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis,” J. Med. Virology, Vol.92, pp. 841-848, doi: 10.1002/jmv.25827, 2020.
  14. [14] K. Chatterjee, K. Chatterjee, A. Kumar, and S. Shankar, “Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model,” Med. J. Armed Forces India, Vol.76, No.2, pp. 147-155, doi: 10.1016/j.mjafi.2020.03.022, 2020.
  15. [15] R. Hinch et al., “Effective configurations of a digital contact tracing app: A report to NHSX,” https://045.medsci.ox.ac.uk/files/files/report-effective-app-configurations.pdf [accessed August 19, 2020]
  16. [16] S. Takahashi, “State-of-the-art of social system research 3 –Methods of evaluation and analysis (1)– Resolutions of models and validation,” J. Soc. Instrum. and Control Engineers, Vol.52, No.7, pp. 582-587, 2013 (in Japanese).
  17. [17] Y. Omae, J. Toyotani, K. Hara, and H. Takahashi, “A prediction method for viral disease outbreak using a multi-agent simulation including capacity limitation for isolation wards and stay-at-home orders,” J. Jpn. Soc. Fuzzy Theory and Intell. Inform., Vol.32 No.6, pp. 998-1007, 2020 (in Japanese with English abstract).
  18. [18] Z. Yang et al., “Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions,” J. Thorac. Dis., Vol.12, No.3, pp. 165-174, doi: 10.21037/jtd.2020.02.64, 2020.
  19. [19] S. He, Y. Peng, and K. Sun, “SEIR modeling of the COVID-19 and its dynamics,” Nonlinear Dyn., Vol.101, pp. 1667-1680, doi: 10.1007/s11071-020-05743-y, 2020.
  20. [20] S. Annas, M. I. Pratama, M. Rifandi, W. Sanusi, and S. Side, “Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia,” Chaos Soliton. Fract., Vol.139, Article No.110072, doi: 10.1016/j.chaos.2020.110072, 2020.
  21. [21] J. Ohashi, “Epidemic prediction of COVID-19 infection (April 11, 2020), the University of Tokyo,” http://www.bs.s.u-tokyo.ac.jp/content/files/covid/COVID-19_SEIRmodel_full_ver4.1.pdf (In Japanese) [accessed August 19, 2020]
  22. [22] Mitsubishi Research Institute, “COVID-19 policy analysis report 3: Each country’s infection status from the analysis of lethality,” https://www.mri.co.jp/knowledge/column/20200423.html (in Japanese) [accessed August 19, 2020]
  23. [23] E. Baldi et al., “Out-of-hospital cardiac arrest during the Covid-19 outbreak in Italy,” N. Engl. J. Med., Vol.383, No.5, pp. 496-498, 2020.
  24. [24] Ministry of Health, Labor and Welfare of the Japanese government, “COVID-19 Contact-Confirming Application (COCOA),” https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/cocoa_00138.html [accessed August 19, 2020]
  25. [25] Ministry of Internal Affairs and Communications of the Japanese government, “Communications Usage Trend Survey,” https://www.soumu.go.jp/johotsusintokei/statistics/data/200529_1.pdf (in Japanese) [accessed August 19, 2020]
  26. [26] J. Kurita, T. Sugawara, and Y. Ohkusa, “Effectiveness of COCOA, a COVID-19 contact notification application, in Japan,” medRxiv preprint, doi: 10.1101/2020.07.11.20151597, 2020.

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