This paper estimates the state transition of COVID-19 positive cases by analyzing the data about confirmed positive cases in Tokyo, Japan. The prediction of the number of newly infected persons is one of the active research topics for the COVID-19 pandemic. Although such a prediction is important for recognizing the future risk of spreading infectious diseases, understanding the state transition after they are confirmed to be positive is also important for estimating the number of required ICUs, hotel rooms for isolation, etc. This paper classifies the state after being positive into “in hotel/home for isolation,” “in hospital with a mild state,” “in hospital with a severe state,” “recovered,” and “dead” and estimates the transition probabilities among those states from the data about confirmed positive cases in Tokyo, Japan. This paper shows the parameters estimated from different periods and discusses the difference considering the pandemic situation. An agent simulation using the estimated transition probabilities as its parameters is also proposed. The result of the simulation from August to November 2020 shows the predicted number of agents is close to the actual data. As one of the possible applications to the proposed agent simulation, this paper shows the simulation result from December 2020 to January 2021 under a hypothetical situation.

1. [1] J. Dehning et al., “Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions,” Science, Vol.369, Issue 6500, Article No.eabb9789, 2020.
2. [2] N. Ferguson et al., “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand,” Imperial College London (16-03-2020), doi: 10.25561/77482, 2020.
3. [3] Y. Takama, “Analysis and Agent Simulation of State Transition of COVID-19 Positive Cases in Tokyo, Japan,” The 7th Int. Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII), Article No.T2-5-5, 2021.
4. [4] R. Gupta et al., “Machine learning models for government to predict COVID-19 outbreak,” Digital Government: Research and Practice, Vol.1, No.4, Article No.26, 2020.
5. [5] M. Kiamari et al., “COVID-19 risk estimation using a time-varying SIR-model,” SIGSPATIAL’20 Int. Workshop on Modeling and Understanding the Spread of COVID-19, pp. 36-42, 2020.
6. [6] A. Sedaghat et al., “COVID-19 (Coronavirus disease) outbreak prediction using a susceptible-exposed-symptomatic infected-recovered-super spreaders-asymptomatic infected-deceased-critical (SEIR-PADC) dynamic model,” IEEE 3rd Int. Conf. and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE), pp. 275-282, 2020.
7. [7] J. Deshmukh et al., “An interactive simulator for COVID-19 trend analysis,” 8th ACM IKDD CODS and 26th COMAD, pp. 385-389, 2021.
8. [8] M. Kumar et al., “Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model,” Digital Government: Research and Practice, Vol.1, No.4, Article No.24, 2020.
9. [9] T. Mantoro et al., “Prediction of COVID-19 spreading using support vector regression and susceptible infectious recovered model,” 6th Int. Conf. on Computing Engineering and Design (ICCED), 2020.
10. [10] J. Pesavento et al., “Data-driven mobility models for COVID-19 simulation,” Proc. of the 3rd ACM SIGSPATIAL Int. Workshop on Advances in Resilient and Intelligent Cities (ARIC), pp. 29-38, 2020.
11. [11] H. Bao et al., “COVID-GAN: estimating human mobility responses to COVID-19 pandemic through spatio-temporal conditional generative adversarial networks,” Proc. of the 28th Int. Conf. on Advances in Geographic Information Systems (SIGSPATIAL), pp. 273-282, 2020.
12. [12] G. Thakur et al., “COVID-19 joint pandemic modeling and analysis platform,” Proc. of the 1st ACM SIGSPATIAL Int. Workshop on Modeling and Understanding the Spread of COVID-19, pp. 43-52, 2020.
13. [13] Y. Zhang et al., “Mapping the landscape of COVID-19 crisis visualizations,” Proc. of the 2021 CHI Conf. on Human Factors in Computing Systems, Article No.608, 2021.
14. [14] H. Samet et al., “Using animation to visualize spatio-temporal varying COVID-19 data,” Proc. of the 1st ACM SIGSPATIAL Int. Workshop on Modeling and Understanding the Spread of COVID-19, pp. 53-62, 2020.
15. [15] J. Hua et al., “An initial visual analysis of the relationship between COVID-19 and local community features,” 24th Int. Conf. on Information Visualization (IV), pp. 718-722, 2020.
16. [16] Z. Wang and O. Aydin, “Sensitivity analysis for COVID-19 epidemiological models within a geographic framework,” Proc. of the 1st ACM SIGSPATIAL Int. Workshop on Modeling and Understanding the Spread of COVID-19, pp. 11-14, 2020.
17. [17] L. Su and H. Yu, “Analysis of factors influencing the spatial distribution of provincial cumulative confirmed count of novel Coronavirus pneumonia (COVID-19) in China,” Int. Conf. on Public Health and Data Science (ICPHDS), pp. 81-85, 2020.
18. [18] C. Berry, H. Berry, and R. Berry, “Mask mandates and COVID-19 infection growth rates,” IEEE Int. Conf. on Big Data, pp. 5639-5642, 2020.
19. [19] J. Kang, N. Huang, and X. Liu, “MAS-SEIR-II simulation on COVID-19 in China,” Proc. of the 2020 Int. Conf. on Big Data in Management (ICBDM), pp. 113-117, 2020.
20. [20] S. A. Chun et al., “Tracking citizen’s concerns during COVID-19 pandemic,” The 21st Annual Int. Conf. on Digital Government Research, pp. 322-323, 2020.
21. [21] M. Silva and F. Benevenuto, “COVID-19 ads as political weapon,” Proc. of the 36th Annual ACM Symp. on Applied Computing (SAC), pp. 1705-1710, 2021.
22. [22] Y. Zhu and Y. Jiang, “The four-stages strategies on social media to cope with “infodemic” and repair public trust: COVID-19 disinformation and effectiveness of government intervention in China,” IEEE Int. Conf. on Intelligence and Security Informatics (ISI), 2020.
23. [23] NHK (January 14, 2021), https://www3.nhk.or.jp/news/html/20210114/k10012814381000.html (in Japanese) [accessed February 15, 2022]