Volume 17 Number 3 (May. 2022)
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JSW 2022 Vol.17(3): 130-136 ISSN: 1796-217X
doi: 10.17706/jsw.17.3.130-136

A Simulation of Covid-19 Transmission until the Steady State Using Graph Algorithm

Toshinari Baba*
New International School of Japan (High School), 3-18-32 Minamiikebukuro, Toshima, Tokyo, Japan.

Abstract—This research computes the average days to reach the steady state of COVID-19 infection by repeated simulation of human interactions using a bi-directed graph. It examines the effect of one infected person on a community comprising groups of people who interacts daily with each other such as school, commuter train, family, etc. Randomization is used to determine group structure and a bi-directed graph models the network of frequently interacting people. Once a person is infected, the incubation period is 5 days, and this person possibly infects other people for the next 6 days. A randomization distribution determines whether each person directly linked to an already infected person will be infected, or not. The effective reproduction number of the actual data is used in the study. Finally, this study examines how many days are required to reach the steady state where a new infected person is not observed any more. The study highlights number of days at the infections peak and how many days are required to reach the steady state. Future research would consider multiple starting points, variants, ages, genders, ethnicities, seasons, and regions. Furthermore, comparison with results of AI machine learning will be examined.

Index Terms—Covid-19, graph algorithm, pandemic simulations, steady state.

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Cite: Toshinari Baba, "A Simulation of Covid-19 Transmission until the Steady State Using Graph Algorithm," Journal of Software vol. 17, no. 3, pp. 130-136, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly 
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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