Modeling the COVID-19 transmission

Authors

  • Yoichi Ikeda ⋅ JP Center for Infectious Disease Education and Research (CiDER), Osaka University

Abstract

I will present the dynamics of the novel coronavirus infectious disease (COVID-19) transmission. The coronavirus is highly contagious and transmissible, so that it was believed that the number of infected people increases exponentially. This is the consequence of the standard traditional model of infectious diseases, so-called the susceptible-infected-removed (SIR) model. In the SIR model, infected people increase until the herd immunity is achieved. However, as shown in observed data, this was not the case for COVID-19 spread, where the secondary and higher transmission were suppressed. To understand this transmission mechanism, we proposed a new compartmental model, what is called the broken-link model, which is motivated by a many-body screening effect, e.g., Debye screening. In my presentation, after a brief review of the SIR model, I will discuss (1) how we measure the spread speed from the data, (2) how we model the spread dynamics, and (3) how the model works for COVID-19 from viewpoints of mathematics and physics.

About the Speaker

Yoichi Ikeda, Center for Infectious Disease Education and Research (CiDER), Osaka University

Yoichi Ikeda is a Professor at the Center for Infectious Disease Education and Research (CiDER) in Osaka University. He obtained his Ph.D. in Physics from Osaka University and has previously held postdoctoral research positions at University of Tokyo, Tokyo Institute of Technology (JSPS), and RIKEN. He has also held positions as specially-appointed Assistant Professor at the Research Center for Nuclear Physics (RCNP) in Osaka University and Associate Professor at Kyushu University.

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Issue

Article ID

SPP-2023-INV-1E-01

Section

Invited Presentations

Published

2023-06-19

How to Cite

[1]
Y Ikeda, Modeling the COVID-19 transmission, Proceedings of the Samahang Pisika ng Pilipinas 41, SPP-2023-INV-1E-01 (2023). URL: https://proceedings.spp-online.org/article/view/SPP-2023-INV-1E-01.