令和2年度電気・電子・情報関係学会四国支部連合大会 講演論文集 (愛媛大学)
2020 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME UNIV.)

1-18

Simulation of Spreading COVID-19 by Using Extended SEIR Modelwith Unconfirmed Cases and Death Toll
Aika OHNO, Shu SUMIMOTO, Yoko UWATE,(Tokushima University)

and Yoshifumi NISHIO

1. Introduction

3. Simulation Result

Currently, COVID-19 is spreading all over the world, andthe number of confirmed cases is increasing day by day.
Then, SEIR model is applied for prediction of spreading
COVID-19. However, because of existing asymptomatic
infected people and limited number of testing, there is adiﬀerence between the number of confirmed cases and infectious people. In addition, it is important to consider thedeath toll of the epidemic, not only the infection cases. In
this study, we extend SEIR model considering confirmed
cases, unconfirmed cases and death toll.

Parameters that we use are shown as follows:
β=0.26, ϵu =0.18, ϵc =0.02, γ=0.09463, κu =0.00004884,
κc =0.004884. Eq. (1) is solved by the Runge-Kutta method.
Figure 2 shows the simulation results.

2. Proposed Method
We propose this extended SEIR model (Fig. 1). It is considered both confirmed and unconfirmed cases for the number of infectious, recovered and death. The extended SEIR
model can be described by Eq. (1). S(t), E(t), Iu (t), Ic (t),Ru (t), Rc (t), Du (t) and Dc (t) mean populations of susceptible, exposed, infectious unconfirmed, infectious confirmed,
recovered unconfirmed, recovered confirmed, death unconfirmed and death confirmed at time t, respectively. Then,β, ϵu , ϵc , γ, κu and κc mean infectious rate, onset rate forunconfirmed infectious, onset rate for confirmed infectious,
recovered rate, death rate for unconfirmed infectious anddeath rate for confirmed infectious, respectively.

Figure 2: Simulation results of the proposed model.
Figure 3 shows real new confirmed data of infectious people in Tokyo.

Figure 3: Real data of new confirmed cases of infectious
people.
There is the diﬀerence between simulation result and realdata, especially the shapes of graphs. It is because of thecharacteristic of SEIR model. SEIR model can express
only one wave of infections when parameters are constant.
Therefore, the simulation result does not fit with the real
data that has a part of second wave.
Figure 4 shows real confirmed death data in Tokyo.

Figure 1: Structure of proposed model.
 dS= −βS(t){Iu (t) + Ic (t)}
dt

 dE

= −βS(t){Iu (t) + Ic (t)} − (ϵu + ϵc )E(t)
dt

dIu

= ϵu E(t) − γIu (t) − κu Iu (t) dIdtcdt= ϵc E(t) − γIc (t) − κc Ic (t)dRu= γIu (t)
dt
dRc

=γIc (t)
dt
 dD
u

=κu Iu (t)dt dDcdt(1)= κc Ic (t)In addition, we calculate new confirmed cases of infectious people N (t) by Eq. (2).
Ic (t)
N (t) =γ(2)Figure 4: Real data of confirmed death cases.
Though there is the diﬀerence between simulation result
and real data, real date plot of confirmed death cases fits thesimulation data. According to the simulation, confirmed
cases of death will be more than 6000 cases after 250thdays.

4. Conclusion
In this study, we simulated the spreading COVID-19 in
Tokyo with extended SEIR model. The simulation results
were close to real data, but they did not fit enough. For
future work, we will optimize simulation results by root
mean square error.