# Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Model

#### 2.2. Sensitivity of the Basic Reproduction Number

#### 2.3. Estimation of the Infection Rate

**Description**

**1**

**.**

## 3. Results

#### 3.1. Peak Prediction

#### 3.2. Possible Effect of Intervention

## 4. Discussion

- The essential epidemic size, which is characterized by ${R}_{0}$, would not be affected by the identification rate p in a realistic parameter range $0.01$–$0.1$, in particular, $p\ge 1.0\times {10}^{-6}$.
- The intervention exactly has a positive effect on the delay of the epidemic peak, which would contribute to improve the medical environment utilizing the extra time period.
- Intervention over a relatively long period is needed to effectively reduce the final epidemic size.

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Comparison of $Y\left(t\right)$ with the estimated infection rate $\beta $ and the number of daily reported cases of COVID-19 in Japan from 15 January ($t=0$) to 29 February ($t=45$).

**Figure 2.**Time variation of the number $Y\left(t\right)$ of infective individuals who are identified at time t ($0\le t\le 365$) for $p=0.1$. The dot lines represent the epidemic peak ${t}^{*}$.

**Figure 3.**Time variation of the number $Y\left(t\right)$ of infective individuals who are identified at time t ($0\le t\le 365$) for $p=0.01$. The dot lines represent the epidemic peak ${t}^{*}$.

**Figure 4.**Time variation of the number $Y\left(t\right)$ of infective individuals who are identified at time t ($0\le t\le 365$) for $p=0.01$ and no intervention, 1 month intervention ($T=77$) and 6 months intervention ($T=220$). The dot lines represent the epidemic peak.

**Figure 5.**The relation between the planned final day for intervention T and (

**a**) the epidemic peak ${t}^{*}$; (

**b**) the number of accumulated cases at time $t=365$: $pR\left(365\right)\times 1.26\times {10}^{8}$.

**Table 1.**Number of newly reported COVID-19 cases in Japan until 1 March 2020 [2].

Week | Number of Newly Reported Cases | Number of Accumulated Cases |
---|---|---|

12 January–18 January | 1 | 1 |

19 January–25 January | 2 | 3 |

26 January–1 February | 14 | 17 |

2 February–8 February | 8 | 25 |

9 February–16 February | 28 | 53 |

17 February–23 February | 79 | 132 |

24 February–1 March | 107 | 239 |

**Table 2.**Parameter values for model Equation (1).

Parameter | Description | Value | Reference |
---|---|---|---|

$\beta $ | Infection rate | $0.26$ ($95\%$CI, $0.24$–$0.28$) | Estimated |

${R}_{0}$ | Basic reproduction number | $2.6$ ($95\%$CI, $2.4$–$2.8$) | Estimated |

$\epsilon $ | Onset rate | $0.2$ | [6] |

$\gamma $ | Removal rate | $0.1$ | [7] |

N | Total population in Japan | $1.26\times {10}^{8}$ | [8] |

p | Identification rate | $0.01$–$0.1$ | [9] |

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**MDPI and ACS Style**

Kuniya, T.
Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020. *J. Clin. Med.* **2020**, *9*, 789.
https://doi.org/10.3390/jcm9030789

**AMA Style**

Kuniya T.
Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020. *Journal of Clinical Medicine*. 2020; 9(3):789.
https://doi.org/10.3390/jcm9030789

**Chicago/Turabian Style**

Kuniya, Toshikazu.
2020. "Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020" *Journal of Clinical Medicine* 9, no. 3: 789.
https://doi.org/10.3390/jcm9030789