在本篇論文中，我們使用人流資料和疾病流行模型分析台灣 COVID-19 的疫情。分
析台灣至今經歷 COVID-19 不同的三個時期：Delta 時期、Omicron 早期和 Omicron
後期。我們解釋了為什麼在前兩個時期沒有引起疫情大爆發，以及在 Omicron 變異株
後期導致確診病例迅速上升的可能因素。利用 Google 提供的人流趨勢變化資料，並
且改進了一個 SEQIR 模型，使模擬更接近實際情況。為了分析疫情持續時間和確診
病例數對疫情造成的影響，我們嘗試了不同的因素進行模擬，並從實驗結果有以下結
論：（一）較低的人流移動更容易匡列接觸者，減少隱形的傳播鏈；（二）佩戴口罩的
比例會影響疫情持續時間和確診人數；（三）隔離時間越短，疫情爆發的機會就越大。
因此我們認為人流變化、戴口罩和隔離政策是影響台灣 COVID-19 的三個關鍵因素。

In this thesis, we analyze the COVID-19 pandemic in Taiwan using mobility data and the epidemic model. To explain three different stages that Taiwan has gone through:
the Delta variant, the early stage of the Omicron variant, and the later stage of the Omicron variant. We explain why the first two periods did not cause a major outbreak in Taiwan and the possible factors that caused the confirmed cases to rise quickly in the later stage of the Omicron variant. Using the human mobility data provided by Google, we improved a SEQIR model so that the simulation could get close to the actual situation. To analyze the impact of the duration and the number of confirmed cases of the epidemic, we experimented with different factors that could have impacts on the spread of the contagious disease. From our experimental results, we reach the following conclusions: (1) Low human mobility facilitates contact tracing and that reduces invisible chains of transmission; (2) The proportion of the population wearing masks could affect the duration of an epidemic and the number of confirmed cases; (3) Shortening quarantine periods increases the chance of having an outbreak. We believe that human mobility, wearing masks, and the quarantine policy are the three key factors impacting COVID19.

Contents . . . . . . . . . . . . . . . . . . . . 1
List of Figures . . . . . . . . . . . . . . . . . 5
List of Tables . . . . . . . . . . . . . . . . . .6
1 Introduction . . . . . . . . . . . . . . . . . .7
2 Human mobility - Delta variant stage. . . . . . 12
2.1 Dataset . . . . . . . . . . . . . . . . . 12
2.2 Analysis and discussion . . . . . . . . .. 13
2.2.1 Human mobility in Taiwan . . . . . . 13
2.2.2 Human mobility in four different countries . . . 18
2.2.3 Three cases of the Delta variant in Taiwan . . . 20
3 SEQIR Model - Omicron variant early stage . . . . . . . . 22
3.1 Research Method . . . . . .. . . . . . . . . . . . . . 22
3.1.1 Power-law degree distribution .. . . . . . . . . 23
3.1.2 Configuration Model .. . . . . . . . . . . . . . 24
3.1.3 SEQIR Model . . . . . . . . . . . . . . . . . . 24
3.1.4 Parameter Setup . . . . . . . . . . . . . . . .26
3.1.5 Simulation . . . . . . . . . . . . . . . . . . . 28
3.2 Experiments and Results . . . . . . . . . . . . . . . . 31
3.2.1 Proportion of infectious nodes in the exposed state . 31
3.2.2 The probability of quarantine . . . . . .. . . . . . 34
3.2.3 A small number of infected node . . . . .. . . . . . 37
4 Quarantine periods - Omicron variant later stage . . . . . . . .43
5 Conclusion 46
6 Appendix 49

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