Sustainable Development Goals
Abstract/Objectives
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 late-stage Omicron variant. We explain why the first two periods did not cause a major outbreak in Taiwan and the possible factor 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 on the epidemic, we experimented with different factors. From the results, there are some conclusions: (1) The lower human mobility makes it easier to list contacts and reduces invisible chains of transmission; (2) The proportion of wearing a mask could affect the duration of an epidemic and the number of confirmed cases; (3) The shorter quarantine periods, the more chance happened an outbreak. We believe that human mobility, wearing a mask, and the quarantine policy are the three key factors impacting COVID-19.
Results/Contributions

In this thesis, we analyzed COVID-19 using network science. Our analysis of human mobility shows the important impact on COVID-19 and explains the reason why the Delta variant did not cause an outbreak in Taiwan. Our SEQIR model is not only more adaptive than the traditional SEIR model but also more intelligible than mathematical estimation methods. Moreover, we show the impact of NPIs on COVID-19, including wearing masks and quarantine policies.

To understand the effect of human mobility, we considered two types of data: the trend of confirmed cases and the change in human mobility. The transit station’s mobility is one of the most important factors relating to the change in the number of confirmed

cases. When the outbreak occurred, there was a reduction of 20% of people entering the transit station in Taiwan, especially on the weekends. In addition to our analysis for Taiwan, we further extended our study to other countries, including Japan, the United Kingdom, the United States, and Singapore.

To address the impact of NPIs on COVID-19, we focus on the duration and the number of infected nodes for a wave of epidemic. Lowering human mobility not only reduces the transmission risk but also eases the efforts for contact tracing and the follow-up quarantine. Enforcing the quarantine policy reduces the chance of continuous spread of infection. Wearing masks could reduce the probability of being infected. Our experiment shows that these NPIs effectively reduce the duration and the number of infected nodes.

Based on the experimental results, we also explain three stages that Taiwan has gone through: In the Delta variant and early stage of the Omicron variant, most people do not enter transit stations on holidays to avoid crowds and long-distance travel. The low mobility is the reason why variants did not cause a major outbreak in Taiwan.

Keywords
COVID-19MobilityDeltaOmicronSEQIRQuarantine