防疫政策是否能有效控制疫情?

Can Policy For Combating COVID-19 Be Effective?

何品玲(Pin-Ling Ho)

指導教授 ： 唐代彪

共同指導教授 ： 鄧志松(Chih-Sung Teng)

國立臺灣大學/社會科學院/國家發展研究所/碩士(2021年)

https://doi.org/10.6342/NTU202100697

摘要

2020年新冠病毒全球大流行，帶給人類前所未有的浩劫與危機。因應病毒的威脅，各國政府紛紛實施各種干預、限制性措施來防疫，頓時讓全球進入前所未有「靜止」的狀態，其所造成的傷害與損失更是無法估算。本論文僅就英國牛津大學研究團隊(OxCGRT)所建立的全球各國政府執行防疫政策總強度平均指數與世界衛生組織(WHO)統計各國確診人數等數據資料，運用相關迴歸分析其之間關係，與在種種可能因素之下對防疫政策執行效果的影響。透過各種假設迴歸模型檢驗，發現其觀察到的現象是「防疫政策總強度平均指數」與「確診比」呈現「低度正向」的關係，與原本假設「實施防疫政策對於疫情能有效控制其關係應該是負向的」是不符合的。追究其原因，雖然防疫總強度平均指數各國多很接近，差異不大，但在防疫措施執行細項如「篩檢」、「接觸者追蹤」等其強度仍有差別。可能是這些基本且必需防疫項目需要政府投入較多，易受限於各國技術與經費、人力、國民收入等問題。因此，多數政府盡可能增強「成本較低」、「較容易執行」如「限制聚集」、「取消公共活動」等的項目試圖遏制日益嚴重的疫情，此為本論文解釋該現象理由之一。另外，影響防疫政策執行效果的因素可能很多。本論文蒐集學者們曾經發表研究文獻裡所提到的各種因素作相關迴歸檢驗，發現有些因素例如「普篩比」、「民主指數」、與「HDI」等可能會間接影響防疫政策對於疫情控制的效果，尤其是對於富裕民主國家而言，突顯這一類國家執行防疫政策的特點，此為另一個解釋的理由。本篇論文透過各項數據以相關迴歸方法探索各國執行防疫政策總強度平均指數與疫情效果之間的關係，期望能在這場人類與病毒的戰爭中，藉著檢視過去以展望未來。

關鍵字

確診比 ； 防疫強度指數 ； 民主指數 ； 人類發展指數 ； Q2GDP成長率

並列摘要

Covid-19 which was a new type coronavirus broke out and brought unprecedented global pandemic in 2020. In order to prevent from coronavirus, all countries in the world have implemented various interventions and restrictive policies. However, these epidemic prevention policies also made the world an unprecedented "silent" state and stopped all kinds of the development. In order to find out the most effective epidemic prevention policies under various index around all over the world, we take advantage of the average index of the total intensity of various epidemic prevention policies established by the Oxford University research team (OxCGRT) of the University of Oxford and the number of confirmed cases in each country reported on the World Health Organization (WHO) and implement these effecters into hypothesis regression model. We discovered the score of "testing policy" and "contact tracing" in severely confirmed countries are low, which probably due to these policies required much of technical, financial, and manpower.

However‚most governments enhanced "lower cost" and "easier implementation" projects such as "restricted gathering", "canceling public events" and "public info campaigns" to control the spread of the epidemic. However, the result shows that the "average index of the total intensity of epidemic prevention policies" and the "confirmed cases ratio" show a low positive relationship, which means the increase of the intensity of the policy worsen the situation and is contrary to the original assumption. To further investigate the result, we also make relevant regression tests based on various factors mentioned in the research literature published by scholars and find that some factors may indirectly affect the effect of epidemic prevention policies on epidemic control, such as "population density"、"general testing ratio"、"democracy Index"、"HDI"、and "national income" etc. These factor and the regression result makes a reasonable explanation for growth of the confirmed cases, especially in high income countries. This study uses various data to explore the relationship between the average index of the total intensity of the epidemic prevention policies implemented by each country and the epidemic effect through various data and related regression methods. It is hoped that in this war between humans and viruses, we can look forward to the future by examining the past.

並列關鍵字

COVID-19 ； confirmed-case ； epidemic prevention policies ； HDI ； regression

參考文獻

王開弘(2006)。《台灣防疫政策的歷史制度分析》。台北:臺灣大學政治學研究所，學位論文。

方世榮(2001)。《統計學導論》。台北市:華泰文化事業。

吳進進等(2020)。〈信息公開是否影響公眾政策遵從意願?〉，《公共行政評論》，(3): 83。

李德仁等(2020)。〈基於時空位置大數據的公共疫情防控服務讓城市更智慧〉，《武漢大學學報·信息科學版》，45(4): 480。

肖水源、劉愛忠(2050)。《瘟疫的歷史》。台北:紅螞蟻。