COVID-19大流行暴露了流行病學數學模型在輔助公共衛生決策上的不足之處。由於資料量有限、資料品質不佳，以及模型驗證上的困難，傳統模型往往無法提供準確的預測結果。當不同模型產生矛盾的結果時，這些缺陷尤為明顯，導致決策者和公眾陷入困惑。本論文通過三篇研究論文，系統性的改進COVID-19建模流程——從資料蒐集、疫情趨勢預測，到用於模型基準比較的合成資料產生。
第一項研究，「A structured course of disease dataset with contact tracing information in Taiwan for COVID-19 modelling」，解決了獲取個體數據的挑戰。通過從台灣開放數據庫和每日新聞稿報導中收集數據，我們建立了一個2020年1月21日至11月9日期間579個確診病例的數據集，包含旅行史、年齡、性別、症狀、病例間接觸類型以及病程的訊息。這項研究提供了開放數據庫中結構化個體數據，涵蓋原始SARS-CoV-2病毒在台灣傳播期間的數據集，增強了流行病學建模，使研究人員能夠開發與驗證新的預測模型。
第二項研究，「Taiwan ended third COVID-19 community outbreak as forecasted」，探討了公共衛生應用的預測建模。我們提出了以7日平均之本地及未知確診與疑似病例比率作為預測疫情結束的控制變量。我們的方法預測台灣第三波COVID-19疫情於8月16日結束，而實際閾值交叉發生在8月27日，比預測晚了十一天。我們強調案例接觸追蹤效率對結束社區傳播中的作用。
第三項研究，「CovSyn: an agent-based model for synthesizing COVID-19 course of disease and contact tracing data」，著重在解決模型基準測試的挑戰。利用收集的台灣COVID-19數據，包括接觸追蹤、檢測、病程，以及接觸網絡（家庭、學校、工作場所、醫療保健和其他），我們開發了代理人基模型的算法CovSyn，用於生成具有足夠詳細個體數據的合成數據集。其參數最佳化通過螢火蟲演算法實現。模型在每日新增、7天移動平均和31天移動平均累計確診達到了0.9的$R^2$值。本研究為流行病學模型驗證提供了標準化測試的資料。
本系列研究推進了COVID-19流行病學建模的研究。首先建立結構化病程資料庫，繼而應用了部分人口級數據來預測台灣第三波社區疫情的結束。另外，我們也使用數據集開發了一個基於代理人基模型的合成算法，能夠生成用於模型基準測試的詳細數據。這種方法提高了流行病學建模能力，為公共衛生提供了疾病爆發期間的決策工具，同時建立了可擴展到COVID-19以外其他傳染病建模的方法。

The COVID-19 pandemic revealed significant limitations in how epidemiological models informed public health policy decisions. Traditional models struggled to provide accurate forecasts due to insufficient data quantity, poor data quality, and model validation challenges. These limitations became evident as multiple models yielded contradictory predictions, creating confusion among policymakers and the public. This dissertation addresses these gaps through three research papers that aim to improve COVID-19 epidemiological modelling—from data collection to forecasting and synthetic data generation for model benchmarking.
The first investigation, "A structured course of disease dataset with contact tracing information in Taiwan for COVID-19 modelling," addresses the challenge of obtaining structured individual-level data. By compiling data from Taiwanese databases and daily news reports, we constructed a dataset of 579 confirmed cases spanning January 21 to November 9, 2020. The dataset captures age, travel history, symptoms, gender, contact types between cases, and key dates in disease progression. This work addresses the scarcity of structured individual-level data in open databases by providing a well-documented dataset covering the period when the original SARS-CoV-2 strain circulated in Taiwan. This resource strengthens epidemiological modelling efforts and supports researchers in developing and validating predictive models.
The second investigation, "Taiwan ended third COVID-19 community outbreak as forecasted," explores predictive modelling for public health applications. We proposed the ratio of the 7-day average of local & unknown confirmed to suspected cases as a control variable for predicting outbreak resolution. Applying this method, we forecasted that Taiwan's third COVID-19 outbreak would end on August 16th, whereas the threshold was actually crossed on August 27th, eleven days after the prediction. Our approach highlighted the role of contact tracing effectiveness in ending community outbreaks.
The third investigation, "CovSyn: an agent-based model for synthesizing COVID-19 course of disease and contact tracing data," addresses model benchmarking challenges. We developed an agent-based algorithm, CovSyn, for generating synthetic datasets with sufficiently detailed individual-level data. Our approach draws on the collected Taiwanese COVID-19 data, incorporating contact tracing, testing, and course-of-disease information alongside a contact network derived from municipality statistics, categorized into household, school, workplace, healthcare, and municipality connections. An extensive parameter space search for optimal results was carried out using the Firefly algorithm. The resulting algorithm reaches an R² of 0.9 when comparing simulated and observed cumulative confirmed cases at the daily, 7-day moving average, and 31-day moving average levels. This work contributes to epidemiological modelling research by offering a benchmarking method built on synthetic data that includes detailed demographic, disease-course, and contact-network information for each synthetic subject.
Together, these studies address the challenges of COVID-19 epidemiological modelling. Starting from the construction of a structured dataset capturing disease progression and contact tracing information, we used a subset of the population-level data to forecast the end of Taiwan's third community outbreak. Independently, we used the collected dataset to develop an agent-based synthesis algorithm capable of generating detailed data for model benchmarking. This approach advances epidemiological modelling capabilities, providing public health officials with tools for decision-making during disease outbreaks while establishing methodologies that can be extended to model other infectious diseases beyond COVID-19.

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