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病原體是如何在世界各地傳播的？又是如何在演化過程中適應人類？我們利用基因體資料分析、統計學、數學模型與生物資訊方法來研究這些問題。透過整合基因體、流行病學、血清學與人類移動資料，我們研究病原體的傳播歷史並理解其演化機制，進而探索如何更精準地控制疫情。我們的研究結合演化生物學、資料科學與公共衛生，研究包括瘧疾、登革熱、結核病、與流感等重要傳染病。
歡迎對分子演化、生物統計、流行病學、生物資訊、或傳染病研究有興趣的同學加入我們！
近期的主題如下：
演化相關的主題：病原體累積的突變是好的還是壞的呢？是否有助於免疫逃脫呢？不同病原體在演化上有何異同？
流行病學相關的主題：什麼因素影響了病例數的起伏呢？如何能預測未來的病例數？不同防疫措施的效果為何？
綜合性的主題：病原體的傳播動態如何受到防疫措施、病原體的演化、以及人類免疫反應共同的影響呢？病原體於區域間傳播的情形如何？抗藥性的分佈如何？
使用的研究工具如下：（依題目不同而使用不同的工具，並非每項都要會）
統計分析方法
生物資訊分析
程式語言：R/Python/C/C++
Linux/Unix
基因體定序之分生實驗
How do pathogens spread across the world, and how do they adapt to humans through evolution? Our research addresses these questions using genomic data analysis, statistical methods, mathematical modeling, and bioinformatics approaches. By integrating genomic, epidemiological, serological, and human mobility data, we aim to reconstruct the transmission history of pathogens and understand their evolutionary dynamics, ultimately providing insights into how epidemics can be more effectively controlled. Our work lies at the intersection of evolutionary biology, data science, and public health. Current research focuses on important infectious diseases including malaria, dengue, tuberculosis, and influenza.
Students interested in molecular evolution, biostatistics, epidemiology, bioinformatics, or infectious disease research are welcome to join our lab!
Our recent research topics are as follows:
Evolutionary: Are observed mutations segregating in the population beneficial or deleterious? Are they related to the immune escape of pathogens? What are the similarities and differences among the evolution of different pathogens?
Epidemiological: What factors influence the fluctuations in the case number? How can we predict the number of cases in the future? What are the effects of different preventive measures?
Interdisciplinary: How are infectious disease dynamics driven by preventive measures, pathogen evolution, and the human immune response? What is the level of pathogen spread between regions? What is the distribution of drug resistance?
We use the following tools: (not all the lab members use the same tools, it depends on the project they work on and it is not necessary to know all of them)
Statistical methods
Bioinformatic tools
R/Python/C/C++
Linux/Unix
Genomic sequencing
Recent Research
Recent Research
我們利用問卷資料，探討 COVID-19 疫情不同階段中，民眾對疾病風險與防疫措施的健康信念，如何影響其家庭內與社區中的社交接觸行為。我們進一步分析這些心理與行為變化如何隨疫情波動而調整，為理解疾病傳播與公共衛生決策提供資訊。 此研究為與台大林先和老師及倫敦衛生與熱帶醫學院Han Fu的合作，感謝美國Crisis Ready計畫與衛福部防疫新生活行為監測計畫的支持。
We used survey data to investigate how health beliefs about COVID-19 influenced individuals’ contact patterns within households and in the community across different epidemic phases in Taiwan. By examining how these perceptions and behaviors evolved over time, we provide empirical evidence relevant to disease transmission and public health decision-making.
我們與台大的蔡坤憲老師及美國UCSF的Bryan Greenhouse合作，結合amplicon sequencing data與流行病學數據，了解瘧疾於聖多美的傳播，發現境外移入將視疫情控制中重要的一環。
We generated amplicon sequencing data from 980 samples between 2010 and 2016 to examine the genetic structure of the malaria parasite population in São Tomé and Príncipe. Our findings highlight the need to prioritize strategies to reduce the introduction of new parasites into this island nation as it approaches elimination.
我們發展了新的分析方法 (pMK test)，利用登革熱病毒基因體序列推測個別基因及血清型在各地區的自然選汰力量。
We developed a new method, pMK test, to infer the selective forces dominating the evolution of each gene for all four dengue serotypes.
我們利用高通量蛋白質晶片得到不同病人的免疫反應資料，利用T-SNE分析發現各國肺結核病人的免疫反應有顯著差異。
We analyzed protein array data from different patients to understand the difference in immune responses to TB between countries.
我們綜合流行病學、基因體，與人潮流動資料所推測出的肺結核位於高雄的傳播鏈，了解傳播鏈及其特性有助於制定防疫措施。合作者為台灣大學的林先和老師。
We collaborate with Prof. Hsien-Ho Lin from National Taiwan University and integrate epidemiological, genomics, and human mobility data to infer transmission clusters of TB in Kaohsiung City, Taiwan.
基因體的工具可以用來了解病原體的傳播。所需要的工具以及所需要回答的問題會依疾病流行程度而有所不同。
Genetic tools can be used to understand disease transmission in different transmission settings.
Figure from Wesolowski et al. 2018 (BMC Medicine)
新冠肺炎傳播模型 Modeling the potential spread of SARS-CoV-2 in Taiwan
我們與Facebook Data for Good合作，結合數學模型與人潮流動資料，以推測疾病傳染的高風險區，並模擬實施交通管制對控制疫情可能造成的影響。此網址顯示了其中一部分研究結果。這是我們的文章。
In collaboration with Facebook Data for Good, we built metapopulation models that incorporate human movement data with the goals of identifying the high risk areas of disease spread and assessing the potential effects of local travel restrictions in Taiwan. Some of our results can be found here. Our paper was published in BMC Public Health.
口罩數學模型 Modeling face mask use
我們建立數學模型以探討配戴口罩對於減緩疫情擴散的作用，並分析在資源有限的情況下，如何分配資源是最有效的。這是我們的文章全文。
We examined the role of face masks in mitigating the spread of COVID-19 in the general population, using epidemic models to estimate the total reduction of infections and deaths under various scenarios. In particular, we examined the optimal deployment of face masks when resources are limited, and explored a range of supply and demand dynamics. Please see the full article for details.
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