RESEARCH

了解病原體如何演化與傳播對於制定有效的傳染病控制方法非重要。因為許多演化力量，像是突變、個體遷移、基因重組、及選汰都會影響病原體基因體上的變異性，藉由收集及分析基因體資料，並整合其他如血清學、流行病學或人類移動的資料，我們可以了解病原體的傳播與自然選汰。我們的工作不只將回答演化生物學中的重要問題，也可望為制定公共衛生政策提供有用的見解。

我們會從不同的角度，使用基因體學、生物資訊、統計方法與數學模型來回答這些問題。除了使用既有的方法外，我們也試著填補目前方法上的不足，開發自己的分析方法。歡迎有興趣的同學加入我們！

近期的主題如下：

  • 偏演化的主題：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？

  • 偏流行病學的主題：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）

  • 統計方法

  • 生物資訊方法 (ex: PAML)

  • R語言/Python/C/C++

  • Linux/Unix

  • 基因體定序之分生實驗

We are interested in understanding the evolutionary forces shaping the genetic diversity of pathogens and how this understanding can be used to inform disease intervention. Specifically, we aim to study how the evolution of pathogens is influenced by a variety of forces – transmission dynamics, migration between populations, natural selection, and host-pathogen interactions – as well as the interplay between these forces, and apply our results to infectious disease epidemiology and control. We will use approaches from genomics, bioinformatics, statistics, and mathematical modeling to answer these questions. Please contact us if you are interested!

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 (ex: PAML)

  • R/Python/C/C++

  • Linux/Unix

  • Genomic sequencing

Recent Research

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.

登革熱的演化樹。藉由演化樹，可以了解登革熱病毒在不同國家或是不同洲之間傳播的情形。

Phylogenetic tree made using genomic sequences of dengue virus