分野別セミナー

Asia Pacific Online Seminars on Mathematics for Industry
第1回: Mathematical Challenges to Infectious Disease
日時： 9月11日（金）12:30 - 14:40


組織委員：
Dimetre Triadis (ラ・トローブ大学 (オーストラリア) / 九州大学マス・フォア・インダストリ研究所オーストラリア分室)
梶原 健司 (九州大学マス・フォア・インダストリ研究所)


※ 本セミナーはKyushu University Asia Week 2020の一環として，九州大学から支援を受けて開催されます．


参加方法：
https://zoom.us/webinar/register/WN_KGViJdwRQ9mz8_lMho2S7g で登録をお願いします．
登録後，会議への参加方法を記したメールが送付されます．


プログラム：
※全ての講演は英語で行われます．通訳などはありません．


12:30 - 12:35 開会の挨拶


12:35 - 13:35 講演1
講演者: Joel Miller(ラ・トローブ大学)
タイトル: The role of mathematical modelling in designing infectious disease interventions
概要:
Mathematical modelling has played an important role in guiding policy decisions, affecting the timing of lockdowns, the design of testing and tracing policies, and the response to outbreaks in high risk communities. It has also played an important role in helping us to interpret the observed course of the epidemic. In this talk, I will discuss a selection of examples showing how simple mathematical models can help guide policy design for infectious disease in general, and COVID-19 in particular, and I will discuss some of the current challenges which mathematical modelling is helping to address.


13:40 - 14:40 講演2
講演者: 岩見 真吾 (九州大学大学院理学研究院)
タイトル: Mathematical sciences enhance COVID-19 research
概要:
The recent spread of corona threatens the health of people around the world. We urgently need strategies to reduce COVID-19 spread and to enhance antiviral drug development for individual patients. Mathematics could contribute to control of COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, and implementation of social distancing measures and other interventions. My group is conducting interdisciplinary research to elucidate “Quantitative Population Dynamics” with original mathematical theory and computational simulation, which are both our CORE approach. Our mathematical model-based approach has quantitatively improved a current gold-standard approach essentially relying on the statistical analysis of “snapshot data” during dynamic interaction processes in virus infection.


In my talk, I would first like to introduce the mathematical model-based approach, showing our previous work with experimental and clinical groups, and discuss how we extract novel and important insights from time-course datasets which are designed for our purpose in several virus infections. Thanks to mathematical models, we analytically derive important indices, which can capture the dynamical properties for infections, and quantify them from estimated parameter values. Then, I would like to discuss how our approach improve our current understanding of COVID-19 research, and help an establishment of a "standard antiviral treatment" for COVID-19 as well.