Event info

1st Edition: Mathematical Challenges to Infectious Disease
Date and Time: 11th September (Friday) 12:30 - 14:40 (JST) 13:30 - 15:40 (AEST)

Organizers:
Dimetre Triadis, La Trobe University (Australia) / Kyushu University (Japan)
Kenji Kajiwara, Kyushu University (Japan)

How to join:
Register at https://zoom.us/webinar/register/WN_KGViJdwRQ9mz8_lMho2S7g
After registration, the information of how to connect to the meeting will be sent by e-mail.

Program:
12:30 - 12:35 (JST) Short address from the organizers

12:35 - 13:35 (JST) Talk 1
Speaker: Joel Miller (La Trobe University)
Title: The role of mathematical modelling in designing infectious disease interventions
Abstract:
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 (JST) Talk 2
Speaker: Shingo Iwami (Kyushu University)
Title: Mathematical sciences enhance COVID-19 research
Abstract:
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.