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- [Pandemic Resilience] Year Started : 2023
Assistant Professor
Department of Infectious Diseases
The University of Tokyo hospital
Behavioral changes during the COVID-19 pandemic have led to a decrease in most infectious diseases, while sexually transmitted infections (STIs) such as syphilis and Mpox have increased. Using big data, I aim to reveal the incidence, prevalence, reinfection rates, and risks of STIs. Based on these findings, I intend to develop strategies for controlling STI outbreaks. Additionally, I aim to establish a system for the early detection of emerging STIs outbreaks in the future.
Associate Professor
Hakubi Center
Kyoto University
In this study, I apply cutting-edge causal inference and machine learning techniques to a nationwide elderly cohort, elucidating the characteristics of older adults who are vulnerable to pandemics (e.g., worsening depressive symptoms, cognitive decline). Additionally, I shed light on the characteristics of older adults who benefit significantly from physical activity and social participation during the COVID-19 crisis. By estimating individual vulnerabilities and intervention effects based on multifaceted attribute information, I pave the way for new horizons in personalized strategies, considering the societal context. Through this approach, I aim to catalyze a historical transformation in the role and practice of medicine and public health globally.
Assistant Professor
School of Medicine
Nanyang Technological University
First, I will establish an efficient method for acquiring clinical data. I will connect a fully automated genetic testing system to a server using IoT technology to streamline the process from sample collection to data storage. Next, I will develop digital twins, which are computer-based patients that replicate real-world patients. I will create a system using data generated from these digital twins to formulate feasible strategies for infectious disease control. The digital twins will be made publicly available, encouraging further use in pandemic preparedness.
Research Associate
Graduate School of Medicine
Chiba University
Numerous infectious diseases have emerged through viral transmissions from animals to humans. This study endeavors to identify viruses with pandemic potential and elucidate the early warning indicators and risk factors for zoonotic disease outbreaks by re-using public deep sequencing data and conducting large-scale virus genomic surveillance in humans and animals. This study will provide an essential scientific groundwork for pandemic preparedness, especially promoting a proactive approach and risk mitigation strategies in the pre-pandemic stage.
Professor
Graduate School of System Informatics
Kobe University
In this study, I focus on multi-group epidemic models in which the host population is divided into subgroups according to the heterogeneity of each individual. In particular, I construct multi-group structured epidemic models that enable us to consider multiple factors such as the variation of the immunity level and the people’s behavior change. I aim for development and implementation of mathematical theory of such multi-group structured models. Moreover, using the theory of type reproduction number, I analyze the effectiveness of policies targeted to specific groups, for instance, vaccination policies targeted to high risk groups.
Associate Professor
School of Politics and International Studies
University of Leeds
Effective pandemic preparedness relies on the active engagement of citizens and their willingness to support long-term investments in preparedness. While interest in preparedness may have surged in response to the COVID-19 pandemic, there is a serious concern that this enthusiasm may wane over time. Furthermore, our understanding of how pre-pandemic preparedness may influence the political dynamics during a pandemic and subsequent pandemic responses remains limited. Through a political science perspective, this project aims to identify political challenges in preparing for future pandemics and their implications for pandemic response.
Assistant Professor
Department of Emergency and Critical Care Medicine
St. Marianna University School of Medicine
This research aims not only to establish a Platform of Multi-platform Trials (PMPT), a new concept to connect multiple platform trials for infectious diseases, but also to address how to sustain and develop PMPT in the unique research environment in Japan, leading to the resilient society for infectious disease threats.
Chief
Disease Control and Prevention Center
National Center for Global Health and Medicine
The goal of this project is to determine the upper limit of “the cost that our society is willing to pay for infectious disease control” in order to prepare for a new pandemic that will eventually occur. The goal of this study is to optimize infectious disease countermeasures from both economic and health perspectives by determining the upper limit of the acceptable cost, or “willingness to pay”.
Professor
Graduate School of Medicine
Kyoto University
In this research, I will analyze three key aspects using real-world data from the COVID-19 pandemic: 1. Analysis of cluster risk factors and visualization of outcomes in elderly-care facilities, 2. Assessment of the physical, mental, and economic impact on elderly welfare and healthcare stakeholders, and 3. Evaluation of time-sensitive initiatives implemented by local authorities. Furthermore, I will provide recommendations for building a resilient elderly welfare system that remains steadfast even during a pandemic, while also developing a seamless system to integrate welfare and healthcare.
researcher
Institute for the Promotion of Science and Technology
Ehime University
Mathematical modelling of infectious diseases is widely used as an important tool for policy making. This approach becomes more effective when combined with observational data on epidemic situations. In this project, I propose a modelling framework to integrate various data sources that reflect social activities of people and the spread of pathogens. By developing this framework, I aim to obtain more granular understanding of transmission dynamics among heterogeneous subgroups in a population and utilize such information to optimize the targeted interventions for disease control.