Speaker: Dr. Daihai He, The Hong Kong Polytechnic University
Date and time: June 7, 2023, 10:30-11:30, Ross 638
Abstract: The nearby cities of Iquitos (Peru) and Manaus (Brazil) experienced the world’s highest infection and mortality rates during the first COVID-19 wave in 2020. Key studies suggested that >70% of the city populations were infected in this wave and thus close to herd immunity and protected. It remains an enigma as to why a deadly second wave followed in Manaus worse than the first. To resolve this, we present a data-driven model of epidemic dynamics in Iquitos which we use to help explain and model events in Manaus. The partially observed Markov process model simultaneous fits a flexible “variable R0”, estimates long-term immunity waning and impulsive immune evasion, and thus provides a comprehensive framework for characterizing and modeling new variants of concern.
Biography: Dr He is an Associate Professor of the Department of Applied Mathematics at The Hong Kong Polytechnic University. He earned a Ph.D. in Engineering from Xi’an Jiaotong University in 1999 and a Ph.D. in Mathematics from McMaster University in Canada in 2006. He also did post-doctoral research in Beijing Normal University (China), University of Michigan (USA), and Tel Aviv University (Israel). His main research interests are infectious disease modelling and statistical analysis of medical data. More than 140 papers have been published in journals such as Science Advances, Annal of Internal Medicine, European Respiratory Journal, Journal of the Royal Society Interface, and the research results have been widely reported in media. His modelling of yellow fever in Angola Africa won the second place in the 2018 International Society for Disease Surveillance’s Best Scientific Contribution Paper; He has several highly cited papers on the study of COVID-19. The study on the typhus epidemic in the Warsaw Ghetto during World War II received more than 100 news reports. He received funding from General Research Fund, Collaborative Research Fund from the Hong Kong Research Grants Council, AIDS Trust fund, Health and Medical Research Fund, and the Alibaba (China) Co. Ltd. Cooperative Research Fund.