October 10, 2024 | Radio Canada via UVic News
In a groundbreaking study, researchers from Canada and the U.S. have developed an innovative method to forecast the transmission rates of infectious diseases, predicting an earlier spike in influenza cases this year. By leveraging advanced mathematics and machine learning, the team analyzed extensive data from late 2015 to September 2024, including weather conditions, policy choices, and movement patterns. Their model anticipates over 1,600 new flu cases daily in U.S. laboratories by the end of November, nearly double last year’s figures.
The study, led by Professor Hao Wang from the University of Alberta, emphasizes that similar trends could be expected in Canada. Wang, who is also the director of the Interdisciplinary Lab for Mathematical Ecology and Epidemiology, hopes this information will help public health officials prepare for an early flu season by ensuring adequate hospital capacity and encouraging timely vaccinations. The team’s innovative approach has been documented in several peer-reviewed papers, highlighting the effectiveness of their forecasting model.
Junling Ma, Professor in the Department of Mathematics and Statistics at the University of Victoria, is recognized for his expertise in mathematical modeling of infectious diseases. Although not directly involved in the recent University of Alberta study, Ma has expressed confidence in the forecasting model used by the research team. This model, which incorporates data from various sources including weather conditions, policy choices, and movement patterns, predicts an earlier spike in influenza cases this year. Ma believes that such models are crucial for public health officials to prepare for potential surges in disease transmission, ensuring adequate hospital capacity and encouraging timely vaccinations. His insights highlight the importance of mathematical biosciences in public health planning and response.
Dr. Ma specializes in mathematical modeling of infectious diseases, focusing on how diseases spread and how they can be controlled. His research includes studying the dynamics of diseases such as influenza, HIV, Ebola, and cholera. Dr. Ma received his B.Sc. and M.Sc. in Applied Mathematics from Xi’an Jiaotong University in China, and later earned his Ph.D. in Applied Mathematics from Princeton University in 2003. His work often involves the use of random networks to model disease spread and the interaction between disease dynamics and viral evolution. In addition to his research, Dr. Ma is actively involved in teaching at the University of Victoria, and has contributed to multiple academic papers on the subject. His expertise is widely recognized, and he frequently collaborates with other researchers to advance the field of mathematical biosciences.
This new forecasting method represents a significant advancement in our ability to predict and manage infectious disease outbreaks. By providing early warnings and detailed predictions, it can help public health officials take proactive measures to protect communities and save lives. For those interested in delving deeper into the mathematical intricacies behind these models, many articles from Dr. Ma’s extensive body of work are accessible via UvicSpace, the institutional repository. Check them out to explore the vital role of mathematical biosciences in public health.
