Math-Bio: Harnessing Mathematical Modeling and Epidemiological Data for Infectious Disease Surveillance and Public Health Decision Making

It’s my great pleasure to announce the continuation of our weekly Math-Bio seminar series! As a reminder, these meetings take place every Wednesday at 2:00 pm (Pacific Time) in the PIMS lounge (ESB 4133). PIMS tea will follow the seminar at around 3:00 pm.

Attend online:
https://ubc.zoom.us/j/64671573478?pwd=J1kiOTYqD0TukMjoVRpTWfBbqSr5TM.1
Meeting ID: 646 7157 3478
Passcode: 979571

Mathematical modeling, when combined with diverse epidemiological datasets, provides valuable insights for understanding and controlling infectious diseases. In this talk, I will present a series of case studies demonstrating how the synergy between modeling and serological, case-based, and wastewater surveillance data can enhance disease monitoring and inform public health strategies.

Serological data measures biomarkers of infection or vaccination, offering direct estimates of population immunity. This approach complements case data by providing a broader understanding of disease epidemiology. Wastewater surveillance, which detects pathogen genomes in sewage, captures infections across the entire population, including symptomatic, asymptomatic, and pre-/post-symptomatic individuals. This approach complements traditional case reporting by providing a broader, community-wide perspective on disease transmission.

In the first case study, I will discuss how we utilized serological data and a static cohort model to quantify the relative contributions of natural infection, routine vaccination, and supplementary immunization activities (SIAs) to measles seroconversion in Kenyan children. In the second case study we developed a simple static model combined with serological data to evaluate the effectiveness of SIAs in reducing the risk of a measles outbreak in the post-pandemic period. Finally, I will introduce my current work on wastewater-based epidemic modeling for mpox surveillance in British Columbia, demonstrating how wastewater and case data can be integrated within a dynamic transmission model to predict future scenarios of mpox outbreak.

These case studies illustrate the power of mathematical modeling in integrating multiple data sources to inform public health strategies and improve infectious disease control efforts.