How can data help us to better understand and respond to the synchronization of infectious outbreaks?
This seminar from the Center for Quantitative Methods and Data Sciences (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, occurred on Wednesday, March 31 from 2-3pm via Zoom. The topic of this month's webinar was "To everything there is a season: synchronization of infectious outbreaks." Click enroll to view the archived recording from this webinar.
A marked seasonality in many infections, like influenza or salmonellosis, is a well-known phenomenon. When we observe a pronounced seasonal pattern, it gives us a reason to expect high predictability of high or low disease incidence periods in a calendar year. With the expansion of national and global surveillance systems, the opportunities to better understand the local, regional, and global temporal fluctuations are also growing. As we learn more about the seasonality of many infections, and is reasonable to expect that some will co-occur. Yet, patterns of co-occurrences and factors driving such synchronization remain elusive.
In this talk, Dr. Naumova demonstrated the methodology developed to assess the extent, lag, and directionality of seasonal synchronization. Dr. Naumova will provide several examples using national databases, such as the CDC’s Foodborne Disease Active Surveillance Network (FoodNet), National Outbreak Reporting System (NORS), and the FluNet supported by the WHO to illustrate seasonal synchronizations among foodborne infections and the challenges of time-referenced surveillance data. The modeling approaches include the trend-adjusted mixed effects nonlinear harmonic regression models and the delta-method to derive the estimates and confidence intervals for the seasonal peak timing and amplitude, allowing us to build local, regional, and global disease calendars. The methodological rigor, standardization, and data harmonization across surveillance systems are enabling comprehensive characterization of disease seasonality and serve as a pathway for implementing the Precision Public Health, Nutrition, and Medicine principles to tailor prevention and intervention strategies.
Faculty: Elena Naumova, PhD is Professor and Chair of the Division of Nutrition Epidemiology and Data Science at the Friedman School of Nutrition Science and Policy at Tufts University. Dr. Naumova's area of expertise is in developing methodology for modeling of transient processes with applications in environmental epidemiology, nutrition, infectious diseases, and public health. As a mathematician by training, she designs statistical, computational and mathematical models to characterize and forecast infectious outbreaks. Dr. Naumova is using large-scale data sources to study infections sensitive to climate variations and extreme weather. She led research programs in emerging biomedical fields of epidemiology, immunogenetics, nutrition and growth, nationally and internationally to set new standards for public health investigations. Dr. Naumova is serving as Editor-in-Chief for the Journal of Public Health Policy (Nature Publishing Group). She is currently funded by the NSF to develop ways to train data-savvy workforce, highlight advancements and challenges of data revolution, share examples where the data analytics and data visualizations enhance our knowledge and help to find solutions to wicked problems. Dr. Naumova hopes to stir the discussion on how data scientists have to rethink and reframe the state-of-the-art methodology to enable the discovery of emerging trends in global health fields.