The focus of the Harvard Medical School MIDAS team is to develop space-time models to detect emerging outbreaks before they spread to most parts of the area under surveillance. The researchers will model the natural temporal and geographical variation in different types of electronic health system infectious disease data. These models help separate background noise from signals suggesting a true outbreak. The models will have a geographic range from hospital ward to an entire nation. The disease targets include antibiotic resistance phenotypes, specific pathogens and disease syndromes and symptoms that can be early indicators of naturally occurring pathogens or bioterrorism agents.
The researchers also will develop space-time early outbreak detection models for different types of infectious disease data and develop space-time models for monitoring the spread and/or decline of an occurring outbreak (for use after detection of an outbreak).
The team will evaluate the ability of the models to detect and monitor outbreaks using historical data for actual and simulated disease outbreaks. The simulated outbreaks will be based on infectious disease transmission models developed by other MIDAS groups.
Co-Principal Investigator: Martin Kulldorff, Ph.D.
Co-Investigators: Kenneth Kleinman, Sc.D., Deborah Yokoe, M.D., Susan Huang, M.D., Thomas O’Brien, M.D., John Stelling, M.D., and Katherine Yih, Ph.D., of Harvard Medical School; Alfred DeMaria, M.D., of the Massachusetts Department of Public Health; John Hsu, M.D., of Kaiser Permanente Northern California; Marcelo Galas, Ph.D., of the National Institute for Infectious Diseases, Argentina; and Louise Ryan, Ph.D., of the Harvard School of Public Health.