Dr. Nicholas Reich, assistant professor of biostatistics at the University of Massachusetts-Amherst School of Public Health and Health Sciences, recently received a two-year, $454,385 grant from the National Institutes of Health (NIH) for a project titled “Inference for interacting pathogens with mechanistic and phenomenological models.”
[Photo: Dr. Nicholas Reich]
Dr. Reich will be developing new statistical models to characterize complex immunological interactions between multiple strains of disease. Understanding these relationships, Dr. Reich notes, plays a vital role in clinical and public health decision-making. For example, a common form of pathogen interaction is short-term immunity or cross-protection from subsequent infection with the same or another pathogen. Another form of interaction is immune enhancement, where infection with one pathogen makes an individual more susceptible to infection or more likely to have severe clinical disease if infected a second time. Understanding how these interactions impact the spread of disease in populations can inform the strategies for designing and testing new vaccines. Since the underlying biological mechanisms about these interactions are not fully understood, this work could suggest hypotheses about how disease progresses at the individual level.
As part of the project, Dr. Reich will develop three new statistical models: a three-pathogen interaction model that includes influenza A, influenza B, and RSV surveillance data to estimate the duration and strength of cross-protection between these three pathogens; a four-pathogen interaction model to dengue surveillance data to estimate the duration and strength of both cross-protection and enhancement between the four serotypes of dengue; and a more general statistical framework for comparing inferences from mechanistic and phenomenological models of multiple interacting time series.
The work is expected to provide the first explicit estimates of cross-protection between influenza A, influenza B, and RSV. Additionally, this work will provide the first explicit estimates of immune enhancement between the four serotypes of dengue. Finally, the study will provide new information on how to best draw statistical inference with mechanistic and phenomenological models in a wide array of scientific settings.
Methods developed in the course of this work will be freely disseminated as R software packages on the Comprehensive R Archive Network.