Models to predict the public health impact of vaccine resistance: A systematic review. Issue 35 (14th August 2019)

• Record Type: Journal Article
• Authors: Reid, Molly C.; Peebles, Kathryn; Stansfield, Sarah E.; Goodreau, Steven M.; Abernethy, Neil; Gottlieb, Geoffrey S.; Mittler, John E.; Herbeck, Joshua T.
• Is Part Of: Vaccine. Volume 37:Issue 35(2019)
• Page Start: 4886
• Page End: 4895
• Publication Date: 2019-08-14
• Subjects: Vaccine resistance -- Mathematical modeling; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2019.07.013
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: Pathogen evolution is a potential threat to the long-term benefits provided by public health vaccination campaigns. Mathematical modeling can be a powerful tool to examine the forces responsible for the development of vaccine resistance and to predict its public health implications. We conducted a systematic review of existing literature to understand the construction and application of vaccine resistance models. We identified 26 studies that modeled the public health impact of vaccine resistance for 12 different pathogens. Most models predicted that vaccines would reduce overall disease burden in spite of evolution of vaccine resistance. Relatively few pathogens and populations for which vaccine resistance may be problematic were covered in the reviewed studies, with low- and middle-income countries particularly under-represented. We discuss the key components of model design, as well as patterns of model predictions.