Evaluating causes of mumps reemergence with computational modeling

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PROJECT SUMMARY/ABSTRACT For millennia, infectious diseases caused acute illness, long-term complications, and death. The invention of safe and effective vaccines revolutionized health care, allowing individuals to mount protective immunity against pathogens without suffering from illness and complications. But, recently, vaccine-preventable diseases are reemerging nationwide. My long-term goal is to identify processes that allow continued transmission of vaccine-preventable diseases among humans to improve and maintain efficacy of large-scale vaccination programs using computational infectious disease modeling. The objective of this proposal is to inform and maintain the efficacy of large-scale vaccination by investigating recent mumps reemergence. Current research suggests two possible causes for mumps reemergence: vaccine underutilization and vaccine mismatch. This proposal will use age-structured computational models of mumps transmission and vaccination calibrated with disease, demographic, and RNA sequence data to determine the relationship between vaccine utilization and mumps outbreaks (aim 1) and evaluate effectiveness of the mumps vaccine over time (aim 2). The proposed research will provide science-based recommendations to improve the mumps vaccination strategy (boosters and/or vaccine reformulations). The proposed research will also generate computational tools including calibrated models of mumps transmission. Finally, this project will train an investigator to transition to use computational models for research on infectious diseases that affect humans. To develop expertise, the candidate will model a reemerging infectious disease that affects humans (training aim 1), interpret computational infectious disease modeling results in their public health context (training aim 2), deepen knowledge of virus important for human health, human immune responses, and vaccination (training aim 3), and develop skills needed to execute a nationwide study modeling reemerging vaccine-preventable infectious diseases (training aim 4). Training aims will be met through coursework, guided readings, guided data tutorials, conference attendance, seminars, and research exchanges, and guided research. A highly qualified team of mentors and collaborators with complementary expertise will guide research and training agendas. This training plan will establish an independent research program using computational infectious disease modeling in a combined viral ecology/evolutionary framework that will be used in an R01 proposal investigating anticipated state and regional differences in identified mumps drivers. Together, this research will scientifically inform more effective vaccination strategies. This proposal addresses several of NIAID’s research priorities: (1) modeling of infectious disease agents, (2) maintaining immunity after vaccination, (3) understanding the human immune system, (4) understanding early development of the immune system, and (5) understanding immunity in the elderly.