NSF Award
1722665
Chronic hepatitis C virus (HCV) infection is a global health problem that causes more deaths than other common infectious conditions, including HIV/AIDS, tuberculosis and malaria. Because the infection has a long latency period with few symptons, the majority of those infected remain unaware of their health status. The World Health Organization (WHO) and the National Academy of Medicine recently set an ambitious target to eliminate HCV by 2030; however, several challenges exist. The true prevalence of disease is unknown and effective screening policies do not exist, and treatment, while effective, is very expensive. This project has the following three overarching aims: 1) estimating the true disease burden of hepatitis C at the State and National level; 2) identifying effective screening and treatment strategies for identifying hepatitis C infection; and 3) designing practical decision support tools for use by stakeholders to manage the disease. From a societal perspective, the research will assist decision makers in reducing HCV disease burden. The team is engaged with the Centers for Disease Control and Prevention (CDC), State and County health departments, and the World Health Organization (WHO), who are the potential stakeholders of the research. This project will also provide learning and research experience to students and fellows on the application of systems dynamic modeling to healthcare.<br/><br/>This research will apply methodologies from epidemiology and operations research to provide decision support for the allocation of limited resources to effective intervention policies to contain the ongoing epidemic of HCV. The research team will address several important research questions related to hepatitis C management and control by 1) conducting surveys to estimate HCV prevalence in at-risk populations; 2) estimating the true HCV prevalence at the State and National level via a validated agent-based simulation model; 3) researching optimization-based approaches to dynamic allocation of adaptive intervention strategies. The research is expected to lead to practical decision support. The research will advance knowledge in mathematical and computational modeling of infectious diseases and guide the allocation of limited resources towards eliminating HCV as a global health threat.
