Estimating the Impact of Prevention Action: A simulation Model of Cervical Cancer Progression

Michal Rosen-Zvi, Lavi Shpigelman, Alan Kalton, Omer Weissbrod, Saheed Akindeinde, Soren Benefeldt, Andrew Bentley, Terry Everett, Joseph Jajinskiji, Emmanuel Kweyu, Chalapathy Neti, Joe Saab, Osamuyimen Stewart, Malcolm Ward, Guo Tong Xie

Abstract: Cervical cancer is one of the highest occurring cancers for women in East Africa. Many studies have shown that disease occurrences and particularly the number of deaths due to the disease can be reduced significantly by screening and vaccination. East Africa and Kenya in particular are undergoing change and taking actions to reduce disease levels. However, up until today disease level in the different districts in Kenya is not known nor what be the prevalence of disease when prevention actions take place. In this paper we propose a novel Bayesian model for estimating disease levels based on available partial reports and demographic information. The result is a simulation engine that provides estimations of the impact of various potential prevention actions.