Multivariate Time Series Analysis of Lung and Colon Cancer Mortality in Jamaica and the U.S.

Music City Center 

Lung and colon cancers are leading causes of mortality worldwide, with variations across healthcare systems. This study uses multivariate time series modeling to analyze lung and colon cancer mortality trends in Jamaica and the U.S. from 1960 to 2014, applying Vector Autoregressive Moving Average (VARMA) models to assess interdependence. Country-specific multivariate forecasts extend 12 years beyond 2014, identifying disparities, similarities, and influencing factors. Model selection and validation use statistical metrics like MAPE, RMSE, and AIC to ensure accuracy. Monte Carlo simulations enhance predictive robustness by accounting for future variability. This research provides data-driven insights into cancer mortality trends, contributing to the development of advanced statistical models for understanding and forecasting cancer outcomes. Findings will support public health planning and policy development in both regions.

Cancer Mortality

Time Series Analysis, VARMA, Multivariate Forecasting

Monte Carlo Simulation, Predictive Analytics

Public Health

Geographic Analysis: Jamaica, United States 

Section on Statistics and Data Science Education