Abstract
Lung and colon cancers are among the leading causes of cancer-related mortality worldwide, with significant variations across different healthcare systems. This study applies multivariate time series modeling and forecasting to analyze lung and colon cancer mortality trends in Jamaica and the United States, examining each cancer type separately within each country and comparing trends between the two regions. The research employs Vector Autoregressive Moving Average (VARMA) models to assess the interdependence between lung and colon cancer mortality from 1960 to 2014 within Jamaica and the U.S. Multivariate forecasting will be performed separately for both countries to evaluate country-specific trends for 12 years beyond 2014. A comparative analysis will then be conducted to identify disparities, similarities, and potential factors influencing mortality differences between the two countries. Model selection and validation are conducted using statistical performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Akaike Information Criterion (AIC) to ensure the most accurate forecasting models. To enhance predictive robustness, Monte Carlo simulation techniques generate probabilistic forecasts, accounting for variability in future mortality trends. This study provides data-driven insights into lung and colon cancer mortality trends in Jamaica and the U.S. By integrating multivariate forecasting and simulation approaches, this research contributes to the development of advanced statistical models for understanding and predicting cancer mortality, aiding in future public health planning and policy development. Keywords: Cancer Mortality, Time Series Analysis, VARMA, Multivariate Forecasting, Monte Carlo Simulation, Predictive Analytics, Public Health, Geographic Analysis: Jamaica, U.S.
Start Time
16-4-2025 9:00 AM
End Time
16-4-2025 11:30 AM
Presentation Type
Poster
Presentation Category
Health
Student Type
Graduate Student - Masters
Faculty Mentor
Mostafa Zahed
Faculty Department
Mathematics and Statistics
Forecasting Lung and Colon Cancer Mortality Trends in Jamaica and the U.S. Using Multivariate Time Series Analysis
Lung and colon cancers are among the leading causes of cancer-related mortality worldwide, with significant variations across different healthcare systems. This study applies multivariate time series modeling and forecasting to analyze lung and colon cancer mortality trends in Jamaica and the United States, examining each cancer type separately within each country and comparing trends between the two regions. The research employs Vector Autoregressive Moving Average (VARMA) models to assess the interdependence between lung and colon cancer mortality from 1960 to 2014 within Jamaica and the U.S. Multivariate forecasting will be performed separately for both countries to evaluate country-specific trends for 12 years beyond 2014. A comparative analysis will then be conducted to identify disparities, similarities, and potential factors influencing mortality differences between the two countries. Model selection and validation are conducted using statistical performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Akaike Information Criterion (AIC) to ensure the most accurate forecasting models. To enhance predictive robustness, Monte Carlo simulation techniques generate probabilistic forecasts, accounting for variability in future mortality trends. This study provides data-driven insights into lung and colon cancer mortality trends in Jamaica and the U.S. By integrating multivariate forecasting and simulation approaches, this research contributes to the development of advanced statistical models for understanding and predicting cancer mortality, aiding in future public health planning and policy development. Keywords: Cancer Mortality, Time Series Analysis, VARMA, Multivariate Forecasting, Monte Carlo Simulation, Predictive Analytics, Public Health, Geographic Analysis: Jamaica, U.S.