Time-series analysis of Manila City morbidity data through autoregressive integrated moving average (ARIMA) using R Studio and XLSTAT
Date of Publication
1-2019
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Premed Physics
Subject Categories
Environmental Health and Protection
College
College of Science
Department/Unit
Physics
Thesis Adviser
Edgar A. Vallar
Defense Panel Chair
Ofelia Rempillo
Defense Panel Member
Gwen Castillo
Gian Bernardo
Abstract/Summary
Pollution in the city of Manila is increasing and the rates for respiratory and cardiovascular diseases are rising as well. Development of preventive programs and damage controls can be attained with the guidance of forecasting through time-series analysis. Such analysis is heavily dependent on the data provided by the local agency unit, therefore, integrating health data organization with time-series analysis can be of great benefit to the community subjected in a polluted setting. Researchers used R studio and XLST AT in performing autoregressive integrated moving average (ARIMA) modeling in forecasting the respiratory and cardiovascular diseases responsible for the morbidity in Manila. The study used weeks of 2012 - 2016 for weekly and by gender forecasting, and annual record from 2003 - 2012 for yearly and optimized forecasting. The processes resulted to models which can be calibrated for future use. Upon forecasting for weekly and yearly time-series, predictions showed that longer observations with heavier weights or value provide more accurate and reliable results. Moreover, forecasting by gender illustrated that males are more susceptible to diseases than females. Results also depicted that acute respiratory infection (ARI), pneumonia, and tuberculosis are the most prevalent among the diseases selected for this study. In the course of the study, R Studio provided more reliable results compared with XLSTAT in terms of MAPE values. For future use, ARIMA models should be calibrated every now and then as the data increases in order to produce more reliable, and more accurate results. It is also recommended, especially to local agencies, to maintain a consistent profiling of morbidity and mortality in the city. Organization of health data records is vital in analysis, therefore, regular recording and census must be done.
Abstract Format
html
Language
English
Format
Accession Number
TU23328
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Keywords
Air quality management—Philippines—Manila; Air—Pollution—Physiological effect; Diseases—Reporting—Philippines—Manila
Recommended Citation
Lenon, G. M., & Martinez, K. D. (2019). Time-series analysis of Manila City morbidity data through autoregressive integrated moving average (ARIMA) using R Studio and XLSTAT. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/18601
Embargo Period
2-3-2023