Predicting the Incidence of Malaria Cases in Mozambique Using Regression Trees and Forests
2013 (English)In: International Journal of Computer Science and Electronics Engineering (IJCSEE), ISSN 2320-401X, Vol. 1, no 1, 50-54 p.Article in journal (Refereed) Published
Malaria remains a significant public health concern in Mozambique with disease cases reported in almost every province. This study investigates the prediction models of the number of malaria cases in districts of Maputo province. Used data include administrative districts, malaria cases, indoor residual spray and climatic variables temperature, rainfall and humidity. Regression trees and random forest models were developed using the statistical tool R, and applied to predict the number of malaria cases during one year, based on observations from preceding years. Models were compared with respect to the mean squared error (MSE) and correlation coefficient. Indoor Residual Spray (IRS), month of January, minimal temperature and rainfall variables were found to be the most important factors when predicting the number of malaria cases, with some districts showing high malaria incidence. Additionally, by reducing the time window for what historical data to take into account, predictive performance can be increased substantially.
Place, publisher, year, edition, pages
2013. Vol. 1, no 1, 50-54 p.
malaria, regression trees, regression forests
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-86341OAI: oai:DiVA.org:su-86341DiVA: diva2:586645