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Predicting the Incidence of Malaria Cases in Mozambique Using Regression Trees and Forests
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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
Abstract [en]

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.
Keyword [en]
malaria, regression trees, regression forests
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-86341OAI: oai:DiVA.org:su-86341DiVA: diva2:586645
Available from: 2013-01-12 Created: 2013-01-12 Last updated: 2015-11-09Bibliographically approved
In thesis
1. Mining Mozambique Health Data: The Case of Malaria: From Bayesian Incidence Risk to Incidence Case Predictions
Open this publication in new window or tab >>Mining Mozambique Health Data: The Case of Malaria: From Bayesian Incidence Risk to Incidence Case Predictions
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The health sector in Mozambique is piled with data, holding records of major public health diseases, such as malaria, cholera, etc. The process of scrutinizing such a mass of health data for useful information is challenging but essential for the health authorities and professionals. Statistical learning and inferential approaches can be used to provide health decision makers with appropriate tools for disease diagnosis and assessment, where the analysis is performed using Bayesian predictive techniques and data mining. The purpose of this thesis is to investigate how predictive data mining and Bayesian regression methods can be used effectively, so as to extract useful knowledge from reported malaria health data to support decision making and management. 

In summary, effective Bayesian predictive methods based on spatial and space-time reported cases of malaria have been derived, allowing the extraction of the main risk factors for malaria. Predictive models that combine consecutive temporal connections within the analysis of the space-time variations of the disease have been found to be relevant when the explicit modeling of seasonality is not required or is even unfeasible.

Investigation of the most effective ways to derive numerical predictive models was performed using several regression predictive methods. The conclusions are that effective numerical prediction of new cases of the disease can be achieved by training support vector machines using a time-window approach for the choice of different training sets based on a number of years and reducing the time towards the test set. The best performance is obtained for a smaller time-window. Another contribution of this thesis is the determining of the importance of predictors in the prediction of the incidence of malaria, performed by adopting the permutation accuracy strategy (from the random forests method) using the test set. Also, an additional contribution relates to a significant reduction in the predictive error, which has been obtained by the employment of a sample correction bias strategy, while testing the predictive models in different regions, other than where they were initially developed.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 93 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-020
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122672 (URN)978-91-7649-304-5 (ISBN)
Public defence
2015-12-16, Aula NOD, NOD-huset, Borgarfjordsgtan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2015-11-24 Created: 2015-11-08 Last updated: 2015-12-14Bibliographically approved

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