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Mapping malaria incidence distribution that accounts for environmental factors in Maputo Province - Mozambique
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Eduardo Mondlane University, Moçambique.
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 2
2010 (English)In: Malaria Journal, ISSN 1475-2875, E-ISSN 1475-2875, Vol. 9, 79Article in journal (Refereed) Published
Abstract [en]

Background: The objective was to study if an association exists between the incidence of malaria and some weather parameters in tropical Maputo province, Mozambique. Methods: A Bayesian hierarchical model to malaria count data aggregated at district level over a two years period is formulated. This model made it possible to account for spatial area variations. The model was extended to include environmental covariates temperature and rainfall. Study period was then divided into two climate conditions: rainy and dry seasons. The incidences of malaria between the two seasons were compared. Parameter estimation and inference were carried out using MCMC simulation techniques based on Poisson variation. Model comparisons are made using DIC. Results: For winter season, in 2001 the temperature covariate with estimated value of -8.88 shows no association to malaria incidence. In year 2002, the parameter estimation of the same covariate resulted in 5.498 of positive level of association. In both years rainfall covariate determines no dependency to malaria incidence. Malaria transmission is higher in wet season with both covariates positively related to malaria with posterior means 1.99 and 2.83 in year 2001. For 2002 only temperature is associated to malaria incidence with estimated value 2.23. Conclusions: The incidence of malaria in year 2001, presents an independent spatial pattern for temperature in summer and for rainfall in winter seasons respectively. In year 2002 temperature determines the spatial pattern of malaria incidence in the region. Temperature influences the model in cases where both covariates are introduced in winter and summer season. Its influence is extended to the summer model with temperature covariate only. It is reasonable to state that with the occurrence of high temperatures, malaria incidence had certainly escalated in this year.

Place, publisher, year, edition, pages
2010. Vol. 9, 79
National Category
Computer and Information Science
Research subject
Statistics; Computer and Systems Sciences
URN: urn:nbn:se:su:diva-49239DOI: 10.1186/1475-2875-9-79ISI: 000276657300001OAI: diva2:376746
Available from: 2010-12-13 Created: 2010-12-13 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.
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
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)
Available from: 2015-11-24 Created: 2015-11-08 Last updated: 2015-12-14Bibliographically approved

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Zacarias, Orlando P.Andersson, Mikael
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