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Spatial and temporal patterns of malaria incidence in Mozambique
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Eduardo Mondlane University, Mozambique.
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 2
2011 (English)In: Malaria Journal, ISSN 1475-2875, E-ISSN 1475-2875, Vol. 10, 189Article in journal (Refereed) Published
Abstract [en]

Background: The objective of this study is to analyze the spatial and temporal patterns of malaria incidence as to determine the means by which climatic factors such as temperature, rainfall and humidity affect its distribution in Maputo province, Mozambique. Methods: This study presents a model of malaria that evolves in space and time in Maputo province-Mozambique, over a ten years period (1999-2008). The model incorporates malaria cases and their relation to environmental variables. Due to incompleteness of climatic data, a multiple imputation technique is employed. Additionally, the whole province is interpolated through a Gaussian process. This method overcomes the misalignment problem of environmental variables (available at meteorological stations points) and malaria cases (available as aggregates for every district - area). Markov Chain Monte Carlo (MCMC) methods are used to obtain posterior inference and Deviance Information Criteria (DIC) to perform model comparison. Results: A Bayesian model with interaction terms was found to be the best fitted model. Malaria incidence was associated to humidity and maximum temperature. Malaria risk increased with maximum temperature over 28 degrees C (relative risk (RR) of 0.0060 and 95% Bayesian credible interval (CI) of 0.00033-0.0095) and humidity (relative risk (RR) of 0.00741 and 95% Bayesian CI 0.005141-0.0093). The results would suggest that additional non-climatic factors including socio-economic status, elevation, etc. also influence malaria transmission in Mozambique. Conclusions: These results demonstrate the potential of climate predictors particularly, humidity and maximum temperature in explaining malaria incidence risk for the studied period in Maputo province. Smoothed maps obtained as monthly average of malaria incidence allowed to visualize months of initial and peak transmission. They also illustrate a variation on malaria incidence risk that might not be related to climatic factors. However, these factors are still determinant for malaria transmission and intensity in the region.

Place, publisher, year, edition, pages
2011. Vol. 10, 189
National Category
Computer and Information Science Mathematics
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-66560DOI: 10.1186/1475-2875-10-189ISI: 000294260800001OAI: diva2:468958
Available from: 2011-12-22 Created: 2011-12-20 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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