A Bayesian belief network modelling of household factors influencing the risk of malaria: A study of parasitaemia in children under five years of age in sub-Saharan Africa. (January 2016)

• Record Type: Journal Article
• Authors: Semakula, Henry Musoke; Song, Guobao; Achuu, Simon Peter; Zhang, Shushen
• Is Part Of: Environmental modelling & software. Volume 75(2016:Jan.)
• Page Start: 59
• Page End: 67
• Publication Date: 2016-01
• Subjects: Malaria parasitaemia -- Bayesian belief network -- Household factors -- Children -- Sub-Saharan Africa; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.10.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Studies that focus on integrated modelling of household factors and the risk for malaria parasitaemia among children in sub-Saharan Africa (SSA) are scarce. By using Malaria Indicator Survey, Demographic Health Survey, AIDS Indicator Survey datasets, expert knowledge and existing literature on malaria, a Bayesian belief network (BBN) model was developed to bridge this gap. Results of sensitivity analysis indicate that drinking water sources, household wealth, nature of toilet facilities, mother's educational attainment, types of main wall, and roofing materials, were significant factors causing the largest entropy reduction in malaria parasitaemia. Cattle rearing and residence type had less influence. Model accuracy was 86.39% with an area under the receiver-operating characteristic curve of 0.82. The model's spherical payoff was 0.80 with the logarithmic and quadratic losses of 0.53 and 0.35 respectively indicating a strong predictive power. The study demonstrated how BBN modelling can be used in determining key interventions for malaria control. Graphical abstract: Highlights: A Bayesian Belief Networks model is developed from household factors to predict malaria parasitaemia risk among children. Datasets of Malaria Indicator Survey, Demographic Health Survey and AIDS indicator survey are used to compile the model. Malaria parasitaemia risk increases in households using borehole water, dug wells and surface water points. A BBN model can be used in determining key interventions for malaria control.