Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda
Abstract Background Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrate...
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ftdoajarticles:oai:doaj.org/article:0a8dc3767e32403482c7f70554dc18f3 2023-11-12T04:13:58+01:00 Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda Henry Musoke Semakula Song Liang Paul Isolo Mukwaya Frank Mugagga Denis Nseka Hannington Wasswa Patrick Mwendwa Patrick Kayima Simon Peter Achuu Jovia Nakato 2023-10-01T00:00:00Z https://doi.org/10.1186/s12936-023-04735-8 https://doaj.org/article/0a8dc3767e32403482c7f70554dc18f3 EN eng BMC https://doi.org/10.1186/s12936-023-04735-8 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-023-04735-8 1475-2875 https://doaj.org/article/0a8dc3767e32403482c7f70554dc18f3 Malaria Journal, Vol 22, Iss 1, Pp 1-14 (2023) Bayesian belief network Children Malaria Ranking Refugees Risk factors Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2023 ftdoajarticles https://doi.org/10.1186/s12936-023-04735-8 2023-10-15T00:39:51Z Abstract Background Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda. Methods In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance. Results Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 22 1 |
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ftdoajarticles |
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English |
topic |
Bayesian belief network Children Malaria Ranking Refugees Risk factors Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Bayesian belief network Children Malaria Ranking Refugees Risk factors Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Henry Musoke Semakula Song Liang Paul Isolo Mukwaya Frank Mugagga Denis Nseka Hannington Wasswa Patrick Mwendwa Patrick Kayima Simon Peter Achuu Jovia Nakato Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
topic_facet |
Bayesian belief network Children Malaria Ranking Refugees Risk factors Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda. Methods In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance. Results Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, ... |
format |
Article in Journal/Newspaper |
author |
Henry Musoke Semakula Song Liang Paul Isolo Mukwaya Frank Mugagga Denis Nseka Hannington Wasswa Patrick Mwendwa Patrick Kayima Simon Peter Achuu Jovia Nakato |
author_facet |
Henry Musoke Semakula Song Liang Paul Isolo Mukwaya Frank Mugagga Denis Nseka Hannington Wasswa Patrick Mwendwa Patrick Kayima Simon Peter Achuu Jovia Nakato |
author_sort |
Henry Musoke Semakula |
title |
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
title_short |
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
title_full |
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
title_fullStr |
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
title_full_unstemmed |
Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda |
title_sort |
bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in uganda |
publisher |
BMC |
publishDate |
2023 |
url |
https://doi.org/10.1186/s12936-023-04735-8 https://doaj.org/article/0a8dc3767e32403482c7f70554dc18f3 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 22, Iss 1, Pp 1-14 (2023) |
op_relation |
https://doi.org/10.1186/s12936-023-04735-8 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-023-04735-8 1475-2875 https://doaj.org/article/0a8dc3767e32403482c7f70554dc18f3 |
op_doi |
https://doi.org/10.1186/s12936-023-04735-8 |
container_title |
Malaria Journal |
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22 |
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1 |
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1782331736377524224 |