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1. Integrated modelling of the determinants of household food insecurity during the 2020–2021 COVID-19 lockdown in Uganda

Author

Henry Musoke Semakula, Song Liang, Sarah Lindley McKune, Paul Isolo Mukwaya, Frank Mugagga, Denis Nseka, Hannington Wasswa, Patrick Kayima, Simon Peter Achuu, Patrick Mwendwa, and Jovia Nakato

Subjects
COVID-19, Bayesian belief networks, Food insecurity, Lockdown, Uganda, Agriculture, Nutrition. Foods and food supply, TX341-641
Abstract

Abstract Background The determinants of household food insecurity (HFI) do not act in isolation, and are known to be complex, stochastic, nonlinear, and multidimensional. Despite this being especially true in periods of shocks, studies that focus on integrated modelling of the HFI determinants during the COVID-19 lockdown are scarce, with no available evidence on Uganda. The main objective of this study was to develop Bayesian belief network (BBN) models to analyse, rank, and illustrate the conceptual reasoning, and complex causal relationships among the determinants of HFI during the COVID-19 lockdown. This study was based on seven rounds of Uganda’s High-Frequency Phone Surveys data sets collected during the lockdown. A total of 15,032 households, 17 independent determinants of HFI, and 8 food security indicators were used in this study. Metrics of sensitivity, and prediction performance were used to evaluate models’ accuracy. Results Eight BBN models were developed for each food insecurity indicator. The accuracy rates of the models ranged between 70.5% and 93.5%, with an average accuracy rate of 78.5%, indicating excellent predictive performance in identifying the determinants of HFI correctly. Our results revealed that approximately 42.2% of the sampled households (n = 15,032) in Uganda were worried about not having enough food. An estimated 25.2% of the respondents reported skipping a meal, while 32.1% reported consuming less food. Less than 20% of the households experienced food shortage, hunger, or having nothing to eat. Overall, 30.6% of the households were food insecure during the lockdown. The top five ranked determinants of HFI were identified as follows: (1) households’ inability to produce enough food; (2) households’ inability to buy food; (3) reduced household income; (4) limited cash assistance, and (5) households’ inability to stock adequate food supplies. Conclusions Ranking, rather than the statistical significance of the determinants of HFI, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted interventions within the constraints of limited funding. These findings emphasize the importance of intervening on the most highly ranked determinants of HFI to enhance the resilience of local food systems, and households’ capacity to cope with recurring and unforeseen shocks.

Published
2024
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2. Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

Author

Henry Musoke Semakula, Song Liang, Paul Isolo Mukwaya, Frank Mugagga, Denis Nseka, Hannington Wasswa, Patrick Mwendwa, Patrick Kayima, Simon Peter Achuu, and Jovia Nakato

Subjects
Bayesian belief network, Children, Malaria, Ranking, Refugees, Risk factors, Arctic medicine. Tropical medicine, RC955-962, Infectious and parasitic diseases, RC109-216
Abstract

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, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises). Conclusion Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Published
2023
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3. Prediction of future malaria hotspots under climate change in sub-Saharan Africa

Author

Paul Isolo Mukwaya, Simon Peter Achuu, Jingwen Chen, Guobao Song, Henry Musoke Semakula, Patrick Mwendwa, Jannette Abalo, Shushen Zhang, Martin Oulu, and Miaogen Shen

Subjects
0301 basic medicine, Atmospheric Science, Sub saharan, 010504 meteorology & atmospheric sciences, Meteorology, Climate change, 01 natural sciences, West africa, law.invention, 03 medical and health sciences, law, parasitic diseases, medicine, 0105 earth and related environmental sciences, Global and Planetary Change, High prevalence, biology, Plasmodium falciparum, Representative Concentration Pathways, biology.organism_classification, medicine.disease, 030104 developmental biology, Geography, Transmission (mechanics), Cartography, Malaria
Abstract

Malaria is a climate sensitive disease that is causing rampant deaths in sub-Saharan Africa (SSA) and its impact is expected to worsen under climate change. Thus, pre-emptive policies for future malaria control require projections based on integrated models that can accommodate complex interactions of both climatic and non-climatic factors that define malaria landscape. In this paper, we combined Geographical Information System (GIS) and Bayesian belief networks (BBN) to generate GIS-BBN models that predicted malaria hotspots in 2030, 2050 and 2100 under representative concentration pathways (RCPs) 4.5 and 8.5. We used malaria data of children of SSA, gridded environmental and social-economic data together with projected climate data from the 21 Coupled Model Inter-comparison Project Phase 5 models to compile the GIS-BBN models. Our model on which projections were made has an accuracy of 80.65% to predict the high, medium, low and no malaria prevalence categories correctly. The non-spatial BBN model projection shows a moderate variation in malaria reduction for the high prevalence category among RCPs. Under the low prevalence category, an increase in malaria is seen but with little variation ranging between 4.6 and 5.6 percentage points. Spatially, under RCP 4.5, most parts of SSA will have medium malaria prevalence in 2030, while under RCP 8.5, most parts will have no malaria except in the highlands. Our BBN-GIS models show an overall shift of malaria hotspots from West Africa to the eastern and southern parts of Africa especially under RCP 8.5. RCP 8.5 will not expand the high and medium malaria prevalence categories in all the projection years. The generated probabilistic maps highlight future malaria hotspots under climate change on which pre-emptive policies can be based.

Published
2017

4. 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

Author

Simon Peter Achuu, Henry Musoke Semakula, Guobao Song, and Shushen Zhang

Subjects
Environmental Engineering, Ecological Modeling, 030231 tropical medicine, Psychological intervention, Bayesian network, Malaria parasitaemia, medicine.disease, Educational attainment, 03 medical and health sciences, 0302 clinical medicine, Geography, Acquired immunodeficiency syndrome (AIDS), Environmental health, parasitic diseases, Econometrics, medicine, Predictive power, Residence, 030212 general & internal medicine, Software, Malaria
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. Display Omitted 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.

Published
2016

5. Potential of household environmental resources and practices in eliminating residual malaria transmission: a case study of Tanzania, Burundi, Malawi and Liberia

Author

Guobao Song, Simon Peter Achuu, Henry Musoke Semakula, and Shushen Zhang

Subjects
Male, Insecticides, Malawi, Mosquito Control, Burundi, Indoor residual spraying, Environment, Environmental resource, Tanzania, Malaria transmission, Environmental protection, Risk Factors, Environmental health, parasitic diseases, medicine, Animals, Humans, Malaria risk, Insecticide-Treated Bednets, Residual transmission, Child, Insecticide treated nets, Family Characteristics, biology, business.industry, Infant, malaria risk, residual transmission, household environmental resources and practices, insecticide-treated nets, indoor-residual spraying, General Medicine, Articles, medicine.disease, biology.organism_classification, Liberia, Insect Vectors, Malaria, Child, Preschool, Cattle, Female, business
Abstract

Background : The increasing protection gaps of insecticide-treated nets and indoor-residual spraying methods against malaria have led to an emergence of residual transmission in sub-Saharan Africa and thus, supplementary strategies to control mosquitoes are urgently required. Objective : To assess household environmental resources and practices that increase or reduce malaria risk among children under-five years of age in order to identify those aspects that can be adopted to control residual transmission. Methods : Household environmental resources, practices and malaria test results were extracted from Malaria Indicators Survey datasets for Tanzania, Burundi, Malawi and Liberia with 16,747 children from 11,469 households utilised in the analysis. Logistic regressions were performed to quantify the contribution of each factor to malaria occurrence. Results : Cattle rearing reduced malaria risk between 26%-49% while rearing goats increased the risk between 26%-32%. All piped-water systems reduced malaria risk between 30%-87% (Tanzania), 48%-95% (Burundi), 67%-77% (Malawi) and 58%- 73 (Liberia). Flush toilets reduced malaria risk between 47%-96%. Protected-wells increased malaria risk between 19%-44%. Interestingly, boreholes increased malaria risk between 19%-75%. Charcoal use reduced malaria risk between 11%-49%. Conclusion : Vector control options for tackling mosquitoes were revealed based on their risk levels. These included cattle rearing, installation of piped-water systems and flush toilets as well as use of smokeless fuels. Keywords : malaria risk, residual transmission, household environmental resources and practices, insecticide-treated nets, indoor-residual spraying

Published
2015

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