Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

Background Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy. Methods Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods. Results Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26; 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = − 0.01–0.02) and slope (β = 0.12; 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation. Conclusions BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.

Development of Drilling Fluid Advisory System Utilizing Bayesian Network

Using the right drilling fluid with optimal rheology and filtration properties is one of the most important factors in successful drilling and completion operations. Designing the right drilling fluid depends on a variety of factors viz. formation lithology, wellbore geometry, temperature, pressure, and drilling objectives. To the best of the author's knowledge there is no standard drilling fluid advisory system to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections. The paper describes a drilling fluid advisory system based on Artificial Bayesian Intelligence. The advisory system includes a Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. This advisory system has been designed to aid drilling engineers when designing drilling fluids for their operations. This paper describes a module that was created in this advisory system. This module was created based on several inputs viz. well geometry (vertical and horizontal), temperature, pressure, productivity. To create the drilling fluids module within the advisory system, a number of drilling fluid specialists/experts were interviewed to gather the information required to determine the best practices as a function of the above inputs. These best practices were then used to build decision trees that would allow the user to take an elementary data set and end up with a decision that honors the best practices. The designing process of this advisory system also included a number of standard lab tests that start from quality assurance, initial designing and finally using field samples to confirm the success of the application. The study also discusses several field cases that validate the drilling fluids advisory system. The novel drilling fluid advisory system based on Artificial Bayesian Intelligence has been designed to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections.

Use of artificial intelligence based models for learning better policy for maternal and child health

More than 640,000 babies died of sepsis before they reach the age of one month in India in 2016. Despite a large number of government schemes aimed at reducing this rate, this number still remains high because of the complexity and interplay of factors involved. Finding an optimum policy and solutions to this problem needs learning from data. We integrated diverse sources of data and applied Bayesian Artificial Intelligence methods for learning to mitigate sepsis and adverse pregnancy outcomes in India. In this project, we created models that combine the robustness of ensemble averaged Baeysian Networks with decision learning and impact evaluation by using simulations and counterfactual reasoning respectively. We will demonstrate the process of learning these models and how these led us to infer the pivotal role of Water, Sanitation and Hygiene for reducing Adverse Pregnancy Outcome and neonatal sepsis in the population studied. We will also demonstrate the creation of explainable AI models for complex public health challenges and their deployment with wiseR, our in-house, open source platform for doing end-to-end Bayesian Decision Network learning.

Learning to Address Health Inequality in the United States with a Bayesian Decision Network

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevitygap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensembleaveraged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stablefamilies within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.

Pre-Conditioning Approach to Bayesian Decision Networks for Water Quality Sensors Positioning in Urban Drainage Systems

In the last decades, the growth of mini- and micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Such pollutants are usually characterized by low removal efficiencies in urban wastewater treatment plants and they may have an acute or cumulative impact on environment. In order to facilitate early detection and efficient containment of the illicit intrusions, the present work aims to develop a decision-support approach for positioning the water quality sensors. It is mainly based on the use of a decision-making support of the BDN type (Bayesian Decision Network), specifically looking soluble conservative pollutants, such as metals. In the application and result section the methodology is tested on two sewer systems, with increasing complexity: a literature scheme from the SWMM manual and a real combined sewer.