Bayesian prior elicitation for malaria modelling

Abstract

Subjective Bayesian methods, which incorporate expert knowledge into disease modelling, remain underutilised in epidemiology. This is despite the growth of knowledge in statistical approaches to disease analysis. Objective priors are often favoured for their simplicity. However, subjective Bayesian approaches can produce more informative models by using expert insights, such as those related to malaria transmission. This study focuses on translating expert knowledge into prior probability distributions through a process known as prior elicitation. Prior elicitation presents several challenges. Converting expert judgments into probability distributions is complex and often requires specialised tools. Established methods like the Sheffield approach are resource-intensive, requiring considerable time and cognitive effort from both experts and researchers. To address these limitations, this study makes a major contribution by integrating the Analytic Hierarchy Process (AHP) with statistical validation techniques to quantify expert knowledge into prior probability distributions. Expert knowledge was collected through questionnaires and structured as pairwise comparisons. These were quantified into AHP weights, representing the relative importance of environmental factors influencing malaria transmission. The weights were then fitted to various probability distributions and evaluated using goodness-of-fit tests. Results showed that the beta, gamma and normal distributions best represented the elicited expert knowledge. These findings suggest that beta, gamma and normal distributions are suitable as prior distributions in Bayesian models of malaria transmission. By simplifying the elicitation process and reducing technical complexity, this approach offers a practical framework for applying subjective Bayesian methods in epidemiology. Future research will compare these elicited priors with objective priors to evaluate their impact on model performance across domains.

HIGHLIGHTS • Innovative Approach: Study uses AHP and statistical methods to enhance expert-informed Bayesian malaria models. • Expert Knowledge Gathering: Expert input on malaria factors was quantified as AHP weights via pairwise comparisons. • Fitting and Validation: AHP weights were fitted to distributions and assessed using goodness-of-fit tests. • Practical Method for Epidemiology: The method simplifies elicitation, aiding practical subjective Bayesian modelling in epidemiology. • Future Research Directions: Future work will compare elicited and objective priors to evaluate Bayesian model performance.