Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Abstract

Regression models are the standard approaches used in infectious disease epidemiology, but have limited ability to represent causality or complexity. We explore Bayesian networks (BNs) as an alternative approach for modelling infectious disease transmission, using leptospirosis as an example. Data were obtained from a leptospirosis study in Fiji in 2013. We compared the performance of naïve versus expert-structured BNs for modelling the relative importance of animal species in disease transmission in different ethnic groups and residential settings. For BNs of animal exposures at the individual/household level, R 2 for predicted versus observed infection rates were 0.59 for naïve and 0.75-0.93 for structured models of ethnic groups; and 0.54 for naïve and 0.93-1.00 for structured models of residential settings. BNs provide a promising approach for modelling infectious disease transmission under complex scenarios. The relative importance of animal species varied between subgroups, with important implications for more targeted public health control strategies.

Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji

“…Livestock are commonly kept for both commercial and subsistence purposes. Contact with specific livestock species varies between ethnic groups and urban/rural settings [ 14 ]. Fijians have varying access to education and basic services such as electricity and metered water (treated water supplied to houses), particularly between rural and urban areas.…”

“…Structures can be machine-learned, such as a “tree augmented naïve” (TAN) network, in which every variable has the target node and at most one other node as a parent node [ 19 ]; or expert structured, where variables and links are defined by the modeler based on knowledge about disease transmission and/or the research question(s) being asked. Structured networks have been shown to improve the predictive performance of BNs by taking into account the complex interactions between predictor variables, including in a previous study of leptospirosis in Fiji [ 14 ]. BNs were implemented in the Netica software [ 29 ].…”

“…Typically, urbanisation in developing countries results in densely populated areas with high poverty levels, poor dwelling construction and low education rates, such as the favelas of Brazil [ 12 ]. However, in other countries such as Fiji, urban dwellers have a lower risk of infection than those in rural areas, mostly due to differences in exposure to subsistence livestock animals [ 13 , 14 ]. Livestock including pigs, cattle and sheep are known to be important reservoirs [ 2 , 10 , 15 – 17 ], although exposure to these animals differs across communities [ 14 ] and even for different individuals within the same communities.…”

“…Bayesian networks (BNs) are a machine learning technique [ 19 ] commonly used in creating decision support systems in numerous fields including environmental management [ 20 – 22 ], and health [ 14 , 23 , 24 ]. BNs are better suited than regression models for assessing complex systems and outcomes under different scenarios [ 14 , 25 ]. Scenario analysis using BNs is facilitated by a graphical interface which allows decision makers to interact directly with the model, define scenarios (including ones with multiple strongly correlated variables) and explore outcomes.…”

Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

“…Bayesian Networks (BNs) are a type of probabilistic decision support tool [14], and are widely used in a variety of applications ranging from environmental management and ecology [7,20,21,27] to medicine [17,26]. While BNs offer a range of advantages such as the ability to deal with missing data [13], explicitly model interactions between the predictors [17], and incorporate expert opinions into the model parameters [14], there are several decisions that can affect model performance and increase the chance of spurious predictions and limit the usefulness of the technique to novice practitioners. Two of the key decisions are the structure of the network and the discretisation of continuous nodes.…”