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

Lau, Colleen L., Mayfield, Helen J., Lowry, John H., Watson, Conall H., Kama, Mike, Nilles, Eric J. and Smith, Carl S. (2017) Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: a case study of leptospirosis in Fiji. Environmental Modelling & Software, 97 November: 271-286. doi:10.1016/j.envsoft.2017.08.004


Author Lau, Colleen L.
Mayfield, Helen J.
Lowry, John H.
Watson, Conall H.
Kama, Mike
Nilles, Eric J.
Smith, Carl S.
Title Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: a case study of leptospirosis in Fiji
Journal name Environmental Modelling & Software   Check publisher's open access policy
ISSN 1364-8152
1873-6726
Publication date 2017-11-01
Year available 2017
Sub-type Article (original research)
DOI 10.1016/j.envsoft.2017.08.004
Open Access Status Not yet assessed
Volume 97
Issue November
Start page 271
End page 286
Total pages 16
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
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 naive 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 naive and 0.75-0.93 for structured models of ethnic groups; and 0.54 for naive 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. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword Bayesian networks
Infectious diseases epidemiology
Leptospirosis
Zoonoses
Environmental health
Public health
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 1109035
MR/J003999/1
2014003059
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Admin only - CHRC
Faculty of Medicine
UQ Business School Publications
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Created: Tue, 12 Sep 2017, 09:49:38 EST by Karen Morgan on behalf of UQ Business School