Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python
File(s)
Published version
Author(s)
Irvine, MA
Hollingsworth, TD
Type
Journal Article
Abstract
Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.
Date Issued
2018-12-01
Date Acceptance
2018-05-24
Citation
Epidemics, 2018, 25, pp.80-88
ISSN
1755-4365
Publisher
Elsevier
Start Page
80
End Page
88
Journal / Book Title
Epidemics
Volume
25
Copyright Statement
© 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)
Subjects
Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
Approximate Bayesian computation
Individual-based model
Lymphatic filariasis
Model fitting
Python library
LYMPHATIC FILARIASIS
TRANSMISSION
ELIMINATION
INFECTION
IMPACT
Approximate Bayesian computation
Individual-based model
Lymphatic filariasis
Model fitting
Python library
Bayes Theorem
Communicable Diseases
Computer Simulation
Elephantiasis, Filarial
Humans
Spatial Analysis
Humans
Communicable Diseases
Elephantiasis, Filarial
Bayes Theorem
Computer Simulation
Spatial Analysis
1103 Clinical Sciences
1117 Public Health and Health Services
Publication Status
Published
Date Publish Online
2018-05-26