Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python

Authored by Michael A Irvine, T Deirdre Hollingsworth

Date Published: 2018

DOI: 10.1016/j.epidem.2018.05.009

Sponsors: No sponsors listed

Platforms: Python

Model Documentation: Other Narrative

Model Code URLs: https://github.com/sempwn/ABCPRC

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.

Tags

Individual-based model Infection Elimination transmission Impact Approximate bayesian computation Lymphatic filariasis Model fitting Lymphatic filariasis Python library