ArticleEpidemics2018
Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.
Article in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Leveraging Synthetic Clinical Data for Validation and Operational Readiness in Clinical Trials.Healthcare (Basel, Switzerland) · 2026Article
- Fitting a lattice model with local and global transmission to spread of a plant disease.PLoS computational biology · 2026Article
- Study on the spatial-temporal evolution and influencing factors of AIDS incidence in China from 2006 to 2022.BMC infectious diseases · 2025Article
- Comparative analysis of epidemiological and Spatiotemporal patterns in seasonal influenza and COVID-19 outbreaks.Scientific reports · 2025Article
- Defining a prevalence level to describe the elimination of Lymphatic Filariasis (LF) transmission and designing monitoring & evaluating (M&E) programmes post the cessation of mass drug administration (MDA).PLoS neglected tropical diseases · 2020Article
- The use of mixture density networks in the emulation of complex epidemiological individual-based models.PLoS computational biology · 2020Article
- Modelling exposure heterogeneity and density dependence in onchocerciasis using a novel individual-based transmission model, EPIONCHO-IBM: Implications for elimination and data needs.PLoS neglected tropical diseases · 2019Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.