ArticleRoyal Society open science2021
Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Understanding Nash epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.Sensors (Basel, Switzerland) · 2023Article
- Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation.Infectious Disease Modelling · 2021Article
- Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.PloS one · 2021Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum
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