arXiv subjects

Paul J Birrell

Publications and source records attributed to Paul J Birrell.

5 recordsLinked to original sources

arXiv2024

Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal Methods

Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and severity dynamics. This paper explores important challenges to the use of state-space models for epidemic inference when multiple dependent datasets are analysed. We propose a new semi-stochastic model that exploits deterministic approximations for large-scale transmission dynamics while retaining stochasticity in the occurrence and reporting of relatively rare severe events. This model is suitable for many real-time situations including large seasonal epidemics and pandemics. Within this context, we develop algorithms to provide exact parameter inference and test them via simulation. Finally, we apply our joint model and the proposed algorithm to several surveillance data on the 2017-18 influenza epidemic in England to reconstruct transmission dynamics and estimate the daily new influenza infections as well as severity indicators such as the case-hospitalisation risk and the hospital-intensive care risk.

stat.AP↗

arXiv2024

Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates.

stat.AP↗

arXiv2020

HIV transmission in men who have sex with men in England: on track for elimination by 2030?

Background: After a decade of a treatment as prevention (TasP) strategy based on progressive HIV testing scale-up and earlier treatment, a reduction in the estimated number of new infections in men-who-have-sex-with-men (MSM) in England had yet to be identified by 2010. To achieve internationally agreed targets for HIV control and elimination, test-and-treat prevention efforts have been dramatically intensified over the period 2010-2015, and, from 2016, further strengthened by pre-exposure prophylaxis (PrEP). Methods: Application of a novel age-stratified back-calculation approach to data on new HIV diagnoses and CD4 count-at-diagnosis, enabled age-specific estimation of HIV incidence, undiagnosed infections and mean time-to-diagnosis across both the 2010-2015 and 2016-2018 periods. Estimated incidence trends were then extrapolated, to quantify the likelihood of achieving HIV elimination by 2030. Findings: A fall in HIV incidence in MSM is estimated to have started in 2012/3, eighteen months before the observed fall in new diagnoses. A steep decrease from 2,770 annual infections (95% credible interval 2.490-3,040) in 2013 to 1,740 (1,500-2,010) in 2015 is estimated, followed by steady decline from 2016, reaching 854 (441-1,540) infections in 2018. A decline is consistently estimated in all age groups, with a fall particularly marked in the 24-35 age group, and slowest in the 45+ group. Comparable declines are estimated in the number of undiagnosed infections. Interpretation: The peak and subsequent sharp decline in HIV incidence occurred prior to the phase-in of PrEP. Definining elimination as a public health threat to be < 50 new infections (1.1 infections per 10,000 at risk), 40% of incidence projections hit this threshold by 2030. In practice, targeted policies will be required, particularly among the 45+y where STIs are increasing most rapidly.

q-bio.QM↗

arXiv2019

Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased, and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter non-identifiability.

stat.CO↗

arXiv2017

Evidence synthesis for stochastic epidemic models

In recent years the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.

stat.ME↗