ORCID Profile
0000-0002-1910-4227
Current Organisation
University of Nottingham
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Publisher: Springer Science and Business Media LLC
Date: 02-02-2011
Publisher: Springer Science and Business Media LLC
Date: 13-04-2019
Publisher: Oxford University Press (OUP)
Date: 06-08-2013
Publisher: Oxford University Press (OUP)
Date: 25-02-2011
Publisher: Springer Science and Business Media LLC
Date: 07-2016
Publisher: Oxford University Press (OUP)
Date: 03-2022
DOI: 10.1111/RSSC.12532
Abstract: Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures and mass urbanization can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley–Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas’ affluence, such models can both simplify logistics and circumvent biases inherent to household surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley–Terry model, which substantially decreases the number of comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania.
Publisher: Wiley
Date: 10-11-2010
Publisher: Elsevier BV
Date: 02-2023
Publisher: Cold Spring Harbor Laboratory
Date: 02-07-2022
DOI: 10.1101/2022.07.01.22277134
Abstract: During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. We introduce an SIR -type model with the infected population structured by ‘infected age’, i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb–14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty.
Publisher: Springer Science and Business Media LLC
Date: 22-05-2017
Publisher: Informa UK Limited
Date: 02-08-2016
Publisher: Elsevier BV
Date: 02-2014
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Simon Preston.