ORCID Profile
0000-0002-2639-1666
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Institute of Mathematical Statistics
Date: 06-2014
DOI: 10.1214/13-AAP942
Publisher: Springer Science and Business Media LLC
Date: 28-10-2010
DOI: 10.1007/S00285-010-0372-6
Abstract: This paper is concerned with SIR (susceptible → infected → removed) household epidemic models in which the infection response may be either mild or severe, with the type of response also affecting the infectiousness of an in idual. Two different models are analysed. In the first model, the infection status of an in idual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an in idual depends on the infection status of its infector and on whether the in idual was infected by a within- or between-household contact. The first scenario may be modelled using a multitype household epidemic model, and the second scenario by a model we denote by the infector-dependent-severity household epidemic model. Large population results of the two models are derived, with the focus being on the distribution of the total numbers of mild and severe cases in a typical household, of any given size, in the event that the epidemic becomes established. The aim of the paper is to investigate whether it is possible to determine which of the two underlying explanations is causing the varying response when given final size household outbreak data containing mild and severe cases. We conduct numerical studies which show that, given data on sufficiently many households, it is generally possible to discriminate between the two models by comparing the Kullback-Leibler ergence for the two fitted models to these data.
Publisher: Springer Science and Business Media LLC
Date: 16-11-2012
DOI: 10.1007/S00285-012-0609-7
Abstract: A random network model which allows for tunable, quite general forms of clustering, degree correlation and degree distribution is defined. The model is an extension of the configuration model, in which stubs (half-edges) are paired to form a network. Clustering is obtained by forming small completely connected subgroups, and positive (negative) degree correlation is obtained by connecting a fraction of the stubs with stubs of similar (dissimilar) degree. An SIR (Susceptible --> Infective --> Recovered) epidemic model is defined on this network. Asymptotic properties of both the network and the epidemic, as the population size tends to infinity, are derived: the degree distribution, degree correlation and clustering coefficient, as well as a reproduction number R(*), the probability of a major outbreak and the relative size of such an outbreak. The theory is illustrated by Monte Carlo simulations and numerical ex les. The main findings are that (1) clustering tends to decrease the spread of disease, (2) the effect of degree correlation is appreciably greater when the disease is close to threshold than when it is well above threshold and (3) disease spread broadly increases with degree correlation ρ when R(*) is just above its threshold value of one and decreases with ρ when R(*) is well above one.
Publisher: Cambridge University Press (CUP)
Date: 09-2009
DOI: 10.1017/S0001867800003554
Abstract: In this paper we consider a stochastic SIR (susceptible→infective→removed) epidemic model in which in iduals may make infectious contacts in two ways, both within ‘households’ (which for ease of exposition are assumed to have equal size) and along the edges of a random graph describing additional social contacts. Heuristically motivated branching process approximations are described, which lead to a threshold parameter for the model and methods for calculating the probability of a major outbreak, given few initial infectives, and the expected proportion of the population who are ultimately infected by such a major outbreak. These approximate results are shown to be exact as the number of households tends to infinity by proving associated limit theorems. Moreover, simulation studies indicate that these asymptotic results provide good approximations for modestly sized finite populations. The extension to unequal-sized households is discussed briefly.
Publisher: Cambridge University Press (CUP)
Date: 09-2015
Publisher: Springer Science and Business Media LLC
Date: 20-06-2017
Publisher: The Royal Society
Date: 08-2018
Abstract: The outbreak of an infectious disease in a human population can lead to in iduals responding with preventive measures in an attempt to avoid getting infected. This leads to changes in contact patterns. However, as we show in this paper, rational behaviour at the in idual level, such as social distancing from infectious contacts, may not always be beneficial for the population as a whole. We use epidemic network models to demonstrate the potential negative consequences at the population level. We take into account the social structure of the population through several network models. As the epidemic evolves, susceptible in iduals may distance themselves from their infectious contacts. Some in iduals replace their lost social connections by seeking new ties. If social distancing occurs at a high rate at the beginning of an epidemic, then this can prevent an outbreak from occurring. However, we show that moderate social distancing can worsen the disease outcome, both in the initial phase of an outbreak and the final epidemic size. Moreover, the same negative effect can arise in real-world networks. Our results suggest that one needs to be careful when targeting behavioural changes as they could potentially worsen the epidemic outcome. Furthermore, network structure crucially influences the way that in idual-level measures impact the epidemic at the population level. These findings highlight the importance of careful analysis of preventive measures in epidemic models.
Publisher: Cambridge University Press (CUP)
Date: 12-2013
Abstract: We consider a stochastic SIR (susceptible → infective → removed) epidemic on a random graph with specified degree distribution, constructed using the configuration model, and investigate the ‘acquaintance vaccination’ method for targeting in iduals of high degree for vaccination. Branching process approximations are developed which yield a post-vaccination threshold parameter, and the asymptotic (large population) probability and final size of a major outbreak. We find that introducing an imperfect vaccine response into the present model for acquaintance vaccination leads to sibling dependence in the approximating branching processes, which may then require infinite type spaces for their analysis and are generally not amenable to numerical calculation. Thus, we propose and analyse an alternative model for acquaintance vaccination, which avoids these difficulties. The theory is illustrated by a brief numerical study, which suggests that the two models for acquaintance vaccination yield quantitatively very similar disease properties.
Publisher: Springer Science and Business Media LLC
Date: 13-03-2019
Publisher: Elsevier BV
Date: 05-2010
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: Elsevier BV
Date: 04-2010
DOI: 10.1016/J.MBS.2009.12.003
Abstract: This paper is concerned with a stochastic SIR (susceptible-->infective-->removed) model for the spread of an epidemic amongst a population of in iduals, with a random network of social contacts, that is also partitioned into households. The behaviour of the model as the population size tends to infinity in an appropriate fashion is investigated. A threshold parameter which determines whether or not an epidemic with few initial infectives can become established and lead to a major outbreak is obtained, as are the probability that a major outbreak occurs and the expected proportion of the population that are ultimately infected by such an outbreak, together with methods for calculating these quantities. Monte Carlo simulations demonstrate that these asymptotic quantities accurately reflect the behaviour of finite populations, even for only moderately sized finite populations. The model is compared and contrasted with related models previously studied in the literature. The effects of the amount of clustering present in the overall population structure and the infectious period distribution on the outcomes of the model are also explored.
Publisher: Cambridge University Press (CUP)
Date: 03-2012
Abstract: We consider a stochastic SIR (susceptible → infective → removed) epidemic model with several types of in iduals. Infectious in iduals can make infectious contacts on two levels, within their own ‘household’ and with their neighbours in a random graph representing additional social contacts. This random graph is an extension of the well-known configuration model to allow for several types of in iduals. We give a strong approximation theorem which leads to a threshold theorem for the epidemic model and a method for calculating the probability of a major outbreak given few initial infectives. A multitype analogue of a theorem of Ball, Sirl and Trapman (2009) heuristically motivates a method for calculating the expected size of such a major outbreak. We also consider vaccination and give some short numerical illustrations of our results.
Publisher: Wiley
Date: 04-2008
DOI: 10.1890/07-1094.1
Abstract: Habitat loss and fragmentation has created metapopulations where there were once continuous populations. Ecologists and conservation biologists have become interested in the optimal way to manage and conserve such metapopulations. Several authors have considered the effect of patch disturbance and recovery on metapopulation persistence, but almost all such studies assume that every patch is equally susceptible to disturbance. We investigated the influence of protecting patches from disturbance on metapopulation persistence, and used a stochastic metapopulation model to answer the question: How can we optimally trade off returns from protection of patches vs. creation of patches? We considered the problem of finding, under budgetary constraints, the optimal combination of increasing the number of patches in the metapopulation network vs. increasing the number of protected patches in the network. We discovered that the optimal trade-off is dependent upon all of the properties of the system: the species dynamics, the dynamics of the landscape, and the relative costs of each action. A stochastic model and accompanying methodology are provided allowing a manager to determine the optimal policy for small metapopulations. We also provide two approximations, including a rule of thumb, for determining the optimal policy for larger metapopulations. The method is illustrated with an ex le inspired by information for the greater bilby, Macrotis lagotis, inhabiting southwestern Queensland, Australia. We found that given realistic costs for each action, protection of patches should be prioritized over patch creation for improving the persistence of the greater bilby during the next 20 years.
Publisher: Cambridge University Press (CUP)
Date: 06-2007
DOI: 10.1017/S0021900200117966
Abstract: We present bounds on the decay parameter for absorbing birth–death processes adapted from results of Chen (2000), (2001). We address numerical issues associated with computing these bounds, and assess their accuracy for several models, including the stochastic logistic model, for which estimates of the decay parameter have been obtained previously by Nåsell (2001).
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for David Sirl.