Household epidemic models with varying infection response

This paper is concerned with SIR (susceptible $\to$ infected $\to$ 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 individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual 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 divergence for the two fitted models to these data.Comment: 29 pages; submitted to Journal of Mathematical Biolog

Epidemics on random intersection graphs

In this paper we consider a model for the spread of a stochastic SIR (Susceptible $\to$ Infectious $\to$ Recovered) epidemic on a network of individuals described by a random intersection graph. Individuals belong to a random number of cliques, each of random size, and infection can be transmitted between two individuals if and only if there is a clique they both belong to. Both the clique sizes and the number of cliques an individual belongs to follow mixed Poisson distributions. An infinite-type branching process approximation (with type being given by the length of an individual's infectious period) for the early stages of an epidemic is developed and made fully rigorous by proving an associated limit theorem as the population size tends to infinity. This leads to a threshold parameter $R_*$, so that in a large population an epidemic with few initial infectives can give rise to a large outbreak if and only if $R_*>1$. A functional equation for the survival probability of the approximating infinite-type branching process is determined; if $R_*\le1$, this equation has no nonzero solution, while if $R_*>1$, it is shown to have precisely one nonzero solution. A law of large numbers for the size of such a large outbreak is proved by exploiting a single-type branching process that approximates the size of the susceptibility set of a typical individual.Comment: Published in at http://dx.doi.org/10.1214/13-AAP942 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Household epidemic models with varying infection response

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 individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual 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 divergence for the two fitted models to these data

An SIR epidemic model on a population with random network and household structure and several types of individuals

We consider a stochastic SIR (susceptible → infective → removed) epidemic model with several types of individuals. Infectious individuals 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 individuals. 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 et al. (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

An SIR epidemic model on a population with random network and household structure, and several types of individuals

We consider a stochastic SIR (susceptible → infective → removed) epidemic model with several types of individuals. Infectious indi- viduals can make infectious contacts on two levels, within their own ‘household’ and with their neighbours in a random graph represent- ing additional social contacts. This random graph is an extension of the well-known configuration model to allow for several types of in- dividuals. We give a strong approximation theorem which leads to a threshold theorem for the epidemic model and a method for calcu- lating the probability of a major outbreak given few initial infectives. A multitype analogue of a theorem of Ball et al. (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

Multi-Level Spatial Comparative Judgement Models To Map Deprivation

While current comparative judgement models provide strong algorithmic efficiency, they remain data inefficient, often requiring days or weeks of extensive data collection to provide sufficient pair- wise comparisons for stable and accurate parameter estimation. This disparity between data and algorithm efficiency is preventing widespread adoption, especially so in challenging data-collection environments such as mapping human rights abuses. We address the data inefficiency challenge by introducing the finite element Gaussian process Bradley–Terry mixture model, an approach that significantly reduces the number of pairwise comparisons required by comparative judgement mod- els. This is achieved via integration of prior spatial assumptions, encoded as a mixture of functions, each function introducing a spatial smoothness constraint at a specific resolution. These functions are modelled nonparametrically, through Gaussian process prior distributions. We use our method to map deprivation in the city of Dar es Salaam, Tanzania and locate slums in the city where poverty reduction measures can be carried out

Household epidemic models with varying infection response

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 individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual 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 divergence for the two fitted models to these data

An Sir Epidemic Model on a Population with Random Network and Household Structure, and Several Types of Individuals

Une équipe irano-britannique a repris d’importantes recherches archéologiques sur le fameux « Mur d’Alexandre » (connu aussi sous d’autres noms), dans le Gorgan, long de près de 200 km, et sur le « Mur de Tammishe » dans l’angle sud-est de la mer Caspienne, beaucoup plus court. Ces investigations font suite aux travaux de M.Y. Kiani dans les années soixante-dix et à ceux, très récents, de J. Nokandeh, codirecteur de la nouvelle mission. Étude de l’environnement, prospections géophysiques et s..