Outputs (30)

Describing financial crisis propagation through epidemic modelling on multiplex networks (2024)
Journal Article
Bozhidarova, M., Ball, F., van Gennip, Y., O'Dea, R. D., & Stupfler, G. (2024). Describing financial crisis propagation through epidemic modelling on multiplex networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480(2287), Article 20230787. https://doi.org/10.1098/rspa.2023.0787

This paper proposes a novel framework for modelling the spread of financial crises in complex networks, combining financial data, Extreme Value Theory and an epidemiological transmission model. We accommodate two key aspects of contagion modelling: f... Read More about Describing financial crisis propagation through epidemic modelling on multiplex networks.

SIR epidemics in populations with large sub-communities (2024)
Journal Article
Ball, F., Sirl, D., & Trapman, P. (in press). SIR epidemics in populations with large sub-communities. Annals of Applied Probability,

We investigate final outcome properties of an SIR (susceptible → in-fective → recovered) epidemic model defined on a population of large sub-communities in which there is stronger disease transmission within the communities than between them. Our ana... Read More about SIR epidemics in populations with large sub-communities.

The impact of household structure on disease-induced herd immunity (2023)
Journal Article
Ball, F., Critcher, L., Neal, P., & Sirl, D. (2023). The impact of household structure on disease-induced herd immunity. Journal of Mathematical Biology, 87(6), Article 83. https://doi.org/10.1007/s00285-023-02010-7

The disease-induced herd immunity level hD is the fraction of the population that must be infected by an epidemic to ensure that a new epidemic among the remaining susceptible population is not supercritical. For a homogeneously mixing population hD... Read More about The impact of household structure on disease-induced herd immunity.

Strong convergence of an epidemic model with mixing groups (2023)
Journal Article
Ball, F., & Neal, P. (in press). Strong convergence of an epidemic model with mixing groups. Advances in Applied Probability, https://doi.org/10.1017/apr.2023.29

We consider an SIR (susceptible → infective → recovered) epidemic in a closed population of size n, in which infection spreads via mixing events, comprising individuals chosen uniformly at random from the population, which occur at the points of a Po... Read More about Strong convergence of an epidemic model with mixing groups.

The size of a Markovian SIR epidemic given only removal data (2023)
Journal Article
Ball, F., & Neal, P. (2023). The size of a Markovian SIR epidemic given only removal data. Advances in Applied Probability, 55(3), 895-926. https://doi.org/10.1017/apr.2022.58

During an epidemic outbreak, typically only partial information about the outbreak is known. A common scenario is that the infection times of individuals are unknown, but individuals, on displaying symptoms, are identified as infectious and removed f... Read More about The size of a Markovian SIR epidemic given only removal data.

Asymptotic persistence time formulae for multitype birth-death processes (2023)
Journal Article
Ball, F. G., & Clancy, D. (2023). Asymptotic persistence time formulae for multitype birth-death processes. Journal of Applied Probability, 60(3), 895-920. https://doi.org/10.1017/jpr.2022.102

We consider a class of processes describing a population consisting of k types of individuals. The process is almost surely absorbed at the origin within finite time, and we study the expected time taken for such extinction to occur. We derive simple... Read More about Asymptotic persistence time formulae for multitype birth-death processes.

An epidemic model with short-lived mixing groups (2022)
Journal Article
Ball, F., & Neal, P. (2022). An epidemic model with short-lived mixing groups. Journal of Mathematical Biology, 85(6-7), Article 63. https://doi.org/10.1007/s00285-022-01822-3

Almost all epidemic models make the assumption that infection is driven by the interaction between pairs of individuals, one of whom is infectious and the other of whom is susceptible. However, in society individuals mix in groups of varying sizes, a... Read More about An epidemic model with short-lived mixing groups.

Epidemics on networks with preventive rewiring (2021)
Journal Article
Ball, F., & Britton, T. (2022). Epidemics on networks with preventive rewiring. Random Structures and Algorithms, 61(2), 250-297. https://doi.org/10.1002/rsa.21066

A stochastic SIR (susceptible → infective → recovered) model is considered for the spread of an epidemic on a network, described initially by an Erd˝os-R´enyi random graph, in which susceptible individuals connected to infectious neighbours may drop... Read More about Epidemics on networks with preventive rewiring.

Central limit theorems for SIR epidemics and percolation on configuration model random graphs (2021)
Journal Article
Ball, F. (2021). Central limit theorems for SIR epidemics and percolation on configuration model random graphs. Annals of Applied Probability, 31(5), 2091-2142. https://doi.org/10.1214/20-AAP1642

We consider a stochastic SIR (susceptible → infective → recovered) epidemic defined on a configuration model random graph, in which infective individuals can infect only their neighbours in the graph during an infectious period which has an arbitrary... Read More about Central limit theorems for SIR epidemics and percolation on configuration model random graphs.

The risk for a new COVID-19 wave and how it depends on R 0, the current immunity level and current restrictions (2021)
Journal Article
Britton, T., Trapman, P., & Ball, F. (2021). The risk for a new COVID-19 wave and how it depends on R 0, the current immunity level and current restrictions. Royal Society Open Science, 8(7), Article 210386. https://doi.org/10.1098/rsos.210386

The COVID-19 pandemic has hit different regions differently. The current disease-induced immunity level î in a region approximately equals the cumulative fraction infected, which primarily depends on two factors: (i) the initial potential for COVID-1... Read More about The risk for a new COVID-19 wave and how it depends on R 0, the current immunity level and current restrictions.