arXiv subjects

Valerie Isham

Publications and source records attributed to Valerie Isham.

3 recordsLinked to original sources

arXiv2025

A Behaviour and Disease Model of Testing and Isolation

There has been interest in the interactions between infectious disease dynamics and behaviour for most of the history of mathematical epidemiology. This has included consideration of which mathematical models best capture each phenomenon, as well as their interaction, but typically in a manner that is agnostic to the exact behaviour in question. Here, we investigate interacting behaviour and disease dynamics specifically related to decisions around testing and isolation. To carry out our investigation we extend an existing "behaviour and disease" (BaD) model by incorporating the dynamics of symptomatic testing and isolation, including the influence of positive tests on perception of infection risk. We provide a dynamical systems analysis of the ordinary differential equations that define this model, providing theoretical results on its behaviour early in a new outbreak (particularly its basic reproduction number) and endemicity of the system (its steady states and associated stability criteria). We then supplement these findings with a numerical analysis to inform how temporal and cumulative outbreak metrics depend on the model parameter values for epidemic and endemic regimes. We observe novel model outputs such as epidemics that have more observed cases detected through increased testing, but are less objectively severe in terms of total number of infections.

q-bio.PE↗

arXiv2020

Key Questions for Modelling COVID-19 Exit Strategies

Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

q-bio.OT↗

arXiv2013

Point process models for fine-resolution rainfall

In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewis clustering mechanism and intended for sub-hourly application, was introduced. That model replaced the rectangular rain cells of the original model with finite Poisson processes of instantaneous pulses, allowing greater variability in rainfall intensity over short intervals. In the present paper, the basic instantaneous pulse model is first extended to allow for randomly varying storm types. A systematic comparison of a number of key model variants, fitted to 5-minute rainfall data from Germany, then generates further new insights into the models, leading to the development of an additional model extension, which introduces dependence between rainfall intensity and duration in a simple way. The new model retains the original rectangular cells, previously assumed inappropriate for fine-scale data, obviating the need for the computationally more intensive instantaneous pulse model.

math.ST↗