arXiv subjects

Matt J. Keeling

Publications and source records attributed to Matt J. Keeling.

5 recordsLinked to original sources

arXiv2025

Data-Driven Construction of Age-Structured Contact Networks

Capturing the structure of a population and characterising contacts within the population are key to reliable projections of infectious disease. Two main elements of population structure -- contact heterogeneity and age -- have been repeatedly demonstrated to be key in infection dynamics, yet are rarely combined. Regarding individuals as nodes and contacts as edges within a network provides a powerful and intuitive method to fully realise this population structure. While there are a few key examples of contact networks being measured explicitly, in general we need to construct the appropriate networks from individual-level data. Here, using data from social contact surveys, we develop a generic and robust algorithm to generate an extrapolated network that preserves both age-structured mixing and heterogeneity in the number of contacts. We then use these networks to simulate the spread of infection through the population, constrained to have a given basic reproduction number ($R_0$) and hence a given early growth rate. Given the over-dominant role that highly connected nodes (`superspreaders') would otherwise play in early dynamics, we scale transmission by the average duration of contacts, providing a better match to surveillance data for numbers of secondary cases. This network-based model shows that, for COVID-like parameters, including both heterogeneity and age-structure reduces both peak height and epidemic size compared to models that ignore heterogeneity. Our robust methodology therefore allows for the inclusion of the full wealth of data commonly collected by surveys but frequently overlooked to be incorporated into more realistic transmission models of infectious diseases.

physics.soc-ph↗

arXiv2022

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

q-bio.PE↗

arXiv2015

Exact and approximate moment closures for non-Markovian network epidemics

Moment-closure techniques are commonly used to generate low-dimensional deterministic models to approximate the average dynamics of stochastic systems on networks. The quality of such closures is usually difficult to asses and the relationship between model assumptions and closure accuracy are often difficult, if not impossible, to quantify. Here we carefully examine some commonly used moment closures, in particular a new one based on the concept of maximum entropy, for approximating the spread of epidemics on networks by reconstructing the probability distributions over triplets based on those over pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under Markovian and non-Markovian assumption characterising the latent and infectious periods. We initially study two special networks, namely the open triplet and closed triangle, for which we can obtain analytical results. We then explore numerically the exactness of moment closures for a wide range of larger motifs, thus gaining understanding of the factors that introduce errors in the approximations, in particular the presence of a random duration of the infectious period and the presence of overlapping triangles in a network. We also derive a simpler and more intuitive proof than previously available concerning the known result that pair-based moment closure is exact for the Markovian SIR model on tree-like networks under pure initial conditions. We also extend such a result to all infectious models, Markovian and non-Markovian, in which susceptibles escape infection independently from each infected neighbour and for which infectives cannot regain susceptible status, provided the network is tree-like and initial conditions are pure. This works represent a valuable step in deepening understanding of the assumptions behind moment closure approximations and for putting them on a more rigorous mathematical footing.

q-bio.PE↗

arXiv2013

The Nosoi commute: a spatial perspective on the rise of BSL-4 laboratories in cities

Recent H5N1 influenza research has revived the debate on the storage and manipulation of potentially harmful pathogens. In the last two decades, new high biosafety (BSL-4) laboratories entered into operation, raising strong concerns from the public. The probability of an accidental release of a pathogen from a BSL-4 laboratory is extremely low, but the corresponding risk -- defined as the probability of occurrence multiplied by its impact -- could be significant depending on the pathogen specificities and the population potentially affected. A list of BSL-4 laboratories throughout the world, with their location and date of first activity, was established from publicly available sources. This database was used to estimate the total population living within a daily commuting distance of BSL-4 laboratories, and to quantify how this figure changed over time. We show that from 1990 to present, the population living within the commuting belt of BSL-4 laboratories increased by a factor of 4 to reach up to 1.8% of the world population, owing to an increase in the number of facilities and their installation in cities. Europe is currently hosting the largest population living in the direct vicinity of BSL-4 laboratories, while the recent building of new facilities in Asia suggests that an important increase of the population living close to BSL-4 laboratories will be observed in the next decades. We discuss the potential implications in term of global risk, and call for better pathogen-specific quantitative assessment of the risk of outbreaks resulting from the accidental release of potentially pandemic pathogens

q-bio.PE↗

arXiv2010

Networks and the Epidemiology of Infectious Disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.

physics.soc-ph↗