Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available

The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs each time they are run, even if the same input values are used. Stochastic computer models are more difficult to analyse and more difficult to emulate - often requiring substantially more computer model runs to fit. We present a method of using deterministic approximations of the computer model to better construct an emulator. The method is applied to numerous toy examples, as well as an idealistic epidemiology model, and a model from the building performance field

An Analytical Heat Wave Definition Based on the Impact on Buildings and Occupants

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Alongside a mean global rise in temperature, climate change predictions point to an increase in heat waves and an associated rise in heat-related mortality. This suggests a growing need to ensure buildings are resilient to such events. Unfortunately, there is no agreed way of doing this, and no standard set of heatwaves for scientists or engineers to use. In addition, in all cases, heat waves are defined in terms of external conditions, yet, as the Paris heat wave of 2003 showed, people die in the industrialised world from the conditions inside buildings, not those outside. In this work, we reverse engineer external temperature time series from monitored conditions within a representative set of buildings during a heat wave. This generates a general probabilistic analytical relationship between internal and external heatwaves and thereby a standard set of events for testing resilience. These heat waves are by their simplicity ideal for discussions between clients and designers, or for the setting of national building codes. In addition, they provide a new framework for the declaration of a health emergency.Engineering and Physical Sciences Research Council (EPSRC)Zero Peak Energy Building Design for IndiaActive Building CentreFundación Séneca-Agencia de Ciencia y Tecnología de la Región de MurciaSpanish Ministry of Economy and Competitivenes

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

Monogamous networks and the spread of sexually transmitted diseases.

Patterns of sexual mixing and heterogeneity in the number of sexual partners can have a huge effect on the spread of a sexually transmitted disease (STD). The sexual mixing network identifies all partnerships within a population over a given period and is a powerful tool in the study of such infections. Previous models assumed all links within the network to be concurrent active partnerships. We present a novel modelling approach in which we adapt the notion of a sexual contact network to a monogamous population by allowing the nature of the links to change. We use the underlying network to represent potential sexual partnerships, only some of which are active at any one time. Thus serial monogamy can be modelled while maintaining the patterns of mixing displayed by the population

Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases

A wide range of communicable human diseases can be considered as spreading through a network of possible transmission routes. The implied network structure is vital in determining disease dynamics, especially when the average number of connections per individual is small as is the case for many sexually transmitted diseases (STDs). Here we develop an intuitive mathematical framework to deal with the heterogeneities implicit within contact networks and those that arise because of the infection process. These models are compared with full stochastic simulations and show excellent agreement across a wide range of parameters. We show how such models can be used to estimate parameters of epidemiological importance, and how they can be extended to examine the effectiveness of various control strategies, in particular screening programs and contact tracing

FirstCite e-publishing Contact tracing and disease control

Contact tracing, followed by treatment or isolation, is a key control measure in the battle against infectious diseases. It is an extreme form of locally targeted control, and as such has the potential to be highly efficient when dealing with low numbers of cases. For this reason it is frequently used to combat sexually transmitted diseases and new invading pathogens. Accurate modelling of contact tracing requires explicit information about the disease-transmission pathways from each individual, and hence the network of contacts. Here, pairwise-approximation methods and full stochastic simulations are used to investigate the utility of contact tracing. A simple relationship is found between the efficiency of contact tracing necessary for eradication and the basic reproductive ratio of the disease. This holds for a wide variety of realistic situations including heterogeneous networks containing core-groups or super-spreaders, and asymptomatic individuals. Clustering (transitivity) within the transmission network is found to destroy the relationship, requiring lower efficiency than predicted

Dataset for "An Analytical Heat Wave Definition Based on the Impact on Buildings and Occupants"

These data support the publication "An Analytical Heat Wave Definition Based on the Impact on Buildings and Occupants". This paper provides novel methods and data for the generation of heat wave signatures for the testing of current and future resilience of buildings to extreme heat events. The main novelty of the work lies in the fact that it generates these events by considering the impact on the internal environment, where most of the heat related morbidities and mortalities occur. Several data sets are included in this deposit to aid the use of the method in the paper and the data we have produced. This includes some data on the real homes used in the paper as well as the EnergyPlus input data files (IDF) used in the simulation. In addition, example weather files including those for the future, are included. Please see the README file for further information.Full details of the data collection method may be found in the associated manuscript.EnergyPlus input data files (IDF) and weather files (.epw) are plain text documents that may be processed with EnergyPlus (https://energyplus.net/) or EPx (https://bigladdersoftware.com/epx/) software. The formats are documented in the Input Output Reference and Auxiliary Programs documentation accompanying the software, respectively