Bayesian Modeling of Dynamic Behavioral Change During an Epidemic

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.Comment: 20 pages, 10 figure

Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT

This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SINR) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus (TSWV) disease

A Framework for Incorporating Behavioural Change into Individual-Level Spatial Epidemic Models

During epidemics, people will often modify their behaviour patterns over time in response to changes in their perceived risk of spreading or contracting the disease. This can substantially impact the trajectory of the epidemic. However, most infectious disease models assume stable population behaviour due to the challenges of modelling these changes. We present a flexible new class of models, called behavioural change individual-level models (BC-ILMs), that incorporate both individual-level covariate information and a data-driven behavioural change effect. Focusing on spatial BC-ILMs, we consider four "alarm" functions to model the effect of behavioural change as a function of infection prevalence over time. We show how these models can be estimated in a simulation setting. We investigate the impact of misspecifying the alarm function when fitting a BC-ILM, and find that if behavioural change is present in a population, using an incorrect alarm function will still result in an improvement in posterior predictive performance over a model that assumes stable population behaviour. We also find that using spike and slab priors on alarm function parameters is a simple and effective method to determine whether a behavioural change effect is present in a population. Finally, we show results from fitting spatial BC-ILMs to data from the 2001 U.K. foot and mouth disease epidemic

Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic

Background A key challenge for modelling infectious disease dynamics is to understand the spatial spread of infection in real landscapes. This ideally requires a parallel record of spatial epidemic spread and a detailed map of susceptible host density along with relevant transport links and geographical features. Results Here we analyse the most detailed such data to date arising from the UK 2001 foot and mouth epidemic. We show that Euclidean distance between infectious and susceptible premises is a better predictor of transmission risk than shortest and quickest routes via road, except where major geographical features intervene. Conclusion Thus, a simple spatial transmission kernel based on Euclidean distance suffices in most regions, probably reflecting the multiplicity of transmission routes during the epidemic

Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia

We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany, in 1861 (Pfeilsticker 1863; Oesterle 1992)

Climbing the mountain: experimental design for the efficient optimization of stem cell bioprocessing

Abstract “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” – R.A. Fisher While this idea is relevant across research scales, its importance becomes critical when dealing with the inherently large, complex and expensive process of preparing material for cell-based therapies (CBTs). Effective and economically viable CBTs will depend on the establishment of optimized protocols for the production of the necessary cell types. Our ability to do this will depend in turn on the capacity to efficiently search through a multi-dimensional problem space of possible protocols in a timely and cost-effective manner. In this review we discuss approaches to, and illustrate examples of the application of statistical design of experiments to stem cell bioprocess optimization

An Empirically Parameterized Individual Based Model of Animal Movement, Perception and Memory

Our capacity to predict patterns of animal movement behavior is limited by our understanding of the underlying cognitive process. Determining what an animal knows about its environment, and how that information is translated into specific movement behaviors, is a conceptual challenge faced by movement ecologists. The modeling framework presented here is designed to evaluate the likelihood of alternative processes of perception, memory and decision making, based on readily available positional data and environmental metrics. The model is based on a flexible cognitive algorithm that provides the framework for an adaptive movement kernel. This enables a straightforward methodology for estimating key parameters for sensory perception, memory and movement while providing testable predictions of animal resource selection and space use patterns. In addition to describing the model and explaining the underlying logic, we demonstrate its parameterization potential using simulated data and investigate the robustness of its predictions over a wide range of temporal and spatial sampling scales. We show that the model can reproduce descriptive probes of movement paths with little sensitivity to the scale at which these paths were sampled and we discuss the merits of our approach in the context of movement- and cognitive-ecology and evolution