amei: An R Package for the Adaptive Management of Epidemiological Interventions

The amei package for R is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action.

amei: An R Package for the Adaptive Management of Epidemiological Interventions

The <b>amei</b> package for <b>R</b> is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action

A space-time conditional intensity model for infectious disease occurence

A novel point process model continuous in space-time is proposed for infectious disease data. Modelling is based on the conditional intensity function (CIF) and extends an additive-multiplicative CIF model previously proposed for discrete space epidemic modelling. Estimation is performed by means of full maximum likelihood and a simulation algorithm is presented. The particular application of interest is the stochastic modelling of the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002–2008. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modelling, simulation and inference of self-exciting spatio-temporal point processes based on the CIF. Application is promoted by an implementation in the R package RLadyBug

Spatio-temporal epidemic modelling using additive-multiplicative intensity models

An extension of the stochastic susceptible-infectious-recovered (SIR) model is proposed in order to accommodate a regression context for modelling infectious disease surveillance data. The proposal is based on a multivariate counting process specified by conditional intensities, which contain an additive epidemic component and a multiplicative endemic component. This allows the analysis of endemic infectious diseases by quantifying risk factors for infection by external sources in addition to infective contacts. Simulation from the model is straightforward by Ogata's modified thinning algorithm. Inference can be performed by considering the full likelihood of the stochastic process with additional parameter restrictions to ensure non-negative conditional intensities. As an illustration we analyse data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of the classical swine fever virus in Germany during 1993-2004

Simulación de un brote de cólera porcino en una instalación de Camagüey.

Para mostrar el comportamiento de un brote epidémico de cólera porcino en una instalación de Camagüey se simuló la entrada de un animal enfermo sin tomar ninguna medida de contención ni preventiva. Se utilizó el programa R, y el paquete Odesolve para resolver el modelo SEIR (susceptibilidad, expuesto, infectado y recuperado). El estudio fue durante el mes de enero de 2001. Se tomó un índice de transmisibilidad de 0,35, una duración de la enfermedad de diez días y un período latente de diez días. El modelo de simulación mostró en el caso de no tomar ninguna medida, cómo se produciría un pico máximo de la enfermedad a los 12 días de iniciado: un total de 127 enfermos, lo que equivale al 63 % de la población expuesta. El número reproductivo básico (Ro) encontrado fue 3,26.Simulation of a Hog Cholera Outbreak on a Swine Breeding Farm ABSTRACT Admission of a swine infected by hog cholera on a swine breeding farm was simulated to demonstrate this disease outbreak performance when no restraining or preventive measures are affected. The SEIR model (susceptivity, exposure, infestation, and restoration) was applied by using the computer program R and the statistical package Odesolve. The study was conducted during January 2011. A transmissibility index of 0,35, a disease duration of ten days, and a latent period of ten days were set. The SEIR model showed how this disease peaked after a twelve-day onset with a total of 127 infected swines, i.e., 63 % of the exposed population, when no measures were affected. The basic reproductive number (Ro) was 3,26

Simulation-based Bayesian inference for epidemic models

This is the author pre-print version. The final version is available from the publisher via the DOI in this record.A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for large-scale systems. In addition, designing efficient proposal distributions for the missing data is typically challenging. Pseudo-marginal methods instead integrate across the missing data using a Monte Carlo estimate for the likelihood, generated from multiple independent simulations from the model. These techniques can avoid the high memory requirements of DA-MCMC, and under certain conditions produce the exact marginal posterior distribution for parameters. A novel method is presented for implementing importance sampling for dynamic epidemic models, by conditioning the simulations on sets of validity criteria (based on the model structure) as well as the observed data. The flexibility of these techniques is illustrated using both removal time and final size data from an outbreak of smallpox. It is shown that these approaches can circumvent the need for reversible-jump MCMC, and can allow inference in situations where DA-MCMC is impossible due to computationally infeasible likelihoods. © 2013 Elsevier B.V. All rights reserved.T. J. M. was in part supported by Department for the Environment, Food and Rural Affairs/Higher Education Funding Council of England, grant number VT0105 and BBSRC grant (BB/I012192/1). J. V. R was in part supported by Australian Research Council’s Discovery Projects funding scheme (project number DP110102893). R. D. was in part supported by Natural Sciences and Engineering Research Council (NSERC) of Canada’s Discovery Grants Program. A. R. C. was in part supported by National Medical Research Council (NMRC/HINIR/005/2009) and NUS Initiative to Improve Health in Asia. The authors would like to thank Andrew Conlan and Theo Kypraios for useful discussions

Epidemia:An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes

This article introduces epidemia, an R package for Bayesian, regression-oriented modeling of infectious diseases. The implemented models define a likelihood for all observed data while also explicitly modeling transmission dynamics: an approach often termed as semi-mechanistic. Infections are propagated over time using renewal equations. This approach is inspired by self-exciting, continuous-time point processes such as the Hawkes process. A variety of inferential tasks can be performed using the package. Key epidemiological quantities, including reproduction numbers and latent infections, may be estimated within the framework. The models may be used to evaluate the determinants of changes in transmission rates, including the effects of control measures. Epidemic dynamics may be simulated either from a fitted model or a prior model; allowing for prior/posterior predictive checks, experimentation, and forecasting

MCMC methods: graph samplers, invariance tests and epidemic models

Markov Chain Monte Carlo (MCMC) techniques are used ubiquitously for simulation-based inference. This thesis provides novel contributions to MCMC methods and their application to graph sampling and epidemic modeling. The first topic considered is that of sampling graphs conditional on a set of prescribed statistics, which is a difficult problem arising naturally in many fields: sociology (Holland and Leinhardt, 1981), psychology (Connor and Simberloff, 1979), categorical data analysis (Agresti, 1992) and finance (Squartini et al., 2018, Gandy and Veraart, 2019) being examples. Bespoke MCMC samplers are proposed for this setting. The second major topic addressed is that of modeling the dynamics of infectious diseases, where MCMC is leveraged as the general inference engine. The first part of this thesis addresses important problems such as the uniform sampling of graphs with given degree sequences, and weighted graphs with given strength sequences. These distributions are frequently used for exact tests on social networks and two-way contingency tables. Another application is quantifying the statistical significance of patterns observed in real networks. This is crucial for understanding whether such patterns indicate the presence of interesting network phenomena, or whether they simply result from less interesting processes, such as nodal-heterogeneity. The MCMC samplers developed in the course of this research are complex, and there is great scope for conceptual, analytic, and implementation errors. This motivates a chapter that develops novel tests for detecting errors in MCMC implementations. The tests introduced are unique in being exact, which allows us to keep the false rejection probability arbitrarily low. Rather than develop bespoke samplers, as in the first part of the thesis, the second part leverages a standard MCMC framework Stan (Stan Development Team, 2018) as the workhorse for fitting state-of-the-art epidemic models. We present a general framework for semi-mechanistic Bayesian modeling of infectious diseases using renewal processes. The term semi-mechanistic relates to statistical estimation within some constrained mechanism. This research was motivated by the ongoing SARS-COV-2 pandemic, and variants of the model have been used in specific analyses of Covid-19. We present epidemia, an R package allowing researchers to leverage the epidemic models. A key goal of this work is to demonstrate that MCMC, and in particular, Stan’s No-U-Turn (Hoffman and Gelman, 2014) sampler, can be routinely employed to fit a large-class of epidemic models. A second goal is to make the models accessible to the general research community, through epidemia.Open Acces