Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation (EnOSI) (original) (raw)

Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation

Spatial and Spatio-temporal Epidemiology, 2014

We explore the use of the optimal statistical interpolation (OSI) data assimilation method for the statistical tracking of emerging epidemics and to study the spatial dynamics of a disease. The epidemic models that we used for this study are spatial variants of the common susceptible-infectious-removed (S-I-R) compartmental model of epidemiology. The spatial S-I-R epidemic model is illustrated by application to simulated spatial dynamic epidemic data from the historic "Black Death" plague of 14th century Europe. Bayesian statistical tracking of emerging epidemic diseases using the OSI as it unfolds is illustrated for a simulated epidemic wave originating in Santa Fe, New Mexico.

Bayesian tracking of emerging epidemics using optimal statistical interpolation (OSI)

2010

Bayesian epidemic models for spatially aggregated count data

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein– Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control , thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece.

Inferring the dynamics of a spatial epidemic from time-series data

Bulletin of Mathematical Biology, 2004

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested

Stochastic Modeling and Combined Spatial Pattern Analysis of Epidemic Spreading

We present analysis of spatial patterns of generic disease spread simulated by a stochastic long-range correlation SIR model, where individuals can be infected at long distance in a power law distribution. We integrated various tools, namely perimeter, circularity, fractal dimension, and aggregation index to characterize and investigate spatial pattern formations. Our primary goal was to understand for a given model of interest which tool has an advantage over the other and to what extent. We found that perimeter and circularity give information only for a case of strong correlationwhile the fractal dimension and aggregation index exhibit the growth rule of pattern formation, depending on the degree of the correlation exponent (β). The aggregation index method used as an alternative method to describe the degree of pathogenic ratio (α). This study may provide a useful approach to characterize and analyze the pattern formation of epidemic spreading Keywords-spatial pattern epidemics, aggregation index, fractal dimension, stochastic, long-rang epidemics I. INTRODUCTION ATTERN formation phenomena, occurring via the aggregation process or clustering of particles, has been the subject of increased interest [1]. Spatial pattern analysis plays an important role in many fields of research, ranging from the microscopic to macroscopic scale, including bacteria colonies [2], epidemiology [3], forests, and ecology [4]. Spatial technology enables epidemiologists to create detailed maps and employ spatial cluster statistics to garner insights about patterns of disease [3]. There has been significant development in creating predictive models to better understand the pattern formation of epidemics; see reviews [5-7]. The mathematical epidemiological model usually takes the form of a deterministic model, which consists of a system of ordinary differential equation (ODE) models describing changes in the number of susceptible, infected, and recovered individuals in a given population [8]. Typically, the ODE

Back-calculation, Classification, and Emulation-based Inference for Spatial Infectious Disease Models

2015

BACK-CALCULATION, CLASSIFICATION AND EMULATION-BASED INFERENCE FOR SPATIAL INFECTIOUS DISEASES MODELS Gyanendra Pokharel Advisor: University of Guelph, 2015 Dr. Rob Deardon Individual-level models (ILMs) are a class of complex probabilistic models which can be used to model infectious disease data. They can incorporate the effects of time and space, the key risk factors of disease transmission, and inference for them is carried out easily within a Bayesian MCMC framework. They are thus useful for modelling individual-level spatial epidemic data. However, fitting these models to what are typically incomplete data can result in poor parameter estimation and can miss important characteristics of the disease systems. Additionally, the complex nature of such ILMs can cause significant computational expense when fitting them to large disease systems. Here, we propose methods and models which address both the incomplete history of epidemic data as well as the computational problem of infer...

Hierarchical space-time modelling of epidemic dynamics: an application to measles outbreaks

Statistical Methods & Applications, 2004

How infectious diseases spread in space and time is an important question that has received considerable theoretical attention. There are, however, few empirical studies to support theoretical approaches, because data is scarce. In this paper we propose to model the epidemic spread of measles in the London boroughs between 1960 and 1970 by an extension of the Kriged Kalman filter to count data. Results show the flexibility of our approach in describing complex spatio-temporal dynamics.

Simulation of the Spread of Epidemic Disease Using Persistent Surveillance Data

This paper proposes a novel datamining framework, which is called susceptibleinfected-removed method based on heat-transfer mechanism (SIR-HT), to simulate the spread of epidemic diseases using persistent surveillance data (A. J. Plaza and C.I Chang, 2009). SIR-HT is formulated by merging the persistent surveillance data about epidemics, geographic information, and the dynamics of disease into a heat transfer model (Dewitt, 2006) according to the theory of statistical mechanics (R.K.Pathria, 2001). In the implementation of this framework, geographic conditions are used to define the heat transfer media, which is featured by heat conductivity and thermal capacitance; persistent surveillance data about epidemic disease is used as the initial conditions; the susceptible-infectedrecovered (SIR) model (Daley, 2005), one of the most fundamental dynamic models about epidemic disease is employed as Neumann boundary conditions. As a result, the spread of epidemics can be simulated by solving the corresponding transient heat-transfer problem (D.K. Gartling and J.N. Reddy, 2000) (Y. Liang et al, 2002). Using COMSOL Multiphysics (Pryor, 2010) as the major platform, SIR-HT is assessed by computing the spread of a flu epidemic at a sample site in the Minneapolis (Minnesota, USA) region.

Computational protocol to perform a spatiotemporal reconstruction of an epidemic

STAR PROTOCOLS, 2023

Computational protocol to perform a spatiotemporal reconstruction of an epidemic Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. Publisher's note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Estimating Parameters for Stochastic Epidemics

Understanding the spread of infectious disease is an important issue in order to prevent major outbreaks. In this report mathematical modeling is used to gain insight into the dynamics of an epidemic. A process model, the SIR model, exploiting knowledge about population dynamics serves as framework. Key interest is in adapting the stochastic model to observed data -especially from animal production. Observing all events of an epidemic is not feasible in practice, hence estimation of model parameters has to be done from missing data. We give a rigorous treatment of an existing technique to handle estimation in partially observed epidemics using Markov Chain Monte Carlo (MCMC). The aim of this report is to extend the basic SIR model to handle two common situations in animal production: interaction into the course of the epidemic and population heterogeneity due to the spatial layout of confinement. Handling partially observed epidemics in these contexts we do by extending the above described MCMC method. A programming environment has been developed to exemplify the model extensions at a proof of concept level. It is made available for download for others to confirm our results or try the extensions on their own data.

Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation (EnOSI) (original) (raw)

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