Analyzing veterinary surveillance data: Approaches to model the relationship between disease incidence and cattle trade

Two approaches to the analysis of registry data for bovine diseases with regard to the relationship between disease incidence and cattle trade are proposed. Firstly, a parameter-driven spatio-temporal disease mapping model formulated in a hierarchical Bayesian framework is used. Various cattle movement parameters, e.g. the number and proportion of in-movements from infected regions, can be included as potential covariates. Within this context problems of such an endogenous covariate are discussed. Since a purely parameter-driven approach is often not adequate to depict local epidemics, a so-called observationdriven infectious disease model is proposed as a second possibility. It includes an autoregressive part for counts in the region of interest in the past. Additionally, the sum of previous cases in other regions weighted by cattle movements is added to assess the spread of the disease by trading. Both models are applied to cases of Coxiellosis in Switzerland, 2005 to 2009

Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach

This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.J.M. Angulo and A.E. Madrid have been partially supported by grants MTM2009-13250 and MTM2012-32666 of SGPI, and P08-FQM-3834 of the Andalusian CICE, Spain. H-L Yu has been partially supported by a grant from National Science Council of Taiwan (NSC101-2628-E-002-017-MY3 and NSC102-2221-E-002-140-MY3). A. Kolovos was supported by SpaceTimeWorks, LLC. G. Christakos was supported by a Yongqian Chair Professorship (Zhejiang University, China)

Spatial heterogeneity in Bayesian disease mapping

© 2018, The Author(s). Disease mapping applications generally assume homogeneous regression effects and use random intercepts to account for residual spatial dependence. However, there may be local variation in the association between disease and area risk factors. We consider implications for model fit, estimated regression coefficients, and substantive inferences of allowing spatial variability in impacts of area risk factors. An application to suicide in 6791 English small areas shows that average regression coefficients and substantive inferences (e.g. about relative risk) may be considerably affected by allowing spatially varying predictor effects, while fit is improved

From spatial ecology to spatial epidemiology: Modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165thPCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r 2= 0.579). Conclusions: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information

Statistical analysis of spatio-temporal veterinary surveillance data: Applications of integrated nested Laplace approximations

The surveillance of animal diseases is an important task of national veterinary authorities. Major aims are the prevention of disease spread and, for zoonoses, the transmission of diseases from animals to humans. Monitoring is mostly done by passive surveillance, where laboratory confirmed cases have to be reported. However, such data are often biased due to reporting delay or underreporting. Since 1991 the Swiss federal veterinary office (BVET) collects data on about 80 notifiable diseases. The week number of diagnosis and the location within one of 184 administrative regions is known for each case. Additionally, data from the Principality of Liechtenstein are available. The aim of this dissertation is to propose approaches for the statistical modelling of spatio-temporal patterns based on the specific characteristics of a disease and to apply them to selected data from the BVET database. If, e.g., regions with a high disease incidence are identified by such a model, appropriate control measures can be initiated. A further emphasis is on the presentation of user-friendly software and available model choice criteria. Spatio-temporal count data are often analyzed using hierarchical Bayesian models. For inference we propose integrated nested Laplace approximations (INLA) and show their versatile applicability as regards space-time modelling. High usability is guaranteed by freely available INLA software. Along with the deviance information criterion we discuss predictive scores, which are provided by INLA for model choice and criticism. Such scores turn out to be useful for the evaluation of cross-validatory as well as one-step-ahead forecasts. We begin with an analysis of aggregated regional data of diseases with constant, endemic risk. In addition to modelling spatial autocorrelation we describe the inclusion of a linear time trend for a case study on Coxiellosis in cows. Furthermore, we discuss the appropriate specification (linear, nonparametric) of a region-specific covariate. For an analysis of Salmonellosis cases in cows we propose a nonparametric time trend and discuss various modelling options. A further emphasis is on the versatile interpretation of spatio-temporal interaction terms and the derivation of criteria to guarantee their identifiability. To analyze case reporting of Bovine Viral Diarrhoea concerning the affiliation of a region to a Swiss canton, we expand these models by a coarser, cantonal grid. A comparison with exclusively regional models using cross-validated scores shows a biased case reporting in several Swiss cantons. An active surveillance and vaccination program was launched for Bluetongue (BT) in 2008/09 within Switzerland. We perform a regression which assesses the association between individual information on vaccination, surveillance and altitude and the occurrence of BT for each farm. Additionally, a two-dimensional location effect on a regular lattice is included in the model. The results indicate that a vaccination reduces the risk of a BT infection. We propose a vector-autoregressive model for multivariate time series to model diseases with local outbreaks. Furthermore, we show how information on networks between regions can directly be related to observed disease counts. Using this methodology, a spatio-temporal spread of Coxiellosis in cows between neighbouring regions and by cattle trade is detected. Comparing one-step-ahead predictive scores it turns out that, for this case study, such a parameterdriven approach exhibits a better predictive performance than so-called observation-driven models, where actually observed previous cases govern the infection mechanis

Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases

Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated