A Bayesian hierarchical model for the estimation of two incomplete surveillance data sets.
A model-based approach to analyze two incomplete disease surveillance datasets is described. Such data typically consist of case counts, each originating from a specific geographical area. A Bayesian hierarchical model is proposed for estimating the total number of cases with disease while simultaneously adjusting for spatial variation. This approach explicitly accounts for model uncertainty and can make use of covariates.The method is applied to two surveillance datasets maintained by the Centers for Disease Control and Prevention on Rocky Mountain spotted fever (RMSF). An inference is drawn using Markov Chain Monte Carlo simulation techniques in a fully Bayesian framework. The central feature of the model is the ability to calculate and estimate the total number of cases and disease incidence for geographical regions where RMSF is endemic.The information generated by this model could significantly reduce the public health impact of RMSF and other vector-borne zoonoses, as well as other infectious or chronic diseases, by improving knowledge of the spatial distribution of disease risk of public health officials and medical practitioners. More accurate information on populations at high risk would focus attention and resources on specific areas, thereby reducing the morbidity and mortality caused by some of the preventable and treatable diseases.
Buenconsejo
Joan
J
Center for Drugs, Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 22, Rm. 3241, Silver Spring, MD 20993-0002, USA. Joan.Buenconsejo@fda.hhs.gov
Fish
Durland
D
Childs
James E
JE
Holford
Theodore R
TR
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011159:Population Surveillance / Q000379:methods*; D012373:Rocky Mountain Spotted Fever / Q000453:epidemiology; D017062:Small-Area Analysis