Disease Mapping

Spatio-temporal pattern of two common cancers among Iranian woman: An adaptive smoothing model

Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women

Cervical cancer in women is one of the most common cancers and breast cancer has grown dramatically in recent years. The purpose of this study was to map the incidence of breast and cervix uteri cancer among Iranian women over a 6-year period …

Trends of breast cancer incidence in Iran during 2004-2008: A bayesian space-time model

Background: Breast cancer is the most frequently diagnosed cancer in women and estimating its relative risks and trends of incidence at the area-level is helpful for health policy makers. However, traditional methods of estimation which do not take …

Bayesian space-time analysis of Echinococcus multilocularis-infections in foxes.

A total of 26,220 foxes that were hunted or found dead in Thuringia, Germany, between 1990 and 2009 were examined for infection with Echinococcus multilocularis, the causative agent of human alveolar echinococcosis, and 6853 animals were found …

Multivariate Disease Mapping of Seven Prevalent Cancers in Iran using a Shared Component Model.

Background: The aim of this study was to model the geographical variation in incidence and risk factors of seven prevalent cancers in Iran. Methods: The data for cancers of esophagus, stomach, bladder, colorectal, lung, prostate, and female breast …

A two-component model for counts of infectious diseases.

We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two …

Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data

We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West Germany. The data are stratified by age group, year, and district. Using an age-period-cohort model with an additional spatial component, our goal is to …

A Bayesian model for spatial wildlife disease prevalence data

The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of wildlife population surveys are often based on …