TY - JOUR

T1 - Cox processes for estimating temporal variation in disease risk

AU - Paez, Marina Silva

AU - Diggle, Peter J.

PY - 2009/12

Y1 - 2009/12

N2 - We propose a class of Cox processes as models for the times of occurrence of cases of a disease, and develop associated methods of Bayesian inference for parameter estimation and for prediction of the temporal variation in disease risk. The data may consist of either incidence times of individual cases or counts of the numbers of incident cases in disjoint time-intervals. We explore the consequences of working with different levels of temporal aggregation of the data. We use a simulated example to demonstrate the feasibility of our methodology, which we then apply to data giving daily counts of incident cases of gastrointestinal infections in the county of Hampshire, UK. Copyright (C) 2009 John Wiley & Sons, Ltd.

AB - We propose a class of Cox processes as models for the times of occurrence of cases of a disease, and develop associated methods of Bayesian inference for parameter estimation and for prediction of the temporal variation in disease risk. The data may consist of either incidence times of individual cases or counts of the numbers of incident cases in disjoint time-intervals. We explore the consequences of working with different levels of temporal aggregation of the data. We use a simulated example to demonstrate the feasibility of our methodology, which we then apply to data giving daily counts of incident cases of gastrointestinal infections in the county of Hampshire, UK. Copyright (C) 2009 John Wiley & Sons, Ltd.

KW - Bayesian inference

KW - Cox processs

KW - disease surveillance

KW - gastrointestinal disease

KW - Monte Carlo inference

KW - point process

KW - LINEAR MIXED MODELS

U2 - 10.1002/env.976

DO - 10.1002/env.976

M3 - Journal article

VL - 20

SP - 981

EP - 1003

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 8

ER -