Cox processes for estimating temporal variation in disease risk

Marina Silva Paez, Peter J. Diggle

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)981-1003
Number of pages23
JournalEnvironmetrics
Volume20
Issue number8
DOIs
StatePublished - Dec 2009
Externally publishedYes

Fingerprint

Cox Process
temporal variation
Count
Temporal Aggregation
Bayesian inference
Infection
Parameter Estimation
Incidence
Disjoint
Interval
methodology
Methodology
Prediction
prediction
Demonstrate
Model

Keywords

  • Bayesian inference
  • Cox processs
  • Disease surveillance
  • Gastrointestinal disease
  • Monte carlo inference
  • Point process

ASJC Scopus subject areas

  • Ecological Modeling
  • Statistics and Probability

Cite this

Cox processes for estimating temporal variation in disease risk. / Paez, Marina Silva; Diggle, Peter J.

In: Environmetrics, Vol. 20, No. 8, 12.2009, p. 981-1003.

Research output: Contribution to journalArticle

Paez, Marina Silva ; Diggle, Peter J. / Cox processes for estimating temporal variation in disease risk. In: Environmetrics. 2009 ; Vol. 20, No. 8. pp. 981-1003.
@article{28fb302a8aad4575a12b3aea2cd79979,
title = "Cox processes for estimating temporal variation in disease risk",
abstract = "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.",
keywords = "Bayesian inference, Cox processs, Disease surveillance, Gastrointestinal disease, Monte carlo inference, Point process",
author = "Paez, {Marina Silva} and Diggle, {Peter J.}",
year = "2009",
month = "12",
doi = "10.1002/env.976",
language = "English (US)",
volume = "20",
pages = "981--1003",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "8",

}

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.

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.

KW - Bayesian inference

KW - Cox processs

KW - Disease surveillance

KW - Gastrointestinal disease

KW - Monte carlo inference

KW - Point process

UR - http://www.scopus.com/inward/record.url?scp=73249152034&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73249152034&partnerID=8YFLogxK

U2 - 10.1002/env.976

DO - 10.1002/env.976

M3 - Article

VL - 20

SP - 981

EP - 1003

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 8

ER -