Tracking measles infection through non-linear state space models

Shi Chen, John Fricks, Matthew J. Ferrari

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Estimating the burden of infectious disease is complicated by the general tendency for underreporting of cases. When the reporting rate is unknown, conventional methods have relied on accounting methods that do not make explicit use of surveillance data or the temporal dynamics of transmission and infection. State space models are a framework for various methods that allow dynamic models to be fitted with partially or imperfectly observed surveillance data. State space models are an appealing approach to burden estimation as they combine expert knowledge in the form of an underlying dynamic model but make explicit use of surveillance data to estimate parameter values, to predict unobserved elements of the model and to provide standard errors for estimates.

Original languageEnglish (US)
Pages (from-to)117-134
Number of pages18
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume61
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

Fingerprint

State-space Model
Surveillance
Nonlinear Model
Infection
Dynamic Model
Infectious Diseases
Standard error
Estimate
Predict
Unknown
State-space model
Burden
Model

Keywords

  • Burden estimation
  • Disease surveillance
  • Extended Kalman filter
  • Susceptible-infected-recovered model
  • Under-reporting

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Tracking measles infection through non-linear state space models. / Chen, Shi; Fricks, John; Ferrari, Matthew J.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 61, No. 1, 01.2012, p. 117-134.

Research output: Contribution to journalArticle

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