Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation

Kirsten Elise Eilertson, John Fricks, Matthew Joseph Ferrari

Research output: Contribution to journalArticle

Abstract

Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State-space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state-space model of measles transmission and vaccine-based interventions at the country-level and a particle filter-based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age-structure to allow explicit representation of age-targeted vaccine interventions. We illustrate the performance of estimators of model parameters and predictions of unobserved states on simulated data from two dynamic models: one on the annual time-scale of observations and one on the biweekly time-scale of the epidemiological dynamics. We show that our model results in approximately unbiased estimates of unobserved burden and the underreporting rate. We further illustrate the performance of the fitted model for prediction of future disease burden in the next one to 15 years.

Original languageEnglish (US)
Pages (from-to)4146-4158
Number of pages13
JournalStatistics in Medicine
Volume38
Issue number21
DOIs
StatePublished - Jan 1 2019

Fingerprint

Particle Filtering
Measles
Maximum Likelihood Estimation
Space Simulation
Prediction
Vaccine
State-space Model
Population
Measles Vaccine
Incidence
Time Scales
Partial Observation
Age Structure
Population Structure
Communicable Diseases
Infectious Diseases
Particle Filter
Vaccines
Observation
Model

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Cite this

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Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation. / Eilertson, Kirsten Elise; Fricks, John; Ferrari, Matthew Joseph.

In: Statistics in Medicine, Vol. 38, No. 21, 01.01.2019, p. 4146-4158.

Research output: Contribution to journalArticle

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