### Supplementary data References Rapid response Subject collections

, 2010

"... Plug-and-play inference for disease dynamics: measles in ..."

### unknown title

"... Plug-and-play inference for disease dynamics: measles in large and small populations as a case study ..."

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Plug-and-play inference for disease dynamics: measles in large and small populations as a case study

### Collaborators

, 2009

"... Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 “bird flu ” influenza, H1N1 “swine flu ” influenza) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA) Ed Ionides ..."

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Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 “bird flu ” influenza, H1N1 “swine flu ” influenza) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA) Ed Ionides Infectious disease dynamics: a statistical perspective 4 Disease dynamics: epidemiology or ecology, or both? • Environmental host/pathogen dynamics are close to predator/prey relationships which are a central topic of ecology. • Analysis of diseases as ecosystems complements more traditional epidemiology (risk factors etc). • Ecologists typically seek to avoid extinctions, whereas epidemiologists typically seek the reverse. Ed Ionides Infectious disease dynamics: a statistical perspective 5 Infectious disease transmission: the statistical challenge • Time series data of sufficient quantity and quality to support investigations of disease dynamics are increasingly available.

### Iterated Filtering

, 2011

"... Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of fi ..."

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Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right. 1

### ITERATED FILTERING 1

"... Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of fi ..."

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Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right. 1. Introduction. Partially observed Markov process (POMP) models are

### Ed Ionides Feature matching versus likelihood for Nicholson’s blowflies 1 Feature matching versus likelihood for dynamic systems: Nicholson’s

, 2011

"... blowflies as a case study ..."

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### DEDICATION.................................

, 2010

"... Time series analysis for nonlinear dynamical systems with applications to modeling of infectious diseases by ..."

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Time series analysis for nonlinear dynamical systems with applications to modeling of infectious diseases by

### Ed Ionides Infectious disease dynamics: a statistical perspective 1 Infectious disease dynamics: a statistical perspective CCMB/Bioinformatics Seminar

, 2009

"... Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 influenza “bird flu”) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA “the hospital super-bug”) Ed Ionides Infe ..."

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Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 influenza “bird flu”) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA “the hospital super-bug”) Ed Ionides Infectious disease dynamics: a statistical perspective 4 Disease dynamics: epidemiology or ecology, or both? • Environmental host/pathogen dynamics are close to predator/prey relationships which are a central topic of ecology. • Analysis of diseases as ecosystems complements more traditional epidemiology (risk factors etc). • Ecologists typically seek to avoid extinctions, whereas epidemiologists typically seek the reverse. Things are not always this simple... – Helicobacter pylori bacteria used to live in the stomach of most humans. Some strains cause stomach ulcers and cancer. It is almost extinct in the developed world due to widespread use of