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Mathematical Tools for Understanding Infectious Disease Dynamics$
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Odo Diekmann, Hans Heesterbeek, and Tom Britton

Print publication date: 2012

Print ISBN-13: 9780691155395

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691155395.001.0001

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Inference, or how to deduce conclusions from data

Inference, or how to deduce conclusions from data

Chapter:
(p.127) Chapter Five Inference, or how to deduce conclusions from data
Source:
Mathematical Tools for Understanding Infectious Disease Dynamics
Author(s):

Odo Diekmann

Hans Heesterbeek

Tom Britton

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691155395.003.0005

This chapter describes methods for making inferences about key epidemiological parameters from available data. The chapter presents the powerful statistical method called maximum-likelihood (ML) which is illustrated in the context of a simple transmission model for intensive care units (ICU). This is further developed to derive estimators for the parameter length of stay in an ICU. The chapter then returns to the prototype stochastic epidemic model of Chapter 3 and derives inference methods for key parameters of this model, both for the situation where the epidemic is observed continuously and the situation where only the final size of the outbreak is observed. Finally, the chapter returns to the ICU situation, but now considers a model with transmission leading to dependencies. Model parameters are again estimated by ML-inference with the aid of counting processes.

Keywords:   infectious disease, epidemiology, epidemiological parameters, maximum likelihood estimation, ICU model, intensive care units, stochastic epidemic model

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