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Statistical modeling and analysis of partially observed infectious diseases

Statistical modeling and analysis of partially observed infectious diseases
Statistical modeling and analysis of partially observed infectious diseases
This thesis is concerned with the development of Bayesian inference approach for the analysis of infectious disease models. Stochastic SIS household-based epidemic models were considered with individuals allowed to be contracted locally at a given rate and there also exists a global force of infection. The study covers both when the population of interest is assumed to be constant and when the population is allowed to vary over time. It also covers when the global force of infection is constant and when it is spatially varying as a function of some unobserved Gaussian random fields realizations. In addition, we also considered diseases coinfection models allowing multiple strains transmission and recovery. For each model, Bayesian inference approach was developed and implemented via MCMC framework using extensive data augmentation schema. Throughout, we consider two most prevalent forms of endemic disease data- the individual-based data and the aggregate-based data. The models and Bayesian approach were tested with simulated data sets and successfully applied to real-life data sets of tick-borne diseases among Tanzania cattle.
Lancaster University
Nnanatu, Chibuzor Christopher
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Nnanatu, Chibuzor Christopher
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Neal, Peter
06d584e3-8648-408c-9bf8-b6171c34cf1e

Nnanatu, Chibuzor Christopher (2018) Statistical modeling and analysis of partially observed infectious diseases. Lancaster University, Doctoral Thesis, 213pp.

Record type: Thesis (Doctoral)

Abstract

This thesis is concerned with the development of Bayesian inference approach for the analysis of infectious disease models. Stochastic SIS household-based epidemic models were considered with individuals allowed to be contracted locally at a given rate and there also exists a global force of infection. The study covers both when the population of interest is assumed to be constant and when the population is allowed to vary over time. It also covers when the global force of infection is constant and when it is spatially varying as a function of some unobserved Gaussian random fields realizations. In addition, we also considered diseases coinfection models allowing multiple strains transmission and recovery. For each model, Bayesian inference approach was developed and implemented via MCMC framework using extensive data augmentation schema. Throughout, we consider two most prevalent forms of endemic disease data- the individual-based data and the aggregate-based data. The models and Bayesian approach were tested with simulated data sets and successfully applied to real-life data sets of tick-borne diseases among Tanzania cattle.

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More information

Published date: 15 August 2018

Identifiers

Local EPrints ID: 455625
URI: http://eprints.soton.ac.uk/id/eprint/455625
PURE UUID: 49f59fbc-7e6f-4466-acae-fa20f8439ff1
ORCID for Chibuzor Christopher Nnanatu: ORCID iD orcid.org/0000-0002-5841-3700

Catalogue record

Date deposited: 29 Mar 2022 16:47
Last modified: 12 Jun 2024 02:04

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Contributors

Author: Chibuzor Christopher Nnanatu ORCID iD
Thesis advisor: Peter Neal

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