Assessing and improving methods and software tools for real-time outbreak response
File(s)
Author(s)
Nash, Rebecca
Type
Thesis
Abstract
Infectious diseases are responsible for millions of preventable deaths per year globally. The frequency and severity of infectious disease outbreaks are increasing due to factors such as urbanisation, globalisation and climate change. In order to identify ways to improve our ability to respond effectively to infectious threats, we must evaluate the strengths and weaknesses of our current methods and software tools for outbreak analytics.
This thesis uses branching process methods for estimating the time-varying reproduction number (Rt) as a case study to reveal the common challenges encountered when responding to outbreaks in realtime. The findings highlight the need for resources to facilitate rapid and accurate parameterisation of models, the importance of extending tools to directly answer policy-relevant questions, and the necessity of accounting for limitations in disease incidence data.
In response to these issues, open source R packages and resources have been developed. An open source database and R package have been created to rapidly parameterise models for a WHO priority disease, Ebola Virus Disease. Additionally, the R package EpiEstim has been extended to estimate the transmission advantage of new pathogen variants in real-time and an Expectation-Maximisation algorithm has been implemented to reconstruct daily incidence data from any temporal aggregation of incidence. These developments prioritise usability alongside functionality, ensuring that they are widely accessible and applicable in a variety of outbreak contexts.
Overall, this thesis emphasises the importance of continuous innovation and evaluation of outbreak analytics tools to meet the evolving challenges of future epidemics and pandemics.
This thesis uses branching process methods for estimating the time-varying reproduction number (Rt) as a case study to reveal the common challenges encountered when responding to outbreaks in realtime. The findings highlight the need for resources to facilitate rapid and accurate parameterisation of models, the importance of extending tools to directly answer policy-relevant questions, and the necessity of accounting for limitations in disease incidence data.
In response to these issues, open source R packages and resources have been developed. An open source database and R package have been created to rapidly parameterise models for a WHO priority disease, Ebola Virus Disease. Additionally, the R package EpiEstim has been extended to estimate the transmission advantage of new pathogen variants in real-time and an Expectation-Maximisation algorithm has been implemented to reconstruct daily incidence data from any temporal aggregation of incidence. These developments prioritise usability alongside functionality, ensuring that they are widely accessible and applicable in a variety of outbreak contexts.
Overall, this thesis emphasises the importance of continuous innovation and evaluation of outbreak analytics tools to meet the evolving challenges of future epidemics and pandemics.
Version
Open Access
Date Issued
2025-01-23
Date Awarded
01/09/2025
License URL
Advisor
Cori, Anne
Nouvellet, Pierre
Sponsor
Medical Research Council (Great Britain)
Grant Number
MR/N014103/1
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)