Exploiting epidemiological surveillance to characterise the transmissibility and severity of emerging pandemic pathogens, and optimise response strategies
File(s)
Author(s)
Perez-Guzman, Pablo N
Type
Thesis
Abstract
In this thesis, I build on a validated SARS-CoV-2 transmission model and particle Markov chain Monte Carlo inference framework to develop a novel parametric method to quantify the effect of intrinsic and effective factors of transmission and severity dynamics of outbreak pathogens. I defined intrinsic factors as those determined by direct pathogen's interaction with a fully susceptible population assuming baseline social contact patterns and healthcare capacity, and effective factors as the time-varying pathogen transmissibility and severity given non-pharmaceutical interventions (NPIs) and changes in healthcare capacity, and the profile of population immunity from prior infection, vaccination or both.
To demonstrate the utility of my method, I conduct two COVID-19 case studies, one with rich surveillance data from England, and another with limited data from Kabwe, Zambia, a peri-urban district economically and demographically representative of low-income settings in the Southern Africa region. In England, I present estimations of the basic and effective reproduction number, infection fatality, infection hospitalisation and hospital fatality ratio the Wildtype, Alpha, Delta and Omicron BA.1 variants, systematically analysing how such key metrics responded to changing NPIs, healthcare, and population immunity (infection, vaccination or both) between March 2020 and February 2022. In Kabwe, I show the all-cause cumulative excess mortality rate between March 2020 and September 2021 was among the highest known for any low-income setting. I further demonstrate that an improved access to vaccines and other basic healthcare commodities could have averted most of this pandemic burden, and conduct an in-depth cost-effectiveness and extended dominance evaluation of a comprehensive range of counterfactual strategies, with earlier access to vaccination and improved healthcare, from the health system’s perspective.
Deriving granular epidemiological insights across all income settings, regardless of data limitation, is critical to build an evidence base for a more equitable global preparedness to outbreak emergencies.
To demonstrate the utility of my method, I conduct two COVID-19 case studies, one with rich surveillance data from England, and another with limited data from Kabwe, Zambia, a peri-urban district economically and demographically representative of low-income settings in the Southern Africa region. In England, I present estimations of the basic and effective reproduction number, infection fatality, infection hospitalisation and hospital fatality ratio the Wildtype, Alpha, Delta and Omicron BA.1 variants, systematically analysing how such key metrics responded to changing NPIs, healthcare, and population immunity (infection, vaccination or both) between March 2020 and February 2022. In Kabwe, I show the all-cause cumulative excess mortality rate between March 2020 and September 2021 was among the highest known for any low-income setting. I further demonstrate that an improved access to vaccines and other basic healthcare commodities could have averted most of this pandemic burden, and conduct an in-depth cost-effectiveness and extended dominance evaluation of a comprehensive range of counterfactual strategies, with earlier access to vaccination and improved healthcare, from the health system’s perspective.
Deriving granular epidemiological insights across all income settings, regardless of data limitation, is critical to build an evidence base for a more equitable global preparedness to outbreak emergencies.
Version
Open Access
Date Issued
2025-03-07
Date Awarded
01/07/2025
Advisor
Hauck, Katharina
Cori, Anne
Verity, Robert
Sponsor
Imperial College London
Medical Research Council (Great Britain)
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)