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Modelling longitudinal data on respiratory infections to inform health policy


Type

Thesis

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Authors

Abstract

Detecting the start of an outbreak, quantifying its burden, disentangling the contribution of different pathogens and evaluating the effectiveness of an intervention are research questions common to several infectious diseases. The answers to these questions provide the epidemiological understanding to prevent future outbreaks, by informing public health policies such as drug stockpiling, vaccination regimes or non-medical interventions. We investigate the use of statistical models to quantify burden of respiratory disease and evaluate effectiveness of public health interventions, while accounting for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, i.e. what what would have happened in the absence of a contributing factor or intervention. We apply these methods to different types of studies, to address specific gaps in the literature. S. pneumoniae is the leading cause of respiratory morbidity and mortality globally, especially in young children and in the elderly. To improve the understanding of factors triggering disease progression, we firstly analyse individual-level information about pneumococcal carriage and lower respiratory tract infection with a multi-state model, using data from a cohort study in Thailand. Secondly, we clarify the role of viral coinfection and meteorological conditions in invasive pneumococcal disease (IPD) incidence using English surveillance data. A novel multivariate linear regression model is proposed to estimate the influenza-specific contribution additional to the seasonal IPD burden across age groups. We then quantify the impact of the currently implemented vaccination policy, by estimating the counterfactual of IPD incidence in absence of vaccination. This allows disentangling serotype replacement from the vaccine effect, making use of a synthetic control approach. Finally, an empirical dynamical modelling strategy is employed to quantify the interaction between influenza and pneumococcus. Counterfactual analysis can also be employed to quantify the burden of novel respiratory pathogens. The last application of this approach is to estimate the excess mortality during the the COVID-19 pandemic in England.

Description

Date

2020-06-14

Advisors

De Angelis, Daniela

Keywords

public health, surveillance data, epidemic models, biostatistics, respiratory infections, COVID-19

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge