Genomic epidemiology of SARS-CoV-2: from outbreak investigations, to national and international surveillance efforts

The response of the global genomics community to the SARS-CoV-2 pandemic has been unprecedented. At time of writing there are more than 3.7 million SARS-CoV-2 genome sequences shared publicly on GISAID (www.gisaid.org). This scale of data on that order of magnitude presents novel opportunities and challenges for the field of genomic epidemiology. This thesis describes the development, validation and implementation of novel tools to facilitate different aspects of genomic epidemiology, from outbreak investigations to surveillance efforts. The Pango nomenclature lineage system is a set of rules that defines epidemiological lineages of SARS-CoV-2. Pango defines lineages from whole genome sequences, which 195 nations around the world have been producing for SARS-CoV-2. In chapter 1, I discuss the development and validation of pangolin, a software tool developed to assign the most likely Pango lineage to novel SARS-CoV-2 genomes. Initially, pangolin used a classic phylogenetic approach to assign lineages although further methods were trialled and implemented as the pandemic progressed to cope with the scale of and analytical challenges associated with SARS-CoV-2 data. Since it was first implemented, millions of SARSCoV-2 genomes have been assigned lineages with the pangolin tool from users across the world. For a number of reasons, labs may not be in a position to produce full genome sequences. Chapter 2 investigates how the lineage system can be used if only spike nucleotide sequences are available and defines ‘lineage sets’ that summarise what lineage information exists within a given spike haplotype. We find that for many lineages, including the main lineages corresponding to the WHO-defined variants of concern (VOCs), the spike nucleotide sequence is sufficient to distinguish Pango lineages and I describe the development of a software tool hedgehog that is a wrapper for pangolin that both defines and assigns these spike-based lineage sets. Pango lineage assignments with pangolin have been used almost ubiquitously across the globe and provide a simple, quick piece of information to classify SARS-CoV-2 genomes. However, for both outbreak investigations and routine surveillance, a more in-depth analysis is needed to give more than just this one piece of information. In chapter 3, I present civet, a software tool that addresses the challenge of the SARS-Cov-2 global dataset that is on the order of 3.7 million sequences and performs robust phylogenetic analyses on query sequences of interest, whilst contextualising them in the background data. Using civet, the user can produce an interactive report that summarises genomic, phylogenetic and epidemiological information, enabling routine analyses and investigations to be carried out in a single command. The suite of tools in this thesis have been developed to enable researchers to rapidly get robust and actionable information from SARS-CoV-2 genomes for genomic epidemiology efforts worldwide

Covid-19 and Capitalism

This open access book provides a comprehensive analysis of the socioeconomic determinants of Covid-19. From the end of 2019 until presently, the world has been ravaged by the Covid-19 pandemic. Although the cause of this is (obviously) a virus, the extent to which this virus spread, and therefore the number of infections and deaths, was largely determined by socio-economic factors. From this, it follows that the course of the pandemic varies greatly from one country to another. This observation applies both to countries’ resilience to such a pandemic (which is mainly rooted in the period preceding the outbreak of the virus) and to the way in which countries have reacted to the virus (including the political choices on how to respond). Meanwhile, research has made it clear that the nature of this response (e.g., elimination policy, mitigation policy, and proceeding herd immunity) was, on the one hand, strongly determined by political and ideological factors and, on the other hand, was highly influential in the factors of success or failure in combating the pandemic. The book focuses on the situation in a number of Western regions (notably the USA, the UK, and the EU and its Member States). The author addresses the reasons why in many Western countries both pandemic prevention and response policies to Covid-19 have failed. The book concludes with recommendations concerning the rearrangement of the socio-economic order that could increase the resilience of (Western) societies against such pandemics

Spatiotemporal analyses of visceral leishmaniasis in the Indian subcontinent

The neglected tropical disease visceral leishmaniasis (VL) has greatly burdened vulnerable populations in the Indian subcontinent. The analyses in this thesis are motivated by observations from VL field epidemiology that cases cluster in space & time. Using a household-level dataset from a highly-endemic Bangladeshi village covering the years 2002–2010, I investigate spatiotemporal clustering using the tau statistic to estimate the magnitude & spatial range of clustering of cases to inform control interventions and to validate a recent mechanistic model result. Then, for Vaishali district, India, I employ a spatiotemporal statistical model to assess if an intensified intervention pilot during 2015–2017 was successful and how many cases may have been averted while accounting for district-level clustering of incidence. To deliver high-quality insights, several novel advances in methodologies were made. A literature review of the tau statistic was performed that detailed its existing uses & methods of inference to assess the presence of spatiotemporal clustering and estimate the range of clustering around cases. This prompted corrections & improvements in inference methods leading to higher precision in clustering estimates than a previous baseline analysis on a measles dataset. A new rate estimator for the tau statistic was created to account for variable person-time at risk in the Bangladeshi study. Finally, customisations in the use of the surveillance & hhh4addon R packages were made to perform an interrupted time series analysis for the Vaishali study. The findings of this thesis contribute to the current VL discourse by quantifying spatiotemporal clustering around cases, partially validating a recent result on clustering and giving a rigorous evaluation of a control pilot that may be required if incidence recrudesces. For spatiotemporal statistics, improvements in the tau statistic and the new applications of these R packages offer valuable examples in methodology & code for other infectious diseases. I summarise the findings of this thesis and list further research opportunities in VL, which I hope to explore as my career in infectious disease modelling progresses