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1.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.01.02.23284109

ABSTRACT

SARS-CoV-2 variants of concern (VOCs) arise against the backdrop of increasingly heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron genomes, we identified >6,000 independent introductions of the antigenically distinct virus into England and reconstructed the dispersal history of resulting local transmission. Travel restrictions on southern Africa did not reduce BA.1 importation intensity as secondary hubs became major exporters. We explored potential drivers of BA.1 spread across England and discovered an early period during which viral lineage movements mainly occurred between larger cities, followed by a multi-focal spatial expansion shaped by shorter distance mobility patterns. We also found evidence that disease incidence impacted human commuting behaviours around major travel hubs. Our results offer a detailed characterisation of processes that drive the invasion of an emerging VOC across multiple spatial scales and provide unique insights on the interplay between disease spread and human mobility.

2.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.05.16.22275147

ABSTRACT

Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters, such as the time-varying reproduction number, Rt at time t, are often inferred from incident time series, with the aim of informing policymakers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to those time series. While studies have proposed corrections for these issues, methodology for formally assessing how these noise sources degrade Rt estimate quality is lacking. By adapting Fisher information and experimental design theory, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections. This yields a novel metric, defined by the geometric means of reporting and cumulative delay probabilities, for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring Rt: epidemic case and death curves. We show that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.

3.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.01.07.21268513

ABSTRACT

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of non-pharmaceutical interventions correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

4.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.14.21267606

ABSTRACT

The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases. The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter- regional travel drove Delta's nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Delta's invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.

5.
Raquel Viana; Sikhulile Moyo; Daniel Gyamfi Amoako; Houriiyah Tegally; Cathrine Scheepers; Richard J Lessells; Jennifer Giandhari; Nicole Wolter; Josie Everatt; Andrew Rambaut; Christian Althaus; Eduan Wilkinson; Adriano Mendes; Amy Strydom; Michaela Davids; Simnikiwe Mayaphi; Simani Gaseitsiwe; Wonderful T Choga; Dorcas Maruapula; Boitumelo Zuze; Botshelo Radibe; Legodile Koopile; Roger Shapiro; Shahin Lockman; Mpaphi B. Mbulawa; Thongbotho Mphoyakgosi; Pamela Smith-Lawrence; Mosepele Mosepele; Mogomotsi Matshaba; Kereng Masupu; Mohammed Chand; Charity Joseph; Lesego Kuate-Lere; Onalethatha Lesetedi-Mafoko; Kgomotso Moruisi; Lesley Scott; Wendy Stevens; Constantinos Kurt Wibmer; Anele Mnguni; Arshad Ismail; Boitshoko Mahlangu; Darren P. Martin; Verity Hill; Rachel Colquhoun; Modisa S. Motswaledi; James Emmanuel San; Noxolo Ntuli; Gerald Motsatsi; Sureshnee Pillay; Thabo Mohale; Upasana Ramphal; Yeshnee Naidoo; Naume Tebeila; Marta Giovanetti; Koleka Mlisana; Carolyn Williamson; Nei-yuan Hsiao; Nokukhanya Msomi; Kamela Mahlakwane; Susan Engelbrecht; Tongai Maponga; Wolfgang Preiser; Zinhle Makatini; Oluwakemi Laguda-Akingba; Lavanya Singh; Ugochukwu J. Anyaneji; Monika Moir; Stephanie van Wyk; Derek Tshiabuila; Yajna Ramphal; Arisha Maharaj; Sergei Pond; Alexander G Lucaci; Steven Weaver; Maciej F Boni; Koen Deforche; Kathleen Subramoney; Diana Hardie; Gert Marais; Deelan Doolabh; Rageema Joseph; Nokuzola Mbhele; Luicer Olubayo; Arash Iranzadeh; Alexander E Zarebski; Joseph Tsui; Moritz UG Kraemer; Oliver G Pybus; Dominique Goedhals; Phillip Armand Bester; Martin M Nyaga; Peter N Mwangi; Allison Glass; Florette Treurnicht; Marietjie Venter; Jinal N. Bhiman; Anne von Gottberg; Tulio de Oliveira.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.19.21268028

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants respectively. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

6.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.12.09.20246207

ABSTRACT

BackgroundLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. FindingsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1{middle dot}60, 95% CI: 1{middle dot}48 - 1{middle dot}74) and were more likely to be hospitalised between April and July, 2020 (OR: 1{middle dot}08, 95% CI: 1{middle dot}04 - 1{middle dot}12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1{middle dot}37, 95% CI: 1{middle dot}32 - 1{middle dot}41; OR: 1{middle dot}23, 95% CI: 1{middle dot}21 - 1{middle dot}25, respectively), and were more likely to die (OR: 1{middle dot}14, 95% CI: 1{middle dot}07 - 1{middle dot}21; 1{middle dot}09, 95% CI: 1{middle dot}05 - 1{middle dot}13, respectively). InterpretationLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities. FundingThis project was supported by a Medical Research Council-Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). This work received funding from the U.K. Medical Research Council under a concordat with the U.K. Department for International Development.

7.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.10.23.20218446

ABSTRACT

The UK's COVID-19 epidemic during early 2020 was one of world's largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the country's first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown were larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whilst lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.

8.
biorxiv; 2020.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2020.08.19.225854

ABSTRACT

COVID-19 (coronavirus disease 2019) is a pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) infection affecting millions of persons around the world. There is an urgent unmet need to provide an easy-to-produce, affordable medicine to prevent transmission and provide early treatment for this disease. The nasal cavity and the rhinopharynx are the sites of initial replication of SARS-CoV-2. Therefore, a nasal spray may be a suitable dosage form for this purpose. The main objective of our study was to test the antiviral action of three candidate nasal spray formulations against SARS-CoV-2. We have found that iota-carrageenan in concentrations as low as 6 {micro}g/ mL inhibits SARS-CoV-2 infection in Vero cell cultures. The concentrations found to be active in vitro against SARS-CoV-2 may be easily achieved by the application of nasal sprays already marketed in several countries. Xylitol at a concentration of 5 % m/V has proved to be viricidal on its own and the association with iota-carrageenan may be beneficial, as well.

9.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.08.18.20177147

ABSTRACT

Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions.

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