Context-specific emergence and growth of the SARS-CoV-2 Delta variant

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.

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Context-specific emergence and growth of the SARS-CoV-2 Delta variant

10.21203/rs.3.rs-1159614/v1 ◽

2021 ◽

Author(s):

Moritz Kraemer ◽

John McCrone ◽

Verity Hill ◽

Sumali Bajaj ◽

Rosario Evans-Pena ◽

...

Keyword(s):

Local Population ◽

Human Movement ◽

Contact Tracing ◽

Global Pattern ◽

Population Mixing ◽

Heterogeneous Landscape ◽

Contact Patterns ◽

Context Specific ◽

The Uk ◽

Pharmaceutical Interventions

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.

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Weekly population testing could stop this pandemic and prevent the next

Royal Society Open Science ◽

10.1098/rsos.210468 ◽

2021 ◽

Vol 8 (7) ◽

pp. 210468

Author(s):

Julian Peto

Keyword(s):

Vaccination Coverage ◽

Financial Support ◽

Local Population ◽

Contact Tracing ◽

Economic Damage ◽

Population Register ◽

Sample Collection ◽

Effective Population ◽

Rapid Spread ◽

The Uk

The rapid spread of the SARS-COV-2 delta variant in the UK despite high vaccination coverage will inevitably accelerate when social restrictions end unless testing and contact tracing become much more effective. To minimize further social and economic damage, the effect on R of introducing weekly population testing as social restrictions are relaxed should be evaluated. The large increase in testing capacity required can be achieved with self-taken saliva samples analysed by RT-LAMP in local testing facilities. The costs and effectiveness can be evaluated in whole-city demonstration studies. A local population register in each city or district is essential to issue weekly invitations, manage sample collection, monitor results and achieve rapid notification of households and other contacts when a test is positive. In the UK, weekly test invitations should be managed, like vaccination invitations, by the NHS, with social and financial support for quarantined households to make self-isolation acceptable. A framework for effective population testing that had been established and evaluated during this pandemic could be rapidly reinstated to suppress the next pandemic while vaccines for a new and perhaps more deadly virus are developed and rolled out.

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OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

10.1101/2020.09.16.20195925 ◽

2020 ◽

Cited By ~ 4

Author(s):

Robert Hinch ◽

William J M Probert ◽

Anel Nurtay ◽

Michelle Kendall ◽

Chris Wymatt ◽

...

Keyword(s):

Economic Activity ◽

Contact Tracing ◽

Agent Based ◽

Health Measures ◽

The World ◽

The Uk ◽

Spread Of Infection ◽

The Impact ◽

Age Stratification ◽

Pharmaceutical Interventions

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

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Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UK

Royal Society Open Science ◽

10.1098/rsos.201491 ◽

2021 ◽

Vol 8 (3) ◽

Cited By ~ 1

Author(s):

Bobby He ◽

Sheheryar Zaidi ◽

Bryn Elesedy ◽

Michael Hutchinson ◽

Andrei Paleyes ◽

...

Keyword(s):

Contact Tracing ◽

Key Factors ◽

Primary Case ◽

Substantial Impact ◽

Individual Level ◽

Household Contacts ◽

Resource Requirements ◽

The Uk ◽

Overall Effectiveness ◽

Pharmaceutical Interventions

We use an individual-level transmission and contact simulation model to explore the effectiveness and resource requirements of various test-trace-isolate (TTI) strategies for reducing the spread of SARS-CoV-2 in the UK, in the context of different scenarios with varying levels of stringency of non-pharmaceutical interventions. Based on modelling results, we show that self-isolation of symptomatic individuals and quarantine of their household contacts has a substantial impact on the number of new infections generated by each primary case. We further show that adding contact tracing of non-household contacts of confirmed cases to this broader package of interventions reduces the number of new infections otherwise generated by 5–15%. We also explore impact of key factors, such as tracing application adoption and testing delay, on overall effectiveness of TTI.

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OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009146 ◽

2021 ◽

Vol 17 (7) ◽

pp. e1009146

Author(s):

Robert Hinch ◽

William J. M. Probert ◽

Anel Nurtay ◽

Michelle Kendall ◽

Chris Wymant ◽

...

Keyword(s):

Computational Models ◽

Contact Tracing ◽

Agent Based ◽

Health Measures ◽

The World ◽

The Uk ◽

Spread Of Infection ◽

The Impact ◽

Age Stratification ◽

Pharmaceutical Interventions

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

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Quantifying the Shift in Social Contact Patterns in Response to Non-Pharmaceutical Interventions

SSRN Electronic Journal ◽

10.2139/ssrn.3706041 ◽

2020 ◽

Author(s):

Zachary McCarthy ◽

Yanyu Xiao ◽

Francesca Scarabel ◽

Biao Tang ◽

Nicola Luigi Bragazzi ◽

...

Keyword(s):

Social Contact ◽

Contact Patterns ◽

Pharmaceutical Interventions

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Digital proximity tracing on empirical contact networks for pandemic control

Nature Communications ◽

10.1038/s41467-021-21809-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

G. Cencetti ◽

G. Santin ◽

A. Longa ◽

E. Pigani ◽

A. Barrat ◽

...

Keyword(s):

Large Scale ◽

Disease Outbreaks ◽

Physical Distance ◽

Contact Tracing ◽

Reproductive Number ◽

Modeling Framework ◽

Infectious Disease Outbreaks ◽

Contact Patterns ◽

Contact Data ◽

The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

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What do users think of the UK’s three COVID-19 contact-tracing apps? A comparative analysis

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2021-100320 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100320

Author(s):

Vahid Garousi ◽

David Cutting

Keyword(s):

Mobile Apps ◽

Analysis Data ◽

End Users ◽

Contact Tracing ◽

User Reviews ◽

The Public ◽

Engineering Teams ◽

Technical Issues ◽

The Uk ◽

The Right

ObjectivesOur goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: ‘NHS COVID-19’ for England and Wales, ‘StopCOVID NI’ for Northern Ireland and ‘Protect Scotland’ for Scotland. Our two research questions are (1) what are the users’ experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps?MethodsWe assess the popularity of the apps and end users’ perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020).ResultsResults show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working.DiscussionTowards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users’ feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future.ConclusionOur results show that more work is needed by the stakeholders behind the apps (eg, apps’ software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

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