̽��ֱ�� of Cambridge - computational modelling

UK modelling study finds case isolation and contact tracing vital to COVID-19 epidemic control

sc604 — Tue, 16 Jun 2020 22:30:00 +0000

Using social-contact data on more than 40,000 individuals from the BBC Pandemic database to simulate SARS-CoV-2 transmission in different settings and under different combinations of control measures, the researchers estimate that a high incidence of COVID-19 would require a considerable number of individuals to be quarantined to control infection. For example, a scenario in which 5,000 new symptomatic cases were diagnosed each day would likely require 150,000–200,000 contacts to be quarantined every day if no physical distancing was in place.

̽��ֱ��study is the first time researchers have used social contact data to quantify the potential impact of control measures on reducing individual-level transmission of SARS-CoV-2 in specific settings. They aimed to identify not only what would theoretically control transmission, but what the practical implications of these measures would be in terms of numbers quarantined.

However, the authors note that the model is based on a series of assumptions about the effectiveness of testing, tracing, isolation, and quarantine—for example about the amount of time it takes to isolate cases with symptoms (average 2.6 days) and the likelihood that their contacts adhere to quarantine (90%)—which, although plausible, are optimistic.

“Our findings reinforce the growing body of evidence which suggests that we can’t rely on one single public health measure to achieve epidemic control,” said Dr Adam Kucharski from the London School of Hygiene & Tropical Medicine. “Successful strategies will likely include intensive testing and contact tracing supplemented with moderate forms of physical distancing, such as limiting the size of social gatherings and remote working, which can both reduce transmission and the number of contacts that need to be traced.”

He adds: “ ̽��ֱ��huge scale of testing and contact tracing that is needed to reduce COVID-19 from spreading is resource intensive, and new app-based tracing, if adopted widely alongside traditional contact tracing, could enhance the effectiveness of identifying contacts, particularly those that would otherwise be missed.”

In the study, researchers analysed data on how 40,162 people moved about the UK and interacted with others prior to COVID-19 to simulate how combinations of different testing, isolation, tracing, and physical distancing scenarios—such as app-based tracing, remote working, limits on different sized gatherings, and mass population-based testing—might contribute to reducing secondary cases [3]. They also modelled the rate at which the virus is transmitted—known as the reproductive number (R), or the average number of people each individual with the virus is likely to infect at a given moment—under different strategies. To keep the COVID-19 epidemic declining, R needs to be less than 1.

In the model, the secondary attack rate (the probability that a close contact of a confirmed case will be infected) was assumed to be 20% among household contacts and 6% among other contacts. ̽��ֱ��researchers calculated that, had no control measures been implemented, R would be 2.6—meaning that one infected person would infect, on average, 2–3 more people.

̽��ֱ��model suggested that mass testing alone, with 5% of the population undergoing random testing each week (i.e. 460,000 tests per day in UK), would lower R to just 2.5, because so many infections would either be missed or detected too late (table 3 and infographic).

Compared with no control measures, self-isolation of symptomatic cases (at home) alone reduced transmission by an estimated 29% (lowering R to 1.8); whilst combining self-isolation, household quarantine, and tracing strategies could potentially lower transmission by as much as 47% (R 1.4) when using app-based contact tracing (assuming the app is adopted by 53% of the population), and by 64% with manual tracing of all contacts (R 0.94).

Achieving such a thorough level of contact tracing may be impractical, but the new study suggests that a large reduction in transmission could also be achieved by supplementing with moderate physical distancing measures. For example, they estimate that, limiting daily contacts outside home, school, and work to four people (e.g. by restricting mass gatherings) along with manual tracing of acquaintances only (i.e. people they have met before) and app-based tracing, would have the greatest impact, reducing disease spread by 66%, and lowering R to 0.87. However, they note that the effectiveness of manual contact tracing strategies is highly dependent on how many contacts are successfully traced, with a high level of tracing required to ensure R is lower than 1, especially if it takes time to isolate symptomatic cases.

̽��ֱ��researchers also modelled the number of contacts that might need to be quarantined under different contact tracing strategies. They estimate that a scenario in which 1,000 new symptomatic cases were reported daily would likely require a minimum of 15,000 contacts quarantined every day (isolation plus app-based testing) and a maximum of 41,000 (isolation plus manual tracing all contacts). This could increase to an average of 150,000–200,000 contacts quarantined daily in a scenario where 5,000 new symptomatic cases were diagnosed each day (table 4).

“Our results highlight several characteristics of SARS-CoV-2 which make effective isolation and contact tracing challenging. ̽��ֱ��high rate of transmission, the short time between one person becoming infected and infecting another, and transmission that occurs without symptoms all make things difficult,” said co-author Dr Hannah Fry from ̽��ֱ�� College London. “If there are a lot of symptomatic COVID-19 cases, then tracing, testing, and trying to quarantine a huge number of contacts will be a big challenge. How well we manage it will affect how and when it is possible to reduce transmission predominantly through targeted isolation and tracing measures or whether ongoing physical distancing measures will be required to control the epidemic.”

According to co-author Professor Julia Gog from Cambridge’s Department of Applied Mathematics and Theoretical Physics, “Planning for control based on isolation and contact tracing should consider the likely need for large numbers of cases to be tested and also a large number of contacts rapidly quarantined. Crucially, this work is able to quantify the scales of what is needed for a successful control strategy involving tracing and isolation by making use of the dataset from the BBC pandemic project. ̽��ֱ��BBC data gives a uniquely detailed picture of how people in the UK mix and the extent of contact tracing that will be necessary if we return to social mixing patterns as they were before the pandemic.”

̽��ֱ��authors highlight several limitations to their study, including that it did not consider more detailed settings beyond home, school, work, or ‘other’ categories, or explicitly include imported infections, which may be detected at a different rate to local infections.

Writing in a linked Comment, Professor Raina MacIntyre (who was not involved in the study) from ̽��ֱ�� ̽��ֱ�� of New South Wales, Australia, says, “Whilst the study is specific to the UK, the findings are relevant to all countries. For countries which are opening up for business and resuming social activities, as social contacts increase, non-pharmaceutical interventions become even more critical. It may even be worthwhile for countries to invest in strategies to vastly improve the uptake of contact tracing apps to enable rapid response to resurgence of COVID-19. If you don’t trace, you leave a chain of transmission free to grow undetected and exponentially. With 80% of cases being mild, it may take several generations of silent epidemic growth before it is even recognised.”

Reference:
Adam J Kucharski et al. 'Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study.' ̽��ֱ��Lancet Infectious Diseases (2020). DOI: 10.1016/ S1473-3099(20)30457-6

Adapted from a press release by ̽��ֱ��Lancet.

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

In the absence of a vaccine or highly effective treatments for COVID-19, combining isolation and intensive contact tracing with physical distancing measures—such as limits on daily social or workplace contacts—might be the most effective and efficient way to achieve and maintain epidemic control, according to new modelling research published in ̽��ֱ��Lancet Infectious Diseases journal.

̽��ֱ��BBC data gives a uniquely detailed picture of how people in the UK mix and the extent of contact tracing that will be necessary if we return to social mixing patterns as they were before the pandemic

Julia Gog

Tim Dennell

Coronavirus (COVID-19) Sheffield, UK

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Licence type:

Attribution-Noncommerical

Meteorite impact turns silica into stishovite in a billionth of a second

Anonymous — Tue, 13 Oct 2015 12:49:29 +0000

̽��ֱ��Barringer meteor crater is an iconic Arizona landmark, more than 1km wide and 170 metres deep, left behind by a massive 300,000 tonne meteorite that hit Earth 50,000 years ago with a force equivalent to a ten megaton nuclear bomb. ̽��ֱ��forces unleashed by such an impact are hard to comprehend, but a team of Stanford scientists has recreated the conditions experienced during the first billionths of a second as the meteor struck in order to reveal the effects it had on the rock underneath.

̽��ֱ��sandstone rocks of Arizona were, on that day of impact 50,000 years ago, pushed beyond their limits and momentarily – for the first few trillionths and billionths of a second – transformed into a new state. ̽��ֱ��Stanford scientists, in a study published in the journal Nature Materials, recreated the conditions as the impact shockwave passed through the ground through computer models of half a million atoms of silica. Blasted by fragments of an asteroid that fell to Earth at tens of kilometres a second, the silica quartz crystals in the sandstone rocks would have experienced pressures of hundreds of thousands of atmospheres, and temperatures of thousands of degrees Celsius.

What the model reveals is that atoms form an immensely dense structure almost instantaneously as the shock wave hits at more than 7km/s. Within ten trillionths of a second the silica has reached temperatures of around 3,000℃ and pressures of more than half a million atmospheres. Then, within the next billionth of a second, the dense silica crystallises into a very rare mineral called stishovite.

̽��ֱ��results are particularly exciting because stishovite is exactly the mineral found in shocked rocks at the Barringer Crater and similar sites across the globe. Indeed, stishovite (named after a Russian high-pressure physics researcher) was first found at the Barringer Crater in 1962. ̽��ֱ��latest simulations give an insight into the birth of mineral grains in the first moments of meteorite impact.

Simulations show how crystals form in billionths of a second

̽��ֱ��size of the crystals that form in the impact event appears to be indicative of the size and nature of the impact. ̽��ֱ��simulations arrive at crystals of stishovite very similar to the range of sizes actually observed in geological samples of asteroid impacts.

Studying transformations of minerals such as quartz, the commonest mineral of Earth’s continental crust, under such extreme conditions of temperature and pressure is challenging. To measure what happens on such short timescales adds another degree of complexity to the problem.

These computer models point the way forward, and will guide experimentalists in the studies of shock events in the future. In the next few years we can expect to see these computer simulations backed up with further laboratory studies of impact events using the next generation of X-ray instruments, called X-ray free electron lasers, which have the potential to “see” materials transform under the same conditions and on the same sorts of timescales.

Simon Redfern, Professor in Earth Sciences, ̽��ֱ�� of Cambridge

This article was originally published on ̽��ֱ��Conversation. Read the original article.

Inset image: Barringer meteor Crater, Arizona (NASA Earth Observatory).

̽��ֱ��opinions expressed in this article are those of the individual author(s) and do not represent the views of the ̽��ֱ�� of Cambridge.

Simon Redfern from the Department of Earth Sciences discusses a study that has recreated the conditions experienced during the meteor strike that formed the Barringer Crater in Arizona.

United States Geological Survey/D. Roddy

Barringer Crater aerial photo

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

What impact will new technology have on tackling emissions?

sj387 — Mon, 21 Oct 2013 09:14:01 +0000

Computational models provide unparalleled insight into current and future demand for water, land and energy, and the impact these demands have on greenhouse gas (GHG) emissions and the environment. What if we could also take into account the fast pace at which new technologies are evolving? This is the aim of a new project in the Cambridge Centre for Climate Change Mitigation Research (4CMR) in the ̽��ֱ�� of Cambridge’s Department of Land Economy.

Dr Jean-Francois Mercure, who leads the research, asserts that building this factor into models will help understanding of the degree to which improvements in energy-consuming technologies and their adoption can help governments reduce emissions: “Technology comes to life through innovation, timely investments and policy incentives, and so it’s important to include technology diffusion and its pace in energy modelling.

“However, this is challenging and most models today attempt to calculate cost-optimal technology roadmaps based on current technology, which is not necessarily likely to happen, and which disregard the process by which new technology regimes come to existence, but also how old technologies endure.”

Technological change occurs constantly, either following innovations in industrial systems or through evolutions of behaviours, such as in the adoption of electric cars. Earlier this year, with funding from the Engineering and Physical Sciences Research Council, Mercure began work on a computational modelling system that takes into account the profile of technology transitions in the past to project how new transitions could arise in the future.

To do so, he is collaborating with environmental scientists at the Tyndall Centre at the ̽��ֱ�� of East Anglia and at the Open ̽��ֱ��, policy advisors and researchers at the UK Department for Energy and Climate Change and the Committee for Climate Change, and applied economists at Cambridge Econometrics.

Mercure believes that this will be the first time an energy–economy–environment model at the global level simultaneously considers technology diffusion in all sectors of energy use alongside natural resource constraints and the interaction between sectors.

“If the global power sector is to decarbonise by 2050 without there being significant economic costs then all countries must make a contribution to the development of renewable technologies,” he added.

“Take as an example the solar photovoltaic industry. Large investments in Germany enabled production costs of firms in China to decline significantly in recent years, which could not have occurred without such investments. Technology sectors typically face a classic vicious circle: established technologies thrive because they are established, and emerging technologies see barriers to their diffusion due to the lock-in of established technologies. This will be the case unless an emerging technology is a radical improvement over established technologies, or it benefits from strong policy support and investment. This applies to many other sectors such as mobility technologies, industry and household appliances.”

Professor Douglas Crawford-Brown, Director of 4CMR, is excited by the prospects of this new modelling: “Dr Mercure’s work sits nicely at the intersection of aggregated economic sectors and the decisions of individual investors. He is plotting an intermediate ground in which both theories of investor behaviour and empirical econometrics allow for much greater insights into energy supply and demand.”

Mercure’s recent research has focused on the global electricity sector, which currently emits 38% of global fuel combustion emissions mostly through the use of fossil fuels. ̽��ֱ��new project will extend the model to all major energy-consuming sectors, including transport, industry (e.g. steel, cement) and buildings (heating, appliances), to model different scenarios of support policies for technological change.

“We want to be able to answer questions about the impact of policy changes on global emissions. Badly coordinated roadmaps of technological change can lead to increases in GHG emissions and so it’s important to know which types of policies will incentivise efficient emissions reductions in order to avoid dangerous climate change.”

New research seeks to take account of the fast pace at which technology is evolving in understanding how to tackle greenhouse gas emissions.

Technology comes to life through innovation, timely investments and policy incentives

Jean-Francois Mercure

Paul Krueger

Car2Go Electric Car Sharing

This work is licensed under a Creative Commons Licence. If you use this content on your site please link back to this page.

Yes