Stanford shows how COVID-19 spread has been contained by travel bans

New supercomputer modeling could play a big part in exit strategies and lifting air travel restrictions

Millions of more people across the EU could have contracted COVID-19 had strict international travel bans not been implemented, shows a new report by supercomputer modeling experts at Stanford University.

Using a newly developed mathematical epidemiology simulation, the study, published in Computer Methods in Biomechanics and Biomedical Engineering, predicts the huge impact that limiting air travel across the 27 EU nations had on restricting the spread of the disease. This image shows how the computer simulator would predict constrained mobility with current travel restrictions, compared to unconstrained mobility without travel restrictions for the days 23 March, 6 April, 20 April.{module INSIDE STORY}

The simulation can show live estimated figures for the growth of spread for each country if we were to remove travel bans today. The images above show how 0.2% of some populations could have become infected by 20th April (when the study was written, 5 April), however, these figures change daily.

This new model could now play a vital part in establishing politicians' exit strategies, with the team able to virtually lift travel restrictions between individual communities, states, or countries, to explore the potential gradual changes in spreading patterns and outbreak dynamics.

"There is a well-reasoned fear that easing of current (travel restriction) measures, even slightly, could trigger a new outbreak and accelerate the spread to an unmanageable degree," lead author Ellen Kuhl, Professor of Mechanical Engineering at Standford University comments.

"Global network mobility models, combined with local epidemiology models, can provide valuable insight into different exit strategies. Our results demonstrate that mathematical modeling can provide guidelines for political decision making with the ultimate goal to gradually return to normal while keeping the rate of new COVID-19 infections steady and manageable," says Kevin Linka, lead author and a postdoctoral researcher in Dr. Kuhl's group.

From its European origin in Italy, the novel coronavirus spread rapidly via the strongest network connections to Germany, Spain, and France, while slowly reaching the less connected countries, Estonia, Slovakia, and Slovenia.

Currently, the levels of the population known to be infected with the disease vary from country to country, however as of April 18, with the flight being reduced by 89% in Germany, 93% in France, 94% in Italy, and 95% in Spain (Eurostat 2020), the graphs in this study show how the spread has been contained.

"Strikingly, our results suggest that the emerging pattern of the COVID-19 outbreak closely followed global mobility patterns of air passenger travel," confirms Professor Kuhl, whose model can also predict the emerging global diffusion pattern of a pandemic at the early stages of the outbreak.

"Our results suggest that unconstrained mobility would have significantly accelerated the spreading of COVID-19, especially in Central Europe, Spain, and France."

Unfortunately, the model also confirms how travel bans were introduced too late to stop the Europe-wide outbreak altogether.

"A recent study based on a global metapopulation disease transmission model for the COVID-19 outbreak in China has shown that the Wuhan travel ban essentially came too late, at a point where most Chinese cities had already received many infected travelers (Chinazzi et al. 2020). Our study shows a similar trend for Europe, where travel restrictions were only implemented a week after every country had reported cases of COVID-19 (European Centre for Disease Prevention and Control 2020).

"As a natural consequence, unfortunately, no European country was protected from the outbreak," Professor Kuhl, who is the Robert Bosch Chair of Mechanical Engineering at Standford added.

The first official case of COVID-19 in Europe was reported in France on January 24, 2020, followed by Germany and Finland only three and five days later. Within only six weeks, all 27 countries of the European Union were affected, with the last cases reported in Malta, Bulgaria, and Cyprus on March 9, 2020. At this point, there were 13,944 active cases within the European Union and the number of active cases doubled every three to four days (European Centre for Disease Prevention and Control 2020).

Dr. Kuhl adds that although air travel is certainly not the only determinant of the outbreak dynamics, their findings indicate that "mobility is a strong contributor to the global spreading of COVID-19". This is becoming especially important now that many countries are beginning to lift their travel restrictions in an attempt to gradually return to normal.

Other limitations highlighted - like any infectious disease model - include the simulation being subject to data uncertainties from differences in testing, inconsistent diagnostics, incomplete counting, and delayed reporting across all countries.

Genomic medicine simulations explain the diversity of cancer evolution

Constructing a mathematical basis for developing therapeutic strategies against cancer using a supercomputer

Understanding the principles of cancer evolution is important in designing a therapeutic strategy. A research group at The Institute of Medical Science, The University of Tokyo (IMSUT) announced a new simulation model that describes various modes of cancer evolution in a unified manner.

"We clarified the conditions under which each of the evolutionary modes is realized by performing simulation analysis using a supercomputer. Our findings have allowed us to explain the underlying principles of the evolutionary diversity of cancer." said lead scientist Atsushi Niida, Senior Assistant Professor, Laboratory of Molecular Medicine, Human Genome Center of IMSUT.

To build a unified evolutionary simulation model 

Cancer can be regarded as a disease of evolution, which results from the natural selection of cells with high proliferative and malignant potential, following the accumulation of mutations in their genomes. Moreover, because cancer has a high evolutionary potential, it easily adapts to treatment-related changes in its environment and acquires therapeutic resistance. Four main evolutionary modes in cancer. Red stars represent normal driver mutations such as single nucleotide mutations. A green star represents large-scale genetic alterations at the chromosomal or genomic level that produce copy number or structural abnormalities. E) An evolutionary model that explains the temporal shift of the principle generating ITH during colorectal tumorigenesis.{module INSIDE STORY} 

Previous genomic studies have shown that cancer evolution can be roughly divided into four modes of evolution. However, it has remained unclear what conditions give rise to each mode. Therefore, the research group has built a unified evolutionary simulation model that can recapitulate a variety of evolutionary modes.

By performing massively parallel simulation with various conditions on SHIROKANE, a supercomputer at IMSUT, the research group determined the conditions under which each evolution mode is generated. By providing a mathematical basis for understanding cancer evolution, this study is expected to contribute to the understanding of therapeutic resistance in cancer and the development of novel therapeutic strategies.

Various mathematical principles that explain the evolution of cancer

Classically, cancer evolution has been regarded as a process of evolution, in which normal cells evolve step-wise into a homogeneous cell population with high malignancy by serially acquiring driver mutations that favor cell growth and survival while undergoing natural selection.

However, in recent years, multi-region sequencing, which analyzes DNA samples obtained from multiple regions of a tumor, has demonstrated intratumor heterogeneity (ITH); that is, multiple clones with different mutations are generated during the course of cancer evolution and coexist in a single tumor. Also, depending on the types of cancers, driver mutations present only in a fraction of cells contribute to ITH; that is, natural selection appears to shape ITH.

On the other hand, it has been found that ITH can be shaped by the accumulation of neutral mutations that do not affect the growth and survival of cells; that is, ITH can be generated by neutral evolution. The evolutionary principle underlying ITH has not only varied among cancer types, but Niida and colleagues have also found that a temporal shift occurs during colorectal tumorigenesis (Saito et al.,2018); that is, it has been found that, in early lesions, ITH is shaped by natural selection while in advanced cancer, ITH is shaped by neutral evolution. Also, in contrast to the gradual evolution that results from the stepwise accumulation of single nucleotide mutations as assumed above, punctuated evolution has attracted attention. This is an evolutionary model in which large changes occur over a short time at the chromosomal and genomic levels, such as copy number changes and structural abnormalities, explosively expanding from a few initial cells.

Thus far, four evolutionary modes have been proposed, but there are many unclear points concerning under what conditions these modes occur. Simulation using an agent-based model is considered to be a useful tool for understanding the principle of cancer evolution. The agent-based model assumes the components of a system called an agent and specifies rules for the autonomous behavior of the agent itself and the interactions between agents and the environment.

Viewing each cell as an agent, ITH can be easily represented by the difference in the internal state of each agent. Niida and colleagues have recently developed MASSIVE, a new methodology of parameter sensitivity analysis, which examines the dynamics of agent-based simulations (Niida et al., 2019). MASSIVE takes an approach completely different from conventional parameter sensitivity analysis methods; it makes it possible to intuitively search a large parameter space by combining massively parallel computing with interactive data visualization.

Based on the above, in order to clarify the conditions under which the above-mentioned four different evolution modes are realized, we have constructed a unified evolution simulation model that can recapitulate various evolution modes using an agent-based model. Parameter sensitivity analysis utilizing MASSIVE was performed on SHIROKANE, a supercomputer of IMSUT. They found that linear evolution occurs when strong driver mutations are assumed, while ITH is generated by natural selection when the driver mutations are weak. Moreover, the simulation revealed that the generation of ITH by neutral evolution requires a high neutral mutation rate, and the presence of cancer stem cells also contributes to neutral evolution by promoting the accumulation of neutral mutations.

Punctuated evolution could also be reproduced by assuming an explosive driver gene that enables cells to overcome the resource limitation required for cell proliferation. Furthermore, it was shown by simulation that punctuated evolution triggers the above-described temporal shift of the principle underlying ITH from natural selection to neutral evolution in colorectal tumorigenesis (Fig. 1E). This result also helps us understand that each mode works not as discrete, exclusive modes but rather blend continuously as a series of phases of cancer evolution.

The results of all the simulation analyses in this research can be searched interactively at https://www.hgc.jp/~aniida/canevosim/index.html.

Contributing to a deeper understanding of therapeutic resistance in cancer

"In this study, we clarified the principle underlying the diversity of cancer evolution by simulation analysis using a supercomputer. Since cancer has a high evolutionary potential, it adapts to the environment changed by therapy and easily acquires therapeutic resistance, so understanding the evolutionary principle of cancer is an important problem for designing therapeutic strategies." Niida emphasized

By providing a mathematical basis for understanding cancer evolution, this study is expected to contribute to the understanding of therapeutic resistance in cancer and the development of novel therapeutic strategies.

Dr. Morton wins £1.2 million to use cutting-edge supercomputer simulations for better understanding of the Sun

Researchers from Northumbria University have been awarded £1.2m to help advance our understanding of the Sun and its impact on the planets within our solar system.

The team, led by solar physicist Dr. Richard Morton, will spend the next four years exploring some of the fascinating phenomena associated with our closest star – including powerful solar winds and the giant, planet-sized concentrations of magnetic fields known as sunspots.

They will use advanced mathematical techniques and cutting-edge supercomputer simulations to create models of the Sun which will provide new insight into the physics behind its activity.

The project, Revealing the Pattern of Solar Alfvénic Waves (RiPSAW), is being funded through UK Research and Innovation (UKRI) after Dr. Morton was awarded a prestigious 2020 UKRI Future Leader Fellowship. {module INSIDE STORY} 230759 web 64397

He will work alongside colleagues at Northumbria, as well as scientists at the National Center for Atmospheric Research and Harvard Smithsonian Centre for Astrophysics in the United States, and the Instituto de Astrofísica de Canaria in Spain.

The aim of the RiPSAW project is to examine the role of magnetic waves in the heating the Sun’s atmosphere to a million degrees and generating powerful solar winds. As Dr. Morton explains: “Many stars possess their own weather systems, although these systems are extreme compared to those we experience on Earth.

“In our solar system, a hot, million-degree wind blows off the Sun at colossal speeds reaching millions of miles per hour, washing over the planets.

“The Earth’s magnetic field protects us by deflecting this wind, but other planetary bodies in the solar system have been exposed to its influence – for example, the Sun’s wind is known to have stripped Mars of its atmosphere.

“We know the Sun loses over 10 trillion tonnes of material each year through its winds, so are also interested in finding out how these winds contribute to a star’s evolution, and how they might influence the habitability of exoplanets around other Sun-like stars.”

Magnetic waves, also known as Alfvén waves, can transfer energy through a star’s atmosphere and are considered an important feature of any magnetic star.

Exciting results from Dr. Morton’s recent observations of the Sun have found evidence that magnetic waves high in the atmosphere react to sound waves leaking out from the inside of the Sun, challenging our current knowledge of how energy is transported through a star’s atmosphere.

The RiPSAW project will use new methods drawn from statistics and machine learning to analyze high-quality data of the Sun from state-of-the-art solar instruments, such as NASA’s Solar Dynamic Observatory.

Dr. Morton is among 90 academics awarded a 2020 UKRI Future Leader Fellowship. The scheme aims to grow the strong supply of talented individuals needed to ensure a vibrant environment for research and innovation in the UK.

The Fellowships are open to researchers and innovators from across the business, universities, and other organizations. Investment of up to £1.5 million over four years is available to enable the next generation of researchers to benefit from outstanding support to develop their careers, and to work on difficult and novel challenges.

Speaking about all 90 fellows announced, Sir Mark Walport, Chief Executive of UK Research and Innovation said: “The Future Leaders Fellowships are UKRI’s flagship talent program, designed to foster and nurture the research and innovation leaders of the future. We are delighted to support these outstanding researchers and innovators across universities, research organizations, and businesses.”

Dr. Morton added: “I feel very grateful to have been awarded such a fantastic fellowship. It will make a huge difference in my career and provides a fantastic opportunity for me to build and develop my own research group. I can’t wait to get started.”