Computers Math

To perform quantum computations, quantum bits (qubits) must be cooled down to temperatures in the millikelvin range (close to -273 Celsius), to slow down atomic motion and minimize noise. However, the electronics used to manage these quantum circuits generate heat, which is difficult to remove at such low temperatures. Most current technologies must therefore separate quantum circuits from their electronic components, causing noise and inefficiencies that hinder the realization of larger quantum systems beyond the lab.
Researchers in EPFL’s Laboratory of Nanoscale Electronics and Structures (LANES), led by Andras Kis, in the School of Engineering have now fabricated a device that not only operates at extremely low temperatures, but does so with efficiency comparable to current technologies at room temperature.
“We are the first to create a device that matches the conversion efficiency of current technologies, but that operates at the low magnetic fields and ultra-low temperatures required for quantum systems. This work is truly a step ahead,” says LANES PhD student Gabriele Pasquale.
The innovative device combines the excellent electrical conductivity of graphene with the semiconductor properties of indium selenide. Only a few atoms thick, it behaves as a two-dimensional object, and this novel combination of materials and structure yields its unprecedented performance. The achievement has been published in Nature Nanotechnology.
Harnessing the Nernst effect
The device exploits the Nernst effect: a complex thermoelectric phenomenon that generates an electrical voltage when a magnetic field is applied perpendicular to an object with a varying temperature. The two-dimensional nature of the lab’s device allows the efficiency of this mechanism to be controlled electrically.
The 2D structure was fabricated at the EPFL Center for MicroNanoTechnology and the LANES lab. Experiments involved using a laser as a heat source, and a specialized dilution refrigerator to reach 100 millikelvin — a temperature even colder than outer space. Converting heat to voltage at such low temperatures is usually extremely challenging, but the novel device and its harnessing of the Nernst effect make this possible, filling a critical gap in quantum technology.
“If you think of a laptop in a cold office, the laptop will still heat up as it operates, causing the temperature of the room to increase as well. In quantum computing systems, there is currently no mechanism to prevent this heat from disturbing the qubits. Our device could provide this necessary cooling,” Pasquale says.
A physicist by training, Pasquale emphasizes that this research is significant because it sheds light on thermopower conversion at low temperatures — an underexplored phenomenon until now. Given the high conversion efficiency and the use of potentially manufacturable electronic components, the LANES team also believes their device could already be integrated into existing low-temperature quantum circuits.
“These findings represent a major advancement in nanotechnology and hold promise for developing advanced cooling technologies essential for quantum computing at millikelvin temperatures,” Pasquale says. “We believe this achievement could revolutionize cooling systems for future technologies.” More

Drosophila — commonly known as fruit flies — are a valuable model for human heart pathophysiology, including cardiac aging and cardiomyopathy. However, a choke point in evaluating fruit fly hearts is the need for human intervention to measure the heart at moments of its largest expansion or its greatest contraction, measurements that allow calculations of cardiac dynamics.
Researchers at the University of Alabama at Birmingham now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and high-speed video microscopy for each heartbeat in the fly.
“Our machine learning method is not just fast; it minimizes human error because you don’t have to manually mark each heart wall under systolic and diastolic conditions,” said Girish Melkani, Ph.D., associate professor in the UAB Department of Pathology, Division of Molecular and Cellular Pathology. “Furthermore, you can run the analyses of several hundred hearts and look at the analyses when done for all the hearts.”
This can expand the ability to test how different environmental or genetic factors affect heart aging or pathology. Melkani envisions using deep learning-assisted studies to explore cardiac mutation models and other small animal models, such as zebrafish and mice. “Additionally, our techniques could be adapted for human heart models, providing valuable insights into cardiac health and disease. Incorporating uncertainty quantification methods could further enhance the reliability of our analyses. Moreover, the machine learning approach can predict cardiac aging with high accuracy.”
The fruit fly model has already been tremendously powerful for understanding the pathophysiological bases for several human cardiovascular diseases, Melkani says. Cardiovascular disease continues to be one of the leading causes of death and disability in the United States.
Melkani and UAB colleagues assessed their trained model on heart performance both in fruit fly cardiac aging and in a fruit fly model of dilated cardiomyopathy caused by the knockdown of a pivotal TCA cycle enzyme, oxoglutarate dehydrogenase. These automated assessments were then validated against existing experimental datasets. For example, for aging of fruit flies at one week versus five weeks of age, which is about halfway through a fruit fly’s life span, the UAB team used 54 hearts for model training and then validated their measurements against an experimental aging model with 177 hearts. Their trained model was able to reconstruct expected trends in cardiac parameters with aging.
Melkani says his team’s model can be applied to readily available consumer hardware, and his team’s code can provide calculated statistics including diastolic and systolic diameters/intervals, fractional shortening, ejection fraction, heart period/rate, and quantified heartbeat arrhythmicity.
“To our knowledge, this innovative platform for deep learning-assisted segmentation is the first of its kind to be applied to standard high-resolution high-speed optical microscopy of Drosophila hearts while also quantifying all relevant parameters,” Melkani said.
“By automating the process and providing detailed cardiac statistics, we pave the way for more accurate, efficient and comprehensive studies of heart function in Drosophila. This method holds tremendous potential — not only for understanding aging and disease in fruit flies — but also for translating these insights into human cardiovascular research.” More

A research team led by Director JO Moon-Ho of the Center for Van der Waals Quantum Solids within the Institute for Basic Science (IBS) has implemented a novel method to achieve epitaxial growth of 1D metallic materials with a width of less than 1 nm. The group applied this process to develop a new structure for 2D semiconductor logic circuits. Notably, they used the 1D metals as a gate electrode of the ultra-miniaturized transistor.
Integrated devices based on two-dimensional (2D) semiconductors, which exhibit excellent properties even at the ultimate limit of material thickness down to the atomic scale, are a major focus of basic and applied research worldwide. However, realizing such ultra-miniaturized transistor devices that can control the electron movement within a few nanometers, let alone developing the manufacturing process for these integrated circuits, has been met with significant technical challenges.
The degree of integration in semiconductor devices is determined by the width and control efficiency of the gate electrode, which controls the flow of electrons in the transistor. In conventional semiconductor fabrication processes, reducing the gate length below a few nanometers is impossible due to the limitations of lithography resolution. To solve this technical problem, the research team leveraged the fact that the mirror twin boundary (MTB) of molybdenum disulfide (MoS2), a 2D semiconductor, is a 1D metal with a width of only 0.4 nm. They used this as a gate electrode to overcome the limitations of the lithography process.
In this study, the 1D MTB metallic phase was achieved by controlling the crystal structure of the existing 2D semiconductor at the atomic level, transforming it into a 1D MTB. This represents a significant breakthrough not only for next-generation semiconductor technology but also for basic materials science, as it demonstrates the large-area synthesis of new material phases through artificial control of crystal structures.
The International Roadmap for Devices and Systems (IRDS) by the IEEE predicts semiconductor node technology to reach around 0.5 nm by 2037, with transistor gate lengths of 12 nm. The research team demonstrated that the channel width modulated by the electric field applied from the 1D MTB gate can be as small as 3.9 nm, significantly exceeding the futuristic prediction.
The 1D MTB-based transistor developed by the research team also offers advantages in circuit performance. Technologies like FinFET or Gate-All-Around, adopted for the miniaturization of silicon semiconductor devices, suffer from parasitic capacitance due to their complex device structures, leading to instability in highly integrated circuits. In contrast, the 1D MTB-based transistor can minimize parasitic capacitance due to its simple structure and extremely narrow gate width.
Director JO Moon-Ho commented, “The 1D metallic phase achieved through epitaxial growth is a new material process that can be applied to ultra-miniaturized semiconductor processes. It is expected to become a key technology for developing various low-power, high-performance electronic devices in the future.” More

As COVID-19 spread globally in 2020, many countries swiftly closed their borders to prevent the disease from entering. However, there was little scientific evidence to support the effectiveness of such measures.
While post-COVID research has extensively focused on the efficacy of internal travel restrictions, cross-border travel has received less attention due to challenges in accessing quality data. In a major multidisciplinary collaboration effort across Finland, Sweden, Norway, and Denmark, a group of researchers — including mathematicians, physicists and computer scientists — have published a pioneering study on the spread of infections across Nordic borders from spring until the end of 2020. The report sheds light on the efficacy of cross-border travel restrictions, helping us better understand which measures actually make a difference.
‘There have been many studies using data and modelling within countries, but this cross-border research is rather unique,’ says Associate Professor of Mathematics Lasse Leskelä from Finland’s Aalto University.
The researchers developed a sophisticated mathematical model relying on a long trail of footwork gathering travel data from the four neighbouring countries. Focus was on the short-term spread of the disease at a stage of the pandemic when infections had already started to spread within each country.
Border closures a blunt tool
The modelling revealed that cross-border closures were only likely to have significant impact in very specific scenarios. For example, a substantial disparity in disease prevalence between two countries would have to be accompanied by a high volume of cross-border traffic for restrictions to notably impact spread. It is notable that even though Sweden’s comparatively loose restrictions in 2020 contributed to the nation having vastly more case numbers than in neighbouring Finland, the overall impact of cross-border travel on the Finnish disease situation was low in absolute terms.
‘The way I see these results is that the closing of borders was mostly not very well justified. This was done out of uncertainty, because countries did not know what else to do. Since it has so many adverse effects, my take on this is that in the future, such drastic measures must be very carefully considered’, says Professor Tapio Ala-Nissilä from Aalto University.

However, the researchers point out that in different stages of a pandemic situation, there can be many layers of complexity. If a government must act, choosing between restricting local populations within its borders versus restricting travel across them, the latter may prove the better option.
‘According to our model, travellers from Sweden were over 10 times more likely to have COVID-19 in the summer of 2020 than the domestic Finnish population. So if you think about when the restrictions should hit and who should be affected, it would make more sense to place restrictions on these travellers at this time,’ Assistant Professor Mikko Kivelä from Aalto University points out.
The model also shows interesting differences between types of travel. Commuters, who may spend half a day in the destination country at a time, played a smaller role in spreading infections than vacationers who possibly spent their entire infectious periods in the country.
Preparing for the next pandemic
Kivelä emphasises that in spring 2020, decision-makers were faced with myriad uncertainties that made it impossible to reliably analyse and estimate the effects of their countermeasures. This is also where the current study makes its most significant contribution — as a predictive model for future use.
‘The really important part is that we have developed different ways of looking at this question: a mathematical machinery to answer questions about what border control interventions are necessary and when to apply,’ says university researcher Mikhail Shubin from the University of Helsinki.
Although the current study pertains to the Nordics, the researchers say that it can be applied to other countries as well. The main concern is getting reliable and comparable data. Often, even if the outward appearance of a particular data set is promising, details like reporting delays will complicate its usage.
‘Access to mobility is not easy to gain, and within the Schengen area in particular there is no detailed tracking for who moves where. You need to have access to lots of data sets, from road crossings to railroads, ferries and aeroplanes. We also used mobile phone data to validate our findings,’ explains Leskelä. ‘Usually, to do this detailed modelling, you need personal contacts and you need to build trust.’
The study is part of the NordicMathCovid project. The project includes teams from Finland, Sweden and Norway and involves a number of universities and public institutions across the Nordics. Supported by NordForsk, the project started in September 2020 and has produced research on pandemic flows and vaccination strategies from varying angles. More

In an ongoing game of cosmic hide and seek, scientists have a new tool that may give them an edge. Physicists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) have developed a computer program incorporating machine learning that could help identify blobs of plasma in outer space known as plasmoids. In a novel twist, the program has been trained using simulated data.
The program will sift through reams of data gathered by spacecraft in the magnetosphere, the region of outer space strongly affected by Earth’s magnetic field, and flag telltale signs of the elusive blobs. Using this technique, scientists hope to learn more about the processes governing magnetic reconnection, a process that occurs in the magnetosphere and throughout the universe that can damage communications satellites and the electrical grid.
Scientists believe that machine learning could improve plasmoid-finding capability, aid the basic understanding of magnetic reconnection and allow researchers to better prepare for the aftermath of reconnection-caused disturbances.
“As far as we know, this is the first time that anyone has used artificial intelligence trained on simulated data to look for plasmoids,” said Kendra Bergstedt, a graduate student in the Princeton Program in Plasma Physics, which is based at PPPL. Bergstedt was the first author of the paper reporting the results in Earth and Space Science. The work pairs the Lab’s growing expertise in computational sciences with its long history of exploring magnetic reconnection.
Looking for a link
Scientists want to find reliable, accurate methods for detecting plasmoids so they can determine whether they affect magnetic reconnection, a process consisting of magnetic field lines separating, violently reattaching and releasing tremendous amounts of energy. When it occurs near Earth, reconnection can trigger a cascade of charged particles falling into the atmosphere, disrupting satellites, mobile phones and the electrical grid. “Some researchers believe that plasmoids aid fast reconnection in large plasmas,” said Hantao Ji, professor of astrophysical sciences at Princeton University and a distinguished research fellow at PPPL. “But those hypotheses haven’t been proven yet.”
The researchers want to know whether plasmoids can change the rate at which reconnection occurs. They also want to gauge how much energy reconnection imparts to the plasma particles. “But to clarify the relationship between plasmoids and reconnection, we have to know where the plasmoids are,” Bergstedt said. “That’s what machine learning could help us do.”
The scientists used computer-generated training data to ensure the program could recognize a range of plasma signatures. Typically, plasmoids created by computer models are idealized versions based on mathematical formulas with shapes — like perfect circles — that do not often occur in nature. If the program were trained only to recognize these perfect versions, it might miss those with other shapes. To prevent those misses, Bergstedt and Ji decided to use artificial, deliberately imperfect data so the program would have an accurate baseline for future studies. “Compared to mathematical models, the real world is messy,” Bergstedt said. “So we decided to let our program learn using data with fluctuations that you would get in actual observations. For instance, rather than beginning our simulations with a perfectly flat electrical current sheet, we give our sheet some wobbles. We’re hoping that the machine learning approach can allow for more nuance than a strict mathematical model can.” This research builds on past attempts in which Bergstedt and Ji wrote computer programs that incorporated more idealized models of plasmoids.

The use of machine learning will only become more common in astrophysics research, according to the scientists. “It could particularly be helpful when making extrapolations from small numbers of measurements, as we sometimes do when studying reconnection,” said Ji. “And the best way to learn how to use a new tool is to actually use it. We don’t want to stand on the sidelines and miss an opportunity.”
Bergstedt and Ji plan to use the plasmoid-detecting program to examine data being gathered by NASA’s Magnetospheric Multiscale (MMS) mission. Launched in 2015 to study reconnection, MMS consists of four spacecraft flying in formation through plasma in the magnetotail, the area in space pointing away from the sun that is controlled by Earth’s magnetic field.
The magnetotail is an ideal place to study reconnection because it combines accessibility with scale. “If we study reconnection by observing the sun, we can only take measurements from afar,” Bergstedt said. “If we observe reconnection in a laboratory, we can put our instruments directly into the plasma, but the sizes of the plasmas would be smaller than those typically found in space.” Studying reconnection in the magnetotail is an ideal middle option. “It’s a large and naturally occurring plasma that we can measure directly using spacecraft that fly through it,” Bergstedt said.
As Bergstedt and Ji improve the plasmoid-detecting program, they hope to take two significant steps. The first is performing a procedure known as domain adaptation, which will help the program analyze datasets that it has never encountered before. The second step involves using the program to analyze data from the MMS spacecraft. “The methodology we demonstrated is mostly a proof of concept since we haven’t aggressively optimized it,” Bergstedt said. “We want to get the model working even better than it is now, start applying it to real data and then we’ll just go from there!”
This research was supported by the DOE’s Fusion Energy Sciences program under contract DE-AC0209CH11466, by NASA under grants NNH15AB29I and 80HQTR21T0105, and by the National Science Foundation Graduate Research Fellowship under grant DGE-2039656. More

The use of wearable electronics that continuously monitor biosignals has transformed the healthcare and fitness industries. These devices are becoming increasingly common and are projected to reach a market valuation of approximately USD 572.06 billion by 2033. With this rapid growth, there is an escalating demand for high-quality bioelectrodes capable of accurately recording biosignals over extended periods. However, many of the materials currently used for bioelectrodes, such as metals, conductive polymers, and hydrogels, have limitations. They often lack the flexibility to stretch the skin without breaking and have low humidity permeability, leading to sweat buildup and discomfort.
To address these limitations, in a study published in the journal NPG Asia Materialson 20 June 2024, a research team led by Assistant Professor Tatsuhiro Horii and Associate Professor Toshinori Fujie from Tokyo Institute of Technology (Tokyo Tech) has developed a bioelectrode material that is stretchable and permeable to humidity and conforms closely to the skin. This innovative material is composed of layers of conductive fibrous networks consisting of single-wall carbon nanotubes (SWCNTs) on a stretchable poly(styrene-b-butadiene-b-styrene) (SBS) nanosheet. The nanosheet conforms tightly to the skin, allowing for precise biosignal measurements, while the carbon nanotube fibers maintain the material’s stretchability and humidity permeability.
“Self-supporting electrodes that are stretchable, permeable to humidity, and conformable to skin surface bumps are required to allow for the natural deformation of skin without restricting body movements,” says Horii.
The researchers applied SWCNTs as aqueous dispersions onto SBS nanosheets, creating multiple layers reaching a thickness of only 431 nm. Each coating of SWCNTs increased the density and thickness of the fibers, modifying the bioelectrode’s characteristics. While adding more SWCNT layers increased nanosheet stiffness (from an initial 48.5 MPa elastic modulus to 60.8 MPa for a single layer and 104.2 MPa for five layers), the bioelectrode maintained impressive flexibility. Pristine SBS nanosheets and those with one or three layers of SWCNTs (SWCNT 3rd-SBS) stretched elastically by 380% of their original length before permanent deformation. This flexibility surpasses metal electrodes like gold, which have Young’s moduli in the several-hundred-GPa range and can only stretch less than 30% of their original length before breaking.
Another crucial requirement for bioelectrodes is high water vapor permeability to prevent sweat buildup during exercise. Adding SWCNTs is beneficial as its fibrous network structure improves breathability compared to continuous films. In experiments measuring water vapor transmission rate (WVTR), researchers found that SWCNT 3rd-SBS exhibited a WVTR of 28,316 g m-2 (2 h)-1, which is twice that of normal skin.
The bioelectrode material is also highly resilient for extended use. To test the material’s durability, the researchers immersed the bioelectrodes in artificial sweat and subjected them to repeated bending, measuring the change in resistance. In these tests, they found that the resistance increased negligibly, by only 1.1 times in sweat and by 1.3 times over 300 cycles of bending. Furthermore, the SWCNT 3rd-SBS nanosheets showed little to no detachment after being rubbed ten times, indicating its suitability for prolonged use.
To assess its real-world performance, the researchers compared an SBS nanosheet with three layers of SWCNT to commercially available bioelectrode materials such as Ag/AgCl gel electrodes. The bioelectrodes were attached to the forearm, and surface electromyography (sEMG) measurements were taken during gripping motions. In this experiment, the performance of the SWCNT-SBS nanosheet was comparable to that of commercial Ag/AgCl gel electrodes, achieving similar signal-to-noise ratios of 24.6 dB and 33.3 dB, respectively.
“We obtained skin-conformable bioelectrodes with high water vapor permeabilities, which showed comparable performance in sEMG measurements to those of conventional electrodes,” concludes Fujie, highlighting the material’s promising capabilities for healthcare wearables. More

The more we discover about the natural world, the more we find that nature is the greatest engineer. Past research believed that liquids can only be transported in fixed direction on species with specific liquid communication properties and cannot switch the transport direction. Recently, The Hong Kong Polytechnic University (PolyU) researchers have shown that an African plant controls water movement in a previously unknown way — and this could inspire breakthroughs in a range of technologies in fluid dynamics and nature-inspired materials, including applications that require multistep and repeated reactions, such as microassays, medical diagnosis and solar desalination etc. The study has been recently published in the international academic journal Science.
Liquid transport is an unsung miracle of nature. Tall trees, for example, have to lift huge amounts of water every day from their roots to their highest leaves, which they accomplish in perfect silence. Some lizards and plants channel water through capillaries. In the desert, where making the most of scarce moisture is vital, some beetles can capture fog-borne water and direct it along their backs using a chemical gradient.
Scientists have long sought to hone humankind’s ability to move liquids directionally. Applications as diverse as microfluidics, water harvesting, and heat transfer depend on the efficient directional transport of water, or other fluids, at small or large scales. While the above species provide nature-based inspiration, they are limited to moving liquids in a single direction. A research team led by Prof. WANG Liqiu, Otto Poon Charitable Foundation Professor in Smart and Sustainable Energy, Chair Professor of Thermal-Fluid and Energy Engineering, Department of Mechanical Engineering of PolyU, has discovered that the succulent plant Crassula muscosa, native to Namibia and South Africa, can transport liquid in selected directions.
Together with colleagues from the University of Hong Kong and Shandong University, the PolyU researchers noticed that when two separate shoots of the plant were infused with the same liquids, the liquids were transported in opposite directions. In one case, the liquid travelled exclusively towards the tip, whereas the other shoot directed the flow straight to the plant root. Given the arid but foggy conditions in which C. muscosa lives, the ability to trap water and transport it in selected directions is a lifeline for the plant.
As the shoots were held horizontally, gravity can be ruled out as the cause of the selective direction of transport. Instead, the plant’s special properties stem from the tiny leaves packed onto its shoots. Also known as “fins,” they have a unique profile, with a swept-back body (resembling a shark’s fin) tapering to a narrow ending that points to the tip of the plant. The asymmetry of this shape is the secret to C. muscosa’s selective directional liquid transport. It all has to do with manipulating the meniscus — the curved surface on top of a liquid.
Specifically, the key lies in subtle differences between the fin shapes on different shoots. When the rows of fins bend sharply towards the tip, the liquid on the shoot also flows in that direction. However, on a shoot whose fins — although still pointing at the tip — have a more upward profile, the direction of movement is instead to the root. The flow direction depends on the angles between the shoot body and the two sides of the fin, as these control the forces exerted on droplets by the meniscus — blocking flow in one direction and sending it in the other.
Armed with this understanding of how the plant directs liquid flow, the team created an artificial mimic. Dubbed CMIAs, for ‘C. muscosa-inspired arrays’, these 3D-printed fins act like the tilted leaves of C. muscosa, controlling the orientation of liquid flow. Cleverly, while the fins on a natural plant shoot are immobile, the use of a magnetic material for artificial CMIAs allows them to be reoriented at will. Simply by applying a magnetic field, the liquid flow through a CMIA can be reversed. This opens up the possibility of liquid transport along dynamically changing paths in industrial and laboratory settings. Alternatively, flow could be redirected by changing the spacing between fins.
Numerous areas of technology stand to benefit from CMIAs. Prof. Wang said, “There are foresee applications of real-time directional control of fluid flow in microfluidics, chemical synthesis, and biomedical diagnostics. The biology-mimicking CMIA design could also be used not just for transporting liquids but for mixing them, for example in a T-shaped valve. The method is suited to a range of chemicals and overcomes the heating problem found in some other microfluidic technologies.” More

A new way to map the spread and evolution of pathogens, and their responses to vaccines and antibiotics, will provide key insights to help predict and prevent future outbreaks. The approach combines a pathogen’s genomic data with human travel patterns, taken from anonymised mobile phone data.
Researchers from the Wellcome Sanger Institute, University of the Witwatersrand and National Institute for Communicable Diseases in South Africa, the University of Cambridge, and partners across the Global Pneumococcal Sequencing project1, integrated genomic data from nearly 7,000 Streptococcus pneumoniae (pneumococcus)samples collected in South Africa with detailed human mobility data2. This enabled them to see how these bacteria, which cause pneumonia and meningitis3, move between regions and evolve over time.
The findings, published today (3 July) in Nature, suggest initial reductions in antibiotic resistance linked to the 2009 pneumococcal vaccine may be only temporary, as non-targeted strains resistant to antibiotics such as penicillin gained a 68 per cent competitive advantage.
This is the first time researchers have been able to precisely quantify the fitness — their ability to survive and reproduce — of different pneumococcal strains. The insight could inform vaccine development to target the most harmful strains, and may be applicable to other pathogens.
Many infectious diseases such as tuberculosis, HIV, and COVID-19 exist in multiple strains or variants circulating simultaneously, making them difficult to study. Pneumococcus, a bacterium that is a leading cause of pneumonia, meningitis, and sepsis worldwide4, is a prime example with over 100 types and 900 genetic strains globally. Pneumonia alone kills around 740,000 children under the age of five each year5, making it the single largest infectious cause of death in children.
Pneumococcal diversity hampers control efforts, as vaccines targeting major strains leave room for others to fill the vacant niches. How these bacteria spread, how vaccines affect their survival, and their resistance to antibiotics remains poorly understood.
In this new study, researchers analysed genome sequences from 6,910 pneumococcus samples collected in South Africa between 2000 and 2014 to track the distribution of different strains over time. They combined these data with anonymised records of human travel patterns collected by Meta2.

The team developed computational models which revealed pneumococcal strains take around 50 years to fully mix throughout South Africa’s population, largely due to localised human movement patterns.
They found that while introduction of a pneumococcal vaccine against certain types of these bacteria in 2009 reduced the number of cases caused by those types6, it also made other non-targeted strains of these bacteria gain a 68 per cent competitive advantage, with an increasing proportion of them becoming resistant to antibiotics such as penicillin. This suggests that the vaccine-linked protection against antibiotic resistance is short-lived.
Dr Sophie Belman, first author of the study, former PhD student at the Wellcome Sanger Institute and now a Schmidt Science Fellow at the Barcelona Supercomputing Centre, Spain, said: “While we found that pneumococcal bacteria generally spread slowly, the use of vaccines and antimicrobials can quickly and significantly change these dynamics. Our models could be applied to other regions and pathogens to better understand and predict pathogen spread, in the context of drug resistance and vaccine effectiveness.”
Dr Anne von Gottberg, author of the study at National Institute for Communicable Diseases, Johannesburg, South Africa, said: “Despite vaccination efforts, pneumonia remains one of the leading causes of death for children under five in South Africa. With continuous genomic surveillance and adaptable vaccination strategies to counter the remarkable adaptability of these pathogens, we may be able to better target interventions to limit the burden of disease.”
Professor Stephen Bentley, senior author of the study at the Wellcome Sanger Institute, said: “The pneumococcus’s diversity has obscured our view on how any given strain spreads from one region to the next. This integrated approach using bacterial genome and human travel data finally allows us to cut through that complexity, uncovering hidden migratory paths in high-definition for the first time. This could allow researchers to anticipate where emerging high-risk strains may take hold next, putting us a step ahead of potential outbreaks.”
Notes Partners from the Global Pneumococcal Sequencing project can be found here: https://www.pneumogen.net/gps/ The human mobility data used in this study are Meta Data for Good baseline data, released during the 2020 SARS-CoV-2 pandemic. These data rely on personal consent for location sharing, and Data for Good ensures individual privacy by preventing re-identification in aggregated datasets. For more information on pneumococcal disease, visit: https://www.cdc.gov/pneumococcal/about/index.html https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5666185/ https://www.who.int/news-room/fact-sheets/detail/pneumonia Before these vaccines, 85 per cent of pneumococcal strains were those targeted by the vaccines. By 2014, this dropped to 33.2 per cent. This change was consistent across all nine provinces in South Africa. More