Tyler O'Neal, Staff Editor BIG DATA

Dattner's modeling shows kids half as susceptible to COVID-19 as adults

New findings could deepen understanding of spread and inform public health policies

A new computational analysis suggests that people under the age of 20 are about half as susceptible to COVID-19 infection as adults, and they are less likely to infect others. Itai Dattner of the University of Haifa, Israel, and colleagues present these findings in the open-access journal PLOS Computational Biology https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008559 . A little girl in a medical mask stands on the street during the COVID 19 Coronavirus pandemic. She is holding a toy teddy bear, who is also wearing a medical mask. CREDIT Nik Anderson, www.vperemen.com{module INSIDE STORY}

Earlier studies have found differences in symptoms and the clinical course of COVID-19 in children compared to adults. Others have reported that a lower proportion of children are diagnosed compared to older age groups. However, only a few studies have compared transmission patterns between age groups, and their conclusions are not definitive.

To better understand susceptibility and infectivity of children, Dattner and colleagues fitted mathematical and statistical models of transmission within households to a dataset of COVID-19 testing results from the dense city of Bnei Brak, Israel. The dataset covered 637 households whose members all underwent PCR testing for active infection in spring of 2020. Some individuals also received serology testing for SARS-CoV-2 antibodies.

By adjusting model parameters to fit the data, the researchers found that people under 20 are 43 percent as susceptible as people over 20. With an infectivity estimated at 63 percent of that of adults, children are also less likely to spread COVID-19 to others. The researchers also found that children are more likely than adults to receive a negative PCR result despite actually being infected.

These findings could explain worldwide reports that a lower proportion of children are diagnosed compared to adults. They could help inform mathematical modeling of COVID-19 dynamics, public health policy, and control measures. Future computational research could explore transmission dynamics in other settings, such as nursing homes and schools.

"When we began this research, understanding children's role in transmission was a top priority, in connection with the question of reopening schools," Dattner says. "It was exciting to work in a large, multidisciplinary team, which was assembled by the Israeli Ministry of Health to address this topic rapidly."

Institute of Industrial Science at The University of Tokyo researchers discover new law of phase separation

Tyler O'Neal, Staff Editor BIG DATA

Researchers from Institute of Industrial Science at The University of Tokyo investigated the mechanism of phase separation into the two phases with very different particle mobilities using supercomputer simulations. They found that slow dynamics of complex connected networks control the rate of demixing, which can assist in the design of new functional porous materials, like lithium-ion batteries.

According to the old adage, oil and water don't mix. If you try to do it anyway, you will see the fascinating process of phase separation, in which the two immiscible liquids spontaneously "demix." In this case, the minority phase always forms droplets. Contrary to this, the researchers found that if one phase has much slower dynamics than the other phase, even the minority phase form complex networks instead of droplets. For example, in phase separation of colloidal suspensions (or protein solutions), the colloid-rich (or protein-rich) phase with slow dynamics forms a space-spanning network structure. The network structure thickens and coarsens with time while having the remarkable property of looking similar over a range of length scales, so the individual parts resemble the whole. Researchers at The University of Tokyo discover a new law about how the complex network of phase-separated structures grows with time, which may lead to more efficient batteries and industrial catalysts{module INSIDE STORY}

In the case of spontaneous demixing, the self-similar property causes the typical size of the domains to increase as a function of the elapsed time while obeying a power law. Classical theories predict that the growth exponent of the domains should be 1/3 and 1 for droplet or bicontinuous structures, respectively. However, for network-forming phase separation, it has not been explored how the structure grows or if there is such a law.

Now, using large-scale supercomputer simulations, researchers at The University of Tokyo studied how the typical size of phase domains grows over time when a system is deeply quenched. "In such a situation, the particle mobility can be significantly different between the two phases, and then, the classical theory does not necessarily apply, " first author Michio Tateno says. The team studied the phase separation of a fluid into a gas and liquid and the demixing of a colloidal suspension consisting of insoluble particles and a liquid, using molecular dynamics simulations and hydrodynamic calculations, respectively. They found that the minority phase of slow dynamics universally forms a network structure that grows with a growth exponent of 1/2, and provided a theoretical explanation for the mechanism.

"Significant differences in the particle mobility between the two phases plays a critical role in controlling the speed of the demixing process," senior author Hajime Tanaka says. Because many devices, like rechargeable batteries and catalysts, rely on the creation of intricate porous networks, this research may lead to advances in these areas. In addition, it may shed light on certain cellular functions that have been hypothesized to be controlled by internal biological phase separations.

Australian scientists build model to aid patient-doctor discussions on bone fracture risk

Tyler O'Neal, Staff Editor BIG DATA

Researchers at the Garvan Institute of Medical Research have developed a computational model to calculate 'skeletal age', a personalised estimate of an individual's risk of bone fracture and premature death.

The skeletal age calculator, which will be accessible to doctors and health professionals, aims to better identify those at risk of a first bone fracture and subsequent fractures, and also estimates how fractures impact life expectancy.

Osteoporosis, a disease which reduces bone strength and increases bone fracture risk, is a major national health issue and estimated to affect over 900,000 Australians. The cost of osteoporosis and fracture in Australia is $3.4 billion annually.

"A fracture shortens life expectancy, even more so in men than in women. But there is a lot of complacency in the community when it comes to bone health - only 20% of those with fragility fractures are taking approved treatments for osteoporosis, which could significantly reduce their risk of further fractures," says Professor Tuan Nguyen, Head of the Genetic Epidemiology of Osteoporosis Lab at Garvan, Professor of Predictive Medicine at the University of Technology Sydney and senior author of the research published in eLife. Professor Tuan Nguyen

"We hope that calculating a person's skeletal age, which may be much higher than their actual age, will identify those who are at higher risk of fractures and encourage them to speak to their doctor about how to better manage their condition." {module INSIDE STORY}

Predicting fracture risk

From age 50, bone fractures affect one in two women and one in three men. For women, the lifetime risk of a hip fracture is equal to or higher than the risk of developing an invasive breast cancer.

With each fracture, the risk of future fracture increases two-fold and studies have shown that pre-existing fractures increase the risk of premature death by about 50% in both men and women. One in three adults over 50 dies within 12 months of sustaining a hip fracture.

"There are existing models to predict the risk of an initial fracture, such as the Garvan Fracture Risk Calculator that is already available to doctors. But it remains unclear why some individuals do well after an initial fracture, while others go on to sustain further fractures and have a higher risk of mortality," says first author Dr Thao Ho-Le.

"We set out to develop a model to complement existing tools, which could simultaneously predict an individual's risk of subsequent fractures and consequently, their chance of premature death."

To develop their sophisticated computational model, the team led by Professor Nguyen used data from Garvan's Dubbo Osteoporosis Epidemiology Study, which was started in 1989 and is the world's longest-running large-scale study of osteoporosis in men and women.

Their model incorporates an individual's age, bone density, history of previous fractures and other health conditions to calculate a personalised estimate of 'skeletal age'.

"In our new model, we quantified the intricate transitions between fracture, re-fracture and mortality. We define skeletal age as the age of an individual's skeleton that results from their risk factors for fracture," explains Professor Nguyen.

"Using this definition, we for instance estimated that a typical 70 year old man who had sustained a fracture had a skeletal age of 75 years. But when the man had a second fracture his skeletal age rose to 87 years. This means the individual now has the same fracture risk profile as an 87 year old man who has a healthy risk profile."

Improving bone health

The team is now developing an online calculator, which doctors will be able to use to calculate their patients' skeletal age.

The researchers hope it will be a valuable tool for initiating discussions between health professionals and their patients on how to improve bone health, which may involve medication, exercise, increasing dietary calcium and getting enough vitamin D.

"The key message of this study is that it's never too early to think about your bone health," says Professor Nguyen. "Do not wait until a fracture has occurred to take preventive action. If your skeletal age is higher than your actual age, you should seek medical advice from your doctor on how to manage the higher risk."