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Research Roundup: COVID-19 mutations, new COVID-19 drug, tsunami risk

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Each week, The Daily’s Science & Tech section produces a roundup of the most exciting and influential research happening on campus or otherwise related to Stanford. Here’s our digest for the week of May 3-9.

Statistics to track coronavirus mutations

A team of researchers is using statistics to monitor genetic mutations of the novel coronavirus in order to understand the size and spread of COVID-19 among the infected population.

“We’re looking at the genetic diversity of the virus over time,” Julia Palacios, an assistant professor in statistics and biomedical data science, told Stanford Humanities and Sciences News.

“Viruses need to infect individuals to evolve,” she added. “If the virus infects a large number of individuals, you will observe a large number of mutations. But if the infected population is small, the genetic diversity will remain constant. As you get more sequences, you can see if the trend is still growing, remains unchanged or is starting to decay, in which case the infection may have peaked.”

The team is working with hundreds of viral RNA COVID-19 samples to identify mutations across infected individuals, which can provide useful information alongside epidemiological models.

“Having a better estimate of the number of cases is very important for epidemiologists to predict what’s going to happen in the fall,” Palacios told Stanford Humanities and Sciences News. “But there are so many unanswered questions. For example, our analyses show a highly variable mutation rate suggesting highly heterogeneous strains that evolve at different speeds, but the more data we get, the more questions we can ask and try to answer.”

“COVID-19 is affecting everybody, and we’re trying to find ways to help,” she added. “That’s the motivation for my group to work on this.”

Drug trial for non-hospitalized coronavirus patients

Clinical trials for interferon-lambda are currently in progress as researchers evaluate its efficacy in treating non-hospitalized patients with mild cases of COVID-19.

Remdesivir, another drug used to treat coronavirus by emergency use, is only approved for hospitalized patients. A larger portion of coronavirus patients are not hospitalized. Currently, there are zero treatment options for them.

“Even though these individuals may not need hospitalization, infection with COVID-19 results in respiratory symptoms and lost productivity,” Upinder Singh, a professor in infectious diseases and microbiology and immunology, told Stanford Medicine News.

The study will recruit 120 participants diagnosed with mild COVID-19.

“Plus — and this is important — patients with mild disease contribute to community disease transmission,” she added. “Limiting viral shedding from this group would reduce transmission to family members and others, which is crucial to controlling epidemic disease spread.”

Strategically placed hills may decrease tsunami risk

Hills with plants developed strategically alongside a city’s coastline may mitigate tsunami risks better than seawalls, found a study published on May 4 in “Proceedings of the National Academy of Sciences.”

“It’s sort of intuitive that the moment you see it as a threat, you build a wall,” geophysics assistant professor Jenny Suckale told Stanford News. “Most coastal communities want to maximize their well-being, not minimize their risk at the expense of everything else. Do you really want to live behind a huge concrete wall because there is a small chance that a big tsunami will hit you? Let’s put more options on the table and have an informed debate.”

The findings suggest that in models, the hills performed roughly as well as the standard seawall. Cities can alter the hill shape based on the coastline or the direction of incoming tsunamis to potentially further decrease the energy impact of the tidal waves.

“Our study shows that design matters. There’s a wrong and a right spacing; there’s a wrong and a right shape,” Suckale told Stanford News. “You should not use aesthetic criteria to design this.”

Contact Derek Chen at derekc8 ‘at’ stanford.edu.

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