Guest column

Houston data startup analyzes COVID-19 risks as companies return to work

Statistical Vision shares key data points it's watching as companies return to work amid the COVID-19 outbreak. Getty Images

In an effort to better help our clients, and frankly all of us, maneuver these uncertain times and to better understand what the upcoming months are likely to bring, we have applied our data science expertise to create a structural model of the spread of COVID-19. The aim of the national model is to determine specifically how mobility and weather impact the local transmission rates while controlling for population density, population immunity rate and the fact that people are taking more precautions.

When I discuss COVID19 with other Houstonians, I'm often asked "We're going back to work — there's traffic! Why haven't cases spiked?"

First, it is worth noting that cases and deaths have increased again in Harris County. But, the question is still valid. Greg Abbott started allowing things to open six weeks ago and we are only starting to see a rise now.

Fortunately, our model (being quantitative and multivariate) can explain why cases may not have 'spiked' the way that was expected. There are four main reasons why cases are only starting to tick up now:

  1. There has not been a 'spike' in people leaving their homes. While Greg Abbott did allow restaurants, movie theaters and malls to begin re-opening on April 30, there was not a sudden spike in people leaving their homes. Indeed, the "people staying home" index, according to Google Mobility Data, peaked on April 1 at 22 percent above normal and has gradually decreased ever since. In Harris County, the extent people are staying at home stands at 14 percent above normal as of May 29 (unfortunately, Google Mobility Data reports 7 to 10 days after the fact.) So, from the peak 'stay at home' measure, we were only a third the way back to normal last week.
  2. Temperatures have increased. Our model indicates that warmer temperatures decrease the transmission rate of COVID19. Our model does not posit a mechanism, but we can rule out both geographic explanations and behavioral explanations, which leaves us with the compelling reason to believe that temperatures matter.
  3. People's behaviors when they do go out have changed. These changes — everything from masks, to skipping hand shakes, to readily available hand sanitizer, to keeping your distance, to staying home when you're feeling sick — have an important and measurable impact on the spread.
  4. Kids are not back in school yet. While our model does not directly measure the impact of kids being in school, the estimate our model produces measuring the importance of staying at home (2.6) is higher than they should be (2, mathematically speaking). We suspect that's because we are missing an important cohort that started staying at home at the same time mobile phone users started staying at home - kids that don't have cell phones. So, while it may seem like we are most of the way back to normal, with regards to going out, being summer time in Houston, kids are not at school, which is likely keeping the rate of spread down.
All of that said, the gradual increase in people leaving their homes has had an impact. And now, cases and deaths are starting to increase. Our model reminds us that there are a variety of factors impacting the transmission rate. Right now, temperatures, people's behaviors and schools being out work in our favor. Come September, two of those three will turn the other way.
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Michael Griebe is the co-founder and chief statistical officer at Houston-based Statistical Vision. To read more about the company's initiative, click here.

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This UH engineer is hoping to make his mark on cancer detection. Photo via UH.edu

Early stage cancer is hard to detect, mostly because traditional diagnostic imaging cannot detect tumors smaller than a certain size. One Houston innovator is looking to change that.

Wei-Chuan Shih, professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering, recently published his findings in IEEE Sensors journal. According to a news release from UH, the cells around cancer tumors are small — ~30-150nm in diameter — and complex, and the precise detection of these exosome-carried biomarkers with molecular specificity has been elusive, until now.

"This work demonstrates, for the first time, that the strong synergy of arrayed radiative coupling and substrate undercut can enable high-performance biosensing in the visible light spectrum where high-quality, low-cost silicon detectors are readily available for point-of-care application," says Shih in the release. "The result is a remarkable sensitivity improvement, with a refractive index sensitivity increase from 207 nm/RIU to 578 nm/RIU."

Wei-Chuan Shih is a professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering. Photo via UH.edu

What Shih has done is essentially restored the electric field around nanodisks, providing accessibility to an otherwise buried enhanced electric field. Nanodisks are antibody-functionalized artificial nanostructures which help capture exosomes with molecular specificity.

"We report radiatively coupled arrayed gold nanodisks on invisible substrate (AGNIS) as a label-free (no need for fluorescent labels), cost-effective, and high-performance platform for molecularly specific exosome biosensing. The AGNIS substrate has been fabricated by wafer-scale nanosphere lithography without the need for costly lithography," says Shih in the release.

This process speeds up screening of the surface proteins of exosomes for diagnostics and biomarker discovery. Current exosome profiling — which relies primarily on DNA sequencing technology, fluorescent techniques such as flow cytometry, or enzyme-linked immunosorbent assay (ELISA) — is labor-intensive and costly. Shih's goal is to amplify the signal by developing the label-free technique, lowering the cost and making diagnosis easier and equitable.

"By decorating the gold nanodisks surface with different antibodies (e.g., CD9, CD63, and CD81), label-free exosome profiling has shown increased expression of all three surface proteins in cancer-derived exosomes," said Shih. "The sensitivity for detecting exosomes is within 112-600 (exosomes/μL), which would be sufficient in many clinical applications."

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