Austin News

The number of people currently in care and being admitted to the hospital for COVID-19 infections is more real-time accurate than deaths and less subject to various biasing factors than case numbers, according to a data analyst.

“Hospitalizations are one of the most accurate and objective metrics we have on the health impact of COVID in the community,” said Samir Patel, director at Askeladden Capital in Austin, told Austin News. “Case counts dramatically understate the actual number of infections.”

Austin Public Health has moved away from using hospitalizations as a key indicator for 'gating' toward COVID-19 reopening, according to media reports.

Fox 7 Austin reported there is a decrease in hospital admissions attributed to COVID-19 and yet Travis County has not yet moved to Stage 3, which would allow dining, shopping and traveling.

“There has been a clear trend of public health agencies 'moving the goalposts,'” Patel told Austin News. “Lockdowns and business closures were initially envisaged as a two to three-week step to 'flatten the curve' to provide hospitals with time to prepare for a surge of COVID patients. Hospitalizations have decreased dramatically and yet many states and counties are still, half a year later, under restrictions initially envisaged to last a few weeks to a month at most.”

As of Sept. 1, there have been a total of 26,516 reported positive COVID-19 cases in Travis County, where Austin is located, according to the Texas Department of State Health Services Dashboard (DSHS). That’s compared to 617,333 coronavirus cases and 12,681 fatalities statewide.

“Given that interventions have severe consequences, such as reducing opportunities for exercise, social connection, and education - all of which are important to both physical and mental health - it doesn't promote public health to panic over COVID cases if they aren't leading to actual, meaningful illness,” said Patel, who studied biochemistry before transitioning to business.

But interventions such as bar closures and masks did not materially alter COVID spread, according to a study by data scientist and MIT alumnus Youyang Gu.

“The data demonstrates that most of the interventions we accept, such as social distancing, masks, business closures and so on, may have some impact, but, generally, have a much smaller impact than is commonly believed,” Patel said. “Stated differently, politicians and public health agencies are obviously incentivized to claim causality, such that they can assign blame for rising COVID prevalence."