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Flattening the curve: Stanford researchers create model to show long-term COVID-19 interventions

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This undated electron microscope image made available by the U.S. National Institutes of Health in February 2020 shows the virus that causes COVID-19. The sample was isolated from a patient in the U.S. (NIAID-RML via AP)

STANFORD, Calif., (KRON) —  A lab at Stanford’s Department of Biology developed a web model to show the spread of COVID-19 to evaluate possible outcomes of non-pharmaceutical interventions like social distancing. Stanford researchers created a disease transmission model that explores how different non-pharmaceutical interventions will affect the total amount of cases, hospitalizations and deaths over time. 

Led by Stanford’s Department of Biology Dr. Erin Mordecai, the goal for the web model is to help users understand the benefits from social distancing of “flattening the curve” to stay below a fixed healthcare capacity. 

“We wanted to start a larger conversation about how our long term response might look,” Dr. Mordecai said. 

A potential vaccine may take 12-18 months to be developed. Researchers at Stanford were inspired to explore non-pharmaceutical interventions after reading about it during the 1918 influenza pandemic. During the 1918 influenza, major cities lifted their measures within three to eight weeks only to experience a resurgence of the influenza. 

“On Stanford’s campus and in the greater Bay Area, we’re glad to see leaders taking aggressive action to prevent the spread of this global pandemic,” Mordecai said. “While the initial shelter-in-place order was set to last three weeks, we’re concerned about the potential for the disease to rapidly spread once we lift control measures.”

No social distancing

The graph above presents a hypothetical scenario where a COVID-19 outbreak began on Jan. 15, spreading for 50 days before social distancing was practiced. The lab runs 20 simulations and graphs the median for each day, showing that hospital capacity would be completely overwhelmed. 

Light social distancing

The above graph shows if light social distancing is practiced for an extended period. Resulting in “flattening the curve” but still overwhelming hospital capacity. 

Many local health resources have a fixed capacity. If there are too many cases of COVID-19 at one time, most people won’t be able to be taken care of. If hospitalizations go above capacity it will make it harder to prioritize which patients to see which is already happening in other parts of the world like Italy. 

The more we practice social distancing the flatter the curve will be. 

Delaying the peak

While some local health resources have fixed limits others will become more available over time. Flattening the curve will allow for more resources to be produced like treatments, ventilators and hospital beds. 

The lab shows the above graph if medium and strong social distancing is practiced which can help delay  the time until the number of cases peak. Social distancing gives us time to have resources we would need by then. 

Flattening the curve

According to the lab, if restrictions are lifted too quickly there could be a resurgence where we can see COVID-19 cases pick back up quickly like the events during the 1918 influenza.

The Lightswitch Method 

The lab also demonstrates an alternative longer term scenario involving an on/off trigger for interventions based on London’s Imperial College report released on Mar. 16. The report shows how the United States could avoid a possible resurgence of events by cycling interventions like school closures and social distancing to “turn on” when hospitalizations reach a certain threshold and “turn off” at a certain threshold.

The lab shows the above graph when things are turned on and social distancing practiced, cases start to decrease. When things are turned off, social distancing is not practiced extensively and can make smaller adjustments. 

The lab concludes that cases will increase but won’t grow out of control before we can switch social distancing back on. 

The web model also has an RShiny app included that allows users to test various combinations of interventions. The lab hopes to add additional scenarios, such as the effects of contact tracing and super spreaders.

“We hope that our models can help people to understand the complexities of containing COVID-19, as well as the potential for us to mitigate its effects in the long term,” Mordecai said.

To learn more about the web model — click here


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