Some US states are planning to reopen their economies after 6 or more weeks of being shut down. Some fear COVID-19 will return if this happens. A new online model may help them predict how social distancing will impact the spread of the virus during reopening.
Public health officials are wringing their hands over the reopening of state economies in the midst of the COVID-19 coronavirus pandemic. Now, researchers at Georgia Tech University and Massachusetts General Hospital have created an online tool to help these folks predict how the virus will respond to this in light of differing social-distancing measures.
The COVID-19 Simulator may sound like a really bad arcade game, but it’s actually a tool that may help officials understand how lifting or extending various social-distancing practices will impact how COVID-19 spreads. It should help them predict the number of virus cases, hospitalizations and deaths by factoring in the most recent state data and the infectious disease modeling recommended by expert epidemiologists.
The tool compares three different intervention scenarios: lifting social-distancing restrictions, extending stay-at-home orders (outside travel only for essential needs) and a full lockdown like those imposed in Italy, India and China. It then projects the rate of new cases, the overall load on hospitals and the health care system and projected numbers of deaths for each intervention scenario.
The tool offers a single-state view of trends, comparing the intervention scenarios in a single state, state-by-state comparisons during a similar time-frame and a national-level analysis that provides a look at the trends for the US as a whole under different intervention scenarios. Having comparable data in one place allows public health officials a much better inventory of possible outcomes to compare when making decisions.
According to the tool, Georgia may be moving a little too fast in lifting restrictions and reopening businesses. The researchers claim there is a possibility of a second wave of infections. By their numbers, Georgia’s current reopening plan could result in over 23,000 deaths. By keeping the previous restrictions in place until July, that number can be pared to just 2,000 deaths.
They feel that, instead of a restrictive 12-week long stay-at-home order that may crush the economy, a more restrictive lockdown order instituted for just 4 weeks would offer the best outcome relative to cases and deaths while still preparing Georgia to open business as soon as May 25th. Apparently, none of the researchers are stuck at home with school-aged kids!
“Policy makers need to make decisions quickly—our analysis shows that even a week’s time can have a huge impact on the future trajectory of COVID-19. We developed the COVID-19 Simulator to inform such key decisions under this highly uncertain environment,” said lead investigator Jagpreet Chhatwal, Ph.D., a Senior Scientist at the MGH Institute for Technology Assessment and an Assistant Professor at Harvard Medical School.
The creators of the tool admit that there is a glitch. Because we still have so much to learn about COVID-19 and how it is spread, developing a prediction model is difficult. “COVID-19 Simulator innovatively combines epidemiological and statistical modeling to utilize best evidence as they arise, and we plan to continue to advance our approach as we learn more about the disease,” said Turgay Ayer, Ph.D., the George Family Foundation Early Career Professor in H. Milton Stewart School of Industrial and Systems Engineering and the Director of Business Intelligence and Healthcare Analytics at the Center for Health and Humanitarian Systems at Georgia Institute of Technology.
The original models from the Institute for Health Metrics and Evaluation (IHME) and others were incorrect by millions, in terms of both cases and deaths. As testing for both the virus and antibodies to it ramps up, we’re discovering that more people have had it than we thought. We’re also realizing that many of those had no idea they had it.
These things all tell us the virus isn’t as deadly as we thought. For a model like the one described herein to function accurately, it would need a constant flow of the most up-to-date information about the virus, the antibodies and the actual number of people who’ve already had it.
Given the damage this virus has done to the people who’ve suffered and died with it and the people who are watching their lives and livelihoods die around them, it’s about time we had accurate information and higher-quality models. Perhaps this tool is the answer there.
Keep the faith and keep after it!
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