Many of you might have seen this graph (or versions of it) from the New York Times:

Graphic courtesy of the New York Times.>

Most people interpret this graph as the solution to the coronavirus pandemic. We assume that if we work to flatten the curve, the disease will go away. #FlattenTheCurve has even become a popular hashtag on twitter. However, here at Speer, we look at this graph and see a lot more.

Flattening the curve means to use protective measures like social distancing, self isolation, wearing masks etc. to slow down the spread of the virus so our healthcare systems don’t get overwhelmed. A big reason why flattening the curve is so appealing is because these are all things that while inconvenient, anyone can adopt and apply to their own lives.

The underlying logistical reason is that the biggest problem with COVID-19 during its initial phases has been the lack of basic equipment and tools necessary to combat it. Even essentials like sanitization products were in short supply, which has led companies and factories to repurpose their supply chains to manufacture hand sanitizer. Protective gear like facial masks were in short supply as well, which led to people reusing medical masks and other PPE (personal protective equipment) which is a terrible idea.

While social distancing and flattening the curve are actively helping to reduce these issues, it isn’t a permanent solution. looking at the graph above, the area under both curves (the integral) is the same. This represents the total number of cases across the entire lifetime of the pandemic. This is a number we want to go down to zero. The problem with the flatten the curve method is it’s essentially just stalling until a vaccine is hopefully developed. This is arguably the best short term solution but could do with some optimization for the long term. Surely there are other elements in the graph above that we could optimize and make better?

What about raising the bar? Or in other words, what about further increasing the capacity of the healthcare system? For instance, in the United States, there are currently only about 2.8 hospital beds per 1000 people. This is a bit shocking, given that hospital beds aren’t just used during pandemics. While a large number of improvised or makeshift beds can be made, improving the capacity of the healthcare system might be a good course of action- especially considering that the system needs to expand to deal with the ever-increasing population.

There are certainly impediments to raising the bar. For one, the costs associated with increasing capacity might be large. But to put this in perspective, are these costs (financially and otherwise, in terms of the psychological toll of lockdown, the cost of markets shutting down, etc.) greater than what we’re paying for our makeshift solutions? Perhaps we should view these costs then, as a kind of insurance that we’re paying, to deter and be better prepared for future disasters of this magnitude.

This is one of the many opportunities presented to us by the pandemic; the opportunity to stop, and reflect on the problems with our infrastructure, and make plans to handle situations and problems like these better in the future. Afterall, the second wave of the Spanish Flu was far more deadly than the first.

At the end of the day, the horrors of what we’re facing are undeniable. This disease, in just a few months, has challenged us on every level. It’s challenging us as individuals, in terms of our discipline to abide by the quarantine regulations and practice social distancing, and our resilience as we try to support our families both emotionally and financially. It’s also challenging us as a society; our ability to coordinate large-scale efforts and mitigate the spread of this disease across geopolitical borders. It’s a messy process, and we’ve unfortunately already seen a lot of casualties.

After all - we’re not just fighting against COVID-19. We’re fighting for our future.

About the author

Ishan Mishra

Full Stack Developer

Before joining Speer, Ishan worked as a project manager for a Bay Area startup and Venture Capital firm. Prior to that, worked at Harvard Medical School as a Computational Biologist, at Teledyne DALSA as an Advanced Developer for CMOS Sensor products and as a nano-photonics Research Assistant at Harvard University.

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