Disclaimer: I am not an expert in epidemiology and I am not a medical doctor. My background is in computer science, engineering, and machine learning.

This article explains my idea about an alternative policy to handle the current COVID-19 pandemic. I hope to spark discussion about the long term solutions for this crisis.

At the time of writing this article (March 18th) we can state that the social distancing policies introduced to contain the virus (non-pharmaceutical interventions) have a very high economical cost. China’s industrial production fell by 13.5%, and their service production fell by 13% due to the extraordinary containment efforts [source].

The Dow Jones index going down 30% due to the coronavirus.

While these very strict lock down measures reduced the number of new infections to almost zero, it is not clear what should happen next.

If the lock downs are lifted, the epidemic could start again since the population does not have immunity. An article from Imperial College discusses multiple intervention scenarios and concludes that without herd immunity a new wave of pandemic could come.

One potential strategy is to flatten the curve. This strategy is popularized in the media, but unfortunately the curve has to be flattened too drastically, requiring enormous economic sacrifices (see the following article). Although most people seem to accept the social distancing measures at this time, this could change once we have unemployment rates of 20% in developed countries. Many people expect that the current restrictions will last only a few weeks, but if we truly attempt to keep the number of cases below the capacity of the healthcare system, these measures could last up to a year or longer.

While everybody hopes for a vaccine, relying on it, is a risky strategy. Even the most optimistic predictions expect a vaccine in 12–18 months. If the current social distancing measures are maintained, the economies could be severely devastated by that time. At some point, the effects on human lives from the collapsing economy could become comparable to that of the coronavirus itself.

There are positive signs that the spread of the virus could slow down with the warmer and more humid weather (in the northern hemisphere) [source], but the virus may come back during the fall.

The huge challenge to contain the COVID-19 virus comes from the high number of asymptomatic cases. In this tweet, Italian doctors report asymptomatic cases in 90% of all the cases. Other sources report substantial asymptomatic transmissions as well (e.g. [source] or [source]). These asymptomatic cases cannot be diagnosed early on and thus, the only effective policy for containment is social distancing for all.

Though having high numbers of asymptomatic cases is very bad for containment efforts, it also means that many people only have mild symptoms. In general, COVID-19 produces mild to medium symptoms for a large fraction of the population.

We could take advantage of this.

If we assume that the people infected with (and subsequently recovered from) become immune to COVID-19, we can fight the pandemic by “immunizing” more people through selected exposure. For a large portion of the population, getting infected would only cause symptoms comparable to that of a cold or a flu. Roughly speaking, for certain people, the virus itself could serve as a “vaccine”.

The difficulty is that we do not know who is “naturally immune” and whose life is threatened. Despite not knowing this exactly, we can get decent predictions (decent in a statistical way, which means better than a random guess) just by looking at the age of the person. Up to 40 years, the fatality rate is at 0.2%, while above 80 years, it rises to 15% [source]. Instead of just using the age as input to make our predictions, we could do much more (e.g. take the individuals medical history, DNA, blood test, infect the blood sample with the virus and observe the reaction, etc).

In the last decade, machine learning has advanced incredibly. We have methods (e.g. deep neural networks) and the computing power (GPU clusters) to make these predictions automatically, once the samples are available. We also have a large community of smart medicine and machine learning researchers who could work on this problem over the next months.

What would be needed is a large amount of data from patients who already contracted the virus. While the samples would not come for free (e.g. DNA tests need to be done), nor can we expect the hospitals, which are already overwhelmed, to collect these samples, there are still accessible medical capacities around the world which could achieve this task.

With a test to predict if an individual would experience serious symptoms from COVID-19, we could target the exposure/isolation to individuals. Once this test is ready, people could make the decision based on their personal prediction, if they wish to take the risk contracting the disease (to achieve immunity). Once a person is immune, she/he no longer needs to follow social distancing measures.

In cases of prediction error, medical care would be available to the patient to handle the more severe symptoms. This test would also give us a good indication who needs isolation. The spread of the virus would become slower once a larger portion of the population is immune (herd immunity).

My hope is that this could be done faster than getting a reliable vaccine, resulting in faster recovery of both the economy and society.

(I thank Joyce for providing feedback on my writing)