The Day after COVID-19 Lockdown: Need to Focus on the Vulnerable

Given the latest statistics, we predict that with a proper differential social distancing placing different guidelines on the low-risk group compared to the high-risk group one could maintain a working economy without quarantine and save a factor of 5 on mortality while not flooding the health system.

Amnon Shashua, Shai Shalev-Shwartz

Both authors are affiliated with the Hebrew University of Jerusalem.

In previous papers, we introduced a mixed-population SEIR model [7] that is aimed at predicting the expectation on the number of critical ICU beds needed to contain the peak of the outburst while in [6] we provided tight concentration bounds on the number of critical ICU beds. The underlying insight was that the population should be divided differentially into a low and high-risk group where the low-risk (people under the age of 65 without co-morbidities) would be subject to social distancing of a relatively ”generous” basic reproduction rate (we assumed R0=1.4) while the high-risk group is under a more strict distancing with an R0=0.7 amongst themselves while observing a tight barrier between the two groups with an allowed ”leakage” of around 2% of the low-risk group infecting individuals from the high-risk group. When the model was fitted to the Israeli data the result was, under worst-case assumptions, an expectation of around 20 critical ICU beds per 100,000 inhabitants.

In this paper, we integrate all we learned to date from our model and from recent data to articulate a strategy of how to live with COVID-19 for the foreseeable future. By and large, there are two rival concepts (or strategies) of a post-lockdown phase. Both concepts acknowledge that the root of the problem is with the high-risk (vulnerable) group — both in terms of flooding the health system and mortality, but they handle the problem very differently. Both concepts are problematic in their own way and the challenge is to choose the less disruptive between the two:

  1. In order to protect the vulnerable, ”flatten the curve” so that very few are infected over the entire population and thereby very few will have a chance to infect the vulnerable amongst us. The tools available (besides a full-blown lockdown) are testing, contact-tracing, and isolation of those infected and those who were in close contact with them. This approach requires a very efficient cycle of large scale testing and accurate contact tracing technologies applied at scale. The weaknesses of this approach are twofold. First, is the high percentage of asymptomatic cases — as much as 50% or even higher [4, 5] — which inevitably has a detrimental epidemiological effect on the ability to flatten the curve. Second, is the (unknown) degree of cooperation from the public — especially from the low-risk group who might prefer to ”lay low” rather than risking tight oversight and perhaps lack of control of what the state will do with them during their sickness period.
  2. Focus on the vulnerable. The idea is to identify the vulnerable (age cut-off and co-morbidities) and have them observe strict social distancing (for their own protection) while receiving assistance from the state. The weakness of this approach is whether it is at all practical to observe a differential social-distancing regime and whether the toll of strict social distancing is too much to bear over an extended period of time.

In the remainder of this paper, we wish to drill-down further on the 2nd concept of a differentiable social distancing. Summarizing what we know so far from the Israeli and worldwide data would be useful to articulate the magnitude of the difference between the the low and high risk groups:

  • In Israel, out of 200 deaths, only 1 is from the low-risk group. Let's assume that the probability to die if one is infected, is between 100 to 200 times larger for members of the high-risk group compared to the low-risk group.
  • In Israel, as of April 7, 2020, there were 115 on respiratory systems out of which 19 were from the low-risk group. On average, the probability to require a critical ICU bed (if one is infected) is 6 times larger for members of the high-risk group.
  • From available data in Stockholm and Geneva, the probability to die, if infected, is around 0.5%. As of April 22, 2020, there have been approximately 1,000 deaths in Stockholm, and authorities believe that 20% of the population have been infected [1]. Given that the population size of Stockholm is around 1 million, we arrive at the probability of 0.5%. Approximately the same day in Geneva there has been 191 deaths and a Serological test conducted by the Geneva University Hospital [3] concluded there were 27,000 people infected indicating a mortality of 0.7%.
  • Mortality is affected by the median age of the population (30 in Israel, 41 in Sweden, 43 in Switzerland), distribution of chronic pre-existing conditions and various anecdotal factors like BCG vaccination during childhood [2].

Lets assume that the size of the low-risk population in Israel is 7 million (out of 9 million) and let p be the probability to die for members of the low-risk group. Given the above, let 100 <= z <= 200 where z · p be the probability to die for members of the high-risk group, then

from which we deduce that 0.01% <= p <= 0.02% and z · p = 2.2%. In other words, the mortality of 0.5% over the entire population is divided extremely unevenly between the low and high-risk groups. It is as if we are faced with two diseases, one mild affecting one group of the population and the other is ferocious affecting a different group of the population.

All of this underscores the most fundamental principle of managing the next phase of living with COVID-19 which is ”protect the Vulnerable population” by all means possible because there lie the high mortality and the risk of flooding the health system. Below is a list of steps one can take in protecting the vulnerable while maintaining reasonable (but strict) social distancing:

  1. Identify the high-risk group. This is based on an age cut-off and list of chronic pre-existing conditions. For example, In Israel on April 7, 2020, there were 148 COVID-19 patients treated in ICU (out of which 115 on respiratory systems). The leading pre-existing conditions at the time were Hypertension (30%), Diabetes (30%), Heart conditions (13%), and Obesity (BMI above 35, 7%). Age cut-off and pre-existing conditions are most likely country (or state) specific. The criteria should be set in a way that is concise and clear to the public. Most likely multiple chronic pre-existing conditions should be part of the cut-off rather than a single pre-existing condition.
  2. Protect retirement homes. In Sweden, for example, 50% of mortality was from residents of retirement homes. The number of staff members that closely interact on a daily basis with each resident should be minimized. In Sweden, it used to be as much as 5 different people before authorities began aggregating duties to reduce the exposure. Staff members should be tested on a daily basis (not to wait for the appearance of symptoms). Visitations and interactions should follow strict physical distancing, masks of facial shield visors, and surface de-contamination post-visit.
  3. The state should issue a special ID card for the population at risk. People holding the card would be entitled to special consideration in public places such as the right to not stand in line (payment and standing in line will be handled by shop staff members), strict physical distancing and de-contamination of surfaces when interacting with staff members (shops etc.), single person workspaces at work, etc.
  4. People of the high-risk group would be entitled to work from home or, if that is not possible, to receive a paid leave of absence from the state until the crisis is over.
  5. Careful monitoring of the number of high-risk individuals on respiratory systems in order to calibrate the enforcement or tightening of guidelines of physical distancing. In [7] we propose separate graphs of respiratory system use over time for the two populations based on physical distancing assumptions.

As mentioned in [7], the low-risk group would be subject to social distancing but the kind that does not disrupt economic activities — we were assuming a basic reproduction rate R0=1.4 (which means that on average an infected person infects 1.4 individuals). The number of critical ICU beds necessary for containing the peak of the outburst is manageable according to [7] and the mortality at the end of the cycle (when 50% of the low-risk group have been infected) would be between 0.005% to 0.01% of the low-risk population. As for mortality of the high-risk group, under the assumption we made in [7] where 2% of the low-risk population infect individuals of the high-risk and they go on and infect 0.7 other people from the high-risk group (presumably from the same household), then a rule of thumb (assuming that households for the high-risk group are a cluster of infection) would be the following: let X stand for the size of the low-risk population, then the number of infected people from the high-risk group would approximate 0.017X with a mortality figure of 0:0004X. The overall mortality, under the differential social distancing scheme, would be around 0.05% of the size of the low-risk group. This figure should be compared to the 0.5% mortality (of those infected) from current statistics — which amounts to 0.25% assuming 50% infection. Therefore, with a proper differentiable social distancing one can save a factor of 5 on mortality while not flooding the health system. Important to note, that the majority of mortality is coming from 2% of the low-risk population infecting the high-risk. This is, of course, avoidable if the high-risk population is placed under quarantine — but given the potentially long period required to sustain COVID-19 until a vaccine is ready, that could be not practical.

Naturally, every death is a tragedy and we have no intention of downplaying the loss of life. It is a matter of every society to make its own balance between expected loss of life due to COVID-19 and those due to other factors (that could result in alternative solutions for COVID-19). Predicting mortality is not a rewarding exercise but in order to chart the path forward and weigh different strategies the calculations around the capacity of the health system and overall mortality must be transparent. So far, we have not seen this level of transparency with alternative solutions.

References

[1] H. to Ellyatt. Sweden resisted a lockdown, and its capital Stockholm is expected to reach herd immunity in weeks. CNBC, 2020.

[2] P. C. et al. Supplements to COVID-19 confirmed cases prediction this version. April 15, 2020.

[3] G. U. Hospital. Covid-19 seroprevalence: First estimate of the prevalence of anti-sars-cov-2 igg antibodies in the geneva population, 2020.

[4] T. John. Iceland lab’s testing suggests 50% of coronavirus cases have no symptoms. CNN, 2020.

[5] D. Karedes. CDC reviewing stunning universal testing results from Boston homeless shelter. April 15, 2020.

[6] S. Shalev-Shwartz and A. Shashua. Can we contain COVID-19 without locking-down the economy? CBMM Memo 104, Massachusetts Institute of Technology, 2020. can also be found on Medium

[7] S. Shalev-Shwartz and A. Shashua. An exit strategy from the COVID-19 lockdown based on risk-sensitive resource allocation. CBMM Memo 106, Massachusetts Institute of Technology, 2020. Can also be found on Medium

CEO of Mobileye, SVP at Intel, Co-CEO of OrCam, Chairman of AI21labs & Sachs Prof. of Computer Science at the Hebrew University of Jerusalem

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