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Implementing a Trigger-Based Approach to Facilitate Local Management of Social Distancing Measures

Last week, I wrote about one of the most dramatic developments in the fight against COVID-19, the fact that Harvard University epidemiologists determined that continuing extreme social distancing measures into the summer months could actually result in more COVID-19 deaths than a ‘do nothing from the beginning’ alternative.  That finding is stunning in its implications.  Even more stunning is the fact that no one in the mainstream media has picked up on it and reported it.  I guess it simply does not match their narrative and thus needs to be ignored.
The main takeaway of that particular result of the Harvard model is strikingly simple (to me, anyway):  Unless a given locality is facing an acute shortage of healthcare-related resources, they should STOP ALL GOVERNMENT-MANDATED SOCIAL-DISTANCING REQUIREMENTS IMMEDIATELY.  Then they should implement a trigger-based plan to appropriately adjust social-distancing measures over time, as warranted by demands on local healthcare resources.  To that end, this post aims to impart some basic understanding regarding what to expect and how to get started with such a trigger-based plan.
The above finding (about prolonged extreme social distancing being worse than ‘doing nothing’) only has relevance if our response to the virus is binary, i.e. we choose to rely exclusively upon either extreme social distancing or no social distancing.  The truth of the matter is that we do not face (or at least should not face) such binary decision-making.
During my post last week, I briefly mentioned the Harvard model’s more nuanced trigger-based approach to controlling the spread of the SARS-CoV2 virus, but I intentionally declined to comment on it at the time, because I wanted the magnitude of that first finding to sink in.  I believe the above finding is critical to breaking psychological inertia, wherein policymakers are prone to overreacting, and members of the populace can be frightened into embracing the notion that more extreme responses inherently yield better outcomes.  It is important to realize and understand that extreme efforts at ‘doing good’ can end up doing ‘more harm than good’ (as my previous post demonstrated).
With that being said, I now turn to the nuances associated with the Harvard model’s on/off approach to social distancing.  First, it bears noting that the Imperial College model (the one that predicted 2.2. million American deaths if we ‘do nothing’) followed a similar on/off approach.  Here are the major takeaways associated with the on/off trigger-based analyses of both models:
  • Harvard and Imperial College both incorporated on/off trigger-based threshold values designed to keep critical cases below the local critical care capacity.
  • Harvard directly tied their threshold values to critical care capacity (i.e. ICU beds), which ended up being 39.33 times the # of available ICU beds for the ‘on’ threshold and 1/7 that value for the ‘off’ threshold.
  • Imperial College assumed a specific set of non-pharmaceutical interventions (NPIs), that included case isolation (CI), household quarantine (HQ), social distancing (SD), and school closures (PC).  Note that the Imperial College model did NOT include or recommend ANY business closures (other than intermittent school closures).
  • Harvard assumed a specific reduction in R0 (60%) rather than a distinct set of NPIs.  As such, Harvard’s model left room for local policymakers to choose which NPIs to employ, rather than prescribing the NPIs.  However, no guidance was given and (to my knowledge) no evidence currently exists linking any individual NPIs or combination of NPIs to a specific reduction in R0.
Figure 6D from the Harvard report (below) shows the cyclical rise and fall of prevalence for infections (black curves, left axis) and critical infections (red curves, right axis), using the following assumptions:
  • seasonal effects:
    • wintertime R0 = 2.2,
    • summertime R0 = 1.3 (40% natural decline),
  • ‘strong’ intermittent social distancing (60% reduction in R0, only in place during the blue-shaded time periods),
  • a doubling of U.S. critical-care capacity (to 1.8 available ICU beds per 10,000 adults),
  • an ‘on’ threshold of 70 confirmed, active SARS-CoV2 cases per 10,000 adults (which equates to 39.33 times the # of local ICU beds), and
  • an ‘off’ threshold of 10 confirmed, active SARS-CoV2 cases per 10,000 adults (which equates to 5.62 times the # of local ICU beds).
Figure 6D

As can be seen in Figure 6D above, the Harvard model predicts a lengthy years-long battle against the SARS-CoV2 virus.  The model also predicts a 3-month time period, between the end of May and mid-August 2020, when the virus will remain well under control even with zero social distancing.  This ‘reprieve’ is due to the anticipated natural reduction in R0 during the summer months.
It is unfortunate that the Harvard researchers did not publish a trigger-based analysis analyzing ‘low’ and ‘moderate’ intermittent social distancing (i.e. a 20% and 40% reduction in R0, respectively) because their one-time-only social-distancing analyses showed that “moderately effective (20%-40%) social distancing yields the smallest overall peak and total outbreak size (italics added, p. 14).  The distinct advantages of ‘low’ levels of social distancing (20% reduction in R0) can be seen visually via the red peaks in Figures 5B and 5C and the red cumulative infection curves in Figures 5G and 5H (where black = 0%, red = 20%, blue = 40%, green = 60%).
Figures 5B & 5G



Figures 5C & 5H

So, where do we go from here?  I suggest the following (at least as a starting point for discussions amongst local civic and community leaders):
  • Establish on/off thresholds for each city or county (or grouping of counties, whenever smaller counties rely upon neighboring counties for enhanced healthcare capacity), using the following formulae:
    • Implement ‘strong’ social distancing measures whenever the # of active + presumptive SARS-CoV2 infections exceeds 39.33 times the # of available ICU beds.
    • Cease ‘strong’ social distancing measures whenever the # of active + presumptive SARS-CoV2 infections drops below 5.62 times the # of available ICU beds.
      • e.g. for Payne County, Oklahoma: 5.62 * 11 ICU beds = 62 active, confirmed + presumptive SARS-CoV2 infections.
    • NOTE: Once quick-turn-around (e.g. 48 hours or less) diagnostic testing for SARS-CoV2 is locally available, presumptive cases could be excluded from the above threshold calculations.  (Presumptive cases would be those for whom a physician has diagnosed the patient as potentially having SARS-CoV2 and has ordered diagnostic testing, but the test results have not yet been confirmed).
  • Eliminate all government-mandated social-distancing restrictions.
  • Establish methods for communicating local trigger thresholds and current conditions to residents and local businesses.
  • In cooperation with local business and civic leaders, establish a list of appropriate social-distancing measures to be implemented once current conditions exceed the local ‘on’ threshold.
    • NOTE:  This could be implemented as a staged approach where certain ‘easier’ measures are implemented well before the official ‘on’ threshold, and with other additional measures gradually phasing in as local conditions approach the ‘on’ threshold.
  • Establish procedures to enable community members (e.g. churches and civic groups) to actively facilitate protection of elderly and at-risk residents (e.g. mobilize volunteers to provide zero-contact grocery deliveries).

Steve Trost is Associate Director of the Institute for the Study of Free Enterprise and can be contacted at trost@okstate.edu.  He has a bachelor
s degree in engineering from MIT, a masters degree and PhD in engineering from Oklahoma State University and a PhD in entrepreneurship (also from OSU).

Follow Dr. Trost on twitter: @TrostParadox



Disclaimer: All comments, observations, and statements presented herein represent the opinions of the author and in no way reflect the views of Oklahoma State University or the Institute for the Study of Free Enterprise.


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