10. Weekly Update: The World is Recovering

(4/15/20) Nearly all hot-spot countries and U.S. states are near or past the peak for death rate. That means the number of active cases (prevalence) is on the decline and some amount of social easing can be reasonably considered. However, until our population is largely immunized by having had the disease or by vaccination, social interactions cannot return to normal.

Remember to check my post called “Daily Rumblings” for late breaking updates.

Our model has been described in previous posts so we can spare that detail for now! Let’s put it to further practice and use it to help inform us on when we can start to relax social distancing and to what extent. We begin by showing the latest death rate plots for hot-spot countries and U.S. states below. Now that these curves have developed toward and past their peaks we superimpose a Gaussian function to visualize the progress made by these populations.  Note that we have de-rated the severity of several of these populations as represented by the colored circles. Major recovery is evident, and as predicted, about 3 weeks after serious social distancing was implemented.

The qualitative red, yellow, and green rankings reflect accelerated, rolling over to a peak, and well on the decrease death rates, respectively.

We make the following comments:

  • The Gaussian dependence in most cases represents well the reported death rate data (even while fixing the width at half max to 4 weeks). This dependence, and the symmetry assumed by the Gaussian, is expected to get distorted due to interventions to reduce the rate, e.g., social distancing. The breakdown in the fit is evident for those populations who were most aggressive, such as China and S. Korea, the latter never reaching exponential growth and the death rate instead being more constant.
  • Once a country reaches the peak in death rate, it is about half way to its final death count.
  • Italy and Spain are making remarkable progress and we (perhaps prematurely) have upgraded their situation to green (out of trouble). How they manage their retreat from social isolation will determine how successful they will be in the long term. This is the topic for below.
  • The U.S. is lagging these other countries in reaching a peak; however, it does appear that the peak is imminent.
  • Regarding the U.S. states: Washington is past its death rate peak and has been upgraded to a green. California never saw high death counts, just high death rate that appears to be peaking so we upgrade that to yellow (warning).
  • We have not followed MI, NJ, and LA from the beginning so we have limited data to fit to the Gaussian, but each of these states is making progress and if the current trends continue over the next week, they will all be upgraded as well.

We now present our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people). We also add a new column for the date we consider to be the earliest each population base can begin relaxing social distancing. We will tentatively call this an easing date and not a safe date so as not to conjure up excessive hopefulness.

Assumptions: Mortality factor is estimated as 1.0% for most favorable populations (not yet strained health care system) and up to 2.0% for least favorable populations (strained healthcare system).

A rough rule of thumb is that the easing date cannot be before the point when the prevalence count drops to less than what it was when the death rate took off. If that was about 4-6 weeks before the death rate peak, then one might think it should be about 4-6 weeks after the peak since the rise and the fall is approximately symmetric. However, as I showed in the previous Post #9, the incidence and prevalence curves precede the death rate curve by about 2.5 and 1.25 weeks, respectively, and that results in the prevalence count coming down to its say minus 5-week mark at about 4 weeks after the death rate peak (this time accounts for decreasing incidence and recovery from the disease). Now this date would not be safe because it corresponds to a prevalence that previously set off the exponential growth in death. However, if we exercise some precautions then sometime soon after 4 weeks may be considered safe.

For our purposes, we assume that as an absolute minimum condition to consider some social relaxation, that the prevalence must drop below 100 active cases per million (i.e., 1/10,000 people). One would still have to ensure that close contact with strangers is minimized and voluminous testing must be continued among other moderation. Frankly, we can never return to normal until some high percentage of a population is immunized either by having had the disease or by vaccine. A ballpark figure is about 50%, but no population yet has had more than 10% infected (Projected by 6/1/20: Italy ~ 4%, Spain ~ 5%, U.S. ~ 2%, NY ~ 10%).

Main comments are:

  • Without making a pretense about safety, our forecasts for when it is reasonable to consider social relaxation are given in the right most column in the above Table.
  • China and S. Korea are already relaxing social isolation so the rest of the world has many weeks to observe the prudence of their approach and decide on some combination of emulating and modifying.
  • Iran would appear to be the next country to drop below the 100 per million prevalence threshold (4/29/20). Italy and Spain follow next in the beginning of May and the U.S. and U.K. not until mid May.
  • Regarding the U.S. states it is not prudent to consider social relaxation for any of the hot-spot states before mid May except for CA and WA for which early May may suffice. NY will not reach an easing date until about 5/19/20.

Finally, we provide an update on the comparison of our forecast of critical values to that of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW), which has emerged as perhaps the leading model for informing our nation on the state of COVID-19 (http://www.healthdata.org/covid/).

We appear to be tracking very closely indicating that there must be components of each model that are similar. We tend to forecast a little earlier from peak rates. As the death rate curve flattens the accuracy of forecasts improve greatly because it is more evident where in the rise and fall cycle a given population is. This also accounts for the better agreement between the two models relative to previous weeks.