# David Capital Partners, LLC, Chicago – “Letter on Covid-19 – 2020

**David Capital Partners, LLC, Chicago – “Letter on Covid-19 – 2020”**

**Excerpts from this letter:**

*STATEMENT #3. Let’s start with COVID-19. How has the pandemic peaked without a vaccine or herd immunity? *

*Many believe the only two paths out of the pandemic are either (1) a vaccine or (2) “herd immunity.”*

*We see this as a false choice. In fact, we believe the most likely outcome is a third and different path: that C19 has reached its “disease break point” in the US/Europe such that population-level spread is now in inexorable decline.*

**So let’s discuss how outbreaks end.**

**First, a definition. ***What is “herd immunity”? A population reaches herd immunity when a sufficient percentage of its members have specific resistance (are “immune”) such that the disease dies out. Specific resistance can be acquired either by recovering from the disease or via a vaccine.*

*The percentage required for herd immunity varies with a disease’s contagiousness. To measure contagiousness, epidemiologists use the mathematical term R0 (pronounced “R-naught”). R0 is the theoretical number of people who contract an illness from each infected person, assuming no existing specific resistance in a population.*

**For influenza (the flu), which has an R0 between 1.5 and 1.8, the threshold for herd immunity might be 45-50%. ***For COVID-19, which we think has an R0 of 2.5-3.0, the herd immunity threshold may be 60-65%.*** For measles, a highly-infectious disease with an R0 in the teens, more than 95% must have specific resistance to reach herd immunity.**

*One fact about herd immunity, however, is too often glossed-over: the herd immunity calculation estimates the theoretical threshold at which it is mathematically inevitable the disease will go extinct.*

**That is a lofty goal. ***So lofty, in fact, that almost no disease ever achieves it. Why? Because in practice, spread of a given disease collapses far before a population ever reaches herd immunity.*

**A good example is the flu. Influenza mutates easily, so each year a new vaccine (a flu shot) is required which attempts to “guess” what the flu will look like that year. Sometimes it hits. Sometimes it misses.**

*But a curious thing happens when the flu shot misses. That year’s strain of the flu explodes, gets a lot of people sick – and then spread of the strain collapses when around 10-15% of the population is infected. The seasonal flu never comes within spitting distance of reaching the 45-50% level required for herd immunity.*

**And this doesn’t apply just to modern seasonal illnesses. ***The 20th-century’s greatest pandemic (the Spanish Flu of 1918) probably had an R0 just above 2.0, so the herd immunity threshold was likely 55-60%. But historians estimate just 20% of people had been infected when the Spanish Flu’s spread suddenly collapsed. Philadelphia saw peak deaths in mid-October 1918, but by mid-November the disease was effectively gone from the city. Spread of the Spanish Flu peaked and plunged in weeks, without ever reaching herd immunity.*

**So how do we explain this?**

**The answer: ***there is not one, but two levels of population “immunity” to consider.*

*First, herd immunity: the level of specific resistance in a population required for a disease to fully disappear.*

*Second, the disease break point: the level of specific resistance in a population at which spread of a disease collapses. The disease break point is generally one-third or less the threshold required for herd immunity.*

**The difference between the two can be understood with basic math. ***Herd immunity is a theoretical calculation. It relies on mathematical assumptions.*** For example, the herd immunity calculation assumes homogeneity of actors and outcomes: that infections, specific resistance, social graphs, and individual susceptibility are all equally distributed. ***If the R0 is 1.8, each infected person transmits the disease to exactly 1.8 people. If 1% of a population has specific resistance, then 1% of each person’s social graph has specific resistance too.*

*The problem is obvious. These assumptions simply do not reflect the real world.*** Actors and outcomes are neither homogenous nor equally distributed. In fact, the opposite is true.**

**Some people have highly-connected social graphs (once infected, they are likely to infect many more) while others have relatively few connections (and may not infect anyone else). Some sub-populations are highly vulnerable (e.g., nursing homes) while others are highly resistant (e.g., young people).**

*Actors and outcomes are not equally distributed. They have tremendous variance.**The disease break point model uses graph theory to better explain how outbreaks evolve in practice. The model assumes actors and outcomes are not equally distributed – and in fact assumes they are concentrated in certain individuals and sub-populations. **A node with a well-connected social graph is more likely to be infected early and to transmit the disease widely. Once recovered, however, the “immune” node becomes a dead-end for future disease spread.*** ****The system spikes and then collapses far quicker than a herd immunity model (a homogenous approximation model) would predict as these “super-spreaders” become “super-suppressors.”**

**For COVID-19, the implications are powerful. ***If C19’s R0 is 2.5-3.0 and its herd immunity threshold is 60-65%, then the disease break point would be only 15-20% specific resistance*** (a population’s precise disease break point likely varies somewhat due to differences in susceptibility and social graphs).**

*Our research indicates Europe and the US reached this disease break point in March and April, respectively. We believe spread of COVID-19 in these geographies has peaked and is now in irrevocable, sustained decline.*