Covid Catastrophe Part I: Early Models Missed the Mark
Covid Catastrophe is a seven part series examining the history of the COVID-19 pandemic, its impact on Maine and what data policymakers used to lock down society and centrally-plan our economy. Check back tomorrow for Part II.
In late December 2019, scientists in Wuhan, a city of 11 million people in central China, discovered a novel coronavirus, dubbed SARS-CoV-2, due to its similarity to the Southeast Asia Respiratory Virus (SARS). “SARS 1” captured the world’s attention in 2003, infecting over 8,000 people, killing 741, mostly in Asia. Only eight people in the U.S. were confirmed to have contracted SARS during that time. In February 2020, as more and more cases of SARS-CoV-2 were being discovered in China, scientists named the disease caused by this new coronavirus, COVID-19. As of late July, over 16.5 million people worldwide are recorded to have contracted COVID-19, resulting in over 655,000 deaths.
In February and March of 2020, the World Health Organization, using limited data from early coronavirus hotspots like Wuhan and northern Italy, issued a report estimating the overall infection fatality rate (IFR) at 3.4%. This statement had revised that number up from around 2%. Anthony Fauci, director of the US National Institute of Allergy and Infectious Diseases, stated this ratio would likely drop as more cases are discovered, due to “another whole cohort that is either asymptomatic or minimally symptomatic.”
In late May, the US Centers for Disease Control and Prevention (CDC) published a report with five varying scenarios of how the virus could spread and affect the nation’s hospital capacity. Even the worst-case scenario of this CDC report pegged the case hospitalization ratio, the rate at which people with positive COVID-19 cases wind up in the hospital, at 4.1% and the IFR at 1%. In the scenario instilled with the highest level of confidence, the CDC estimated the case hospitalization rate to be 3.4% and the IFR at 0.4%.
These predictions were much, much lower than the models published by University of Washington’s Institute for Health Metrics and Evaluation (IHME) and Imperial College of London in early 2020. In this scenario, the CDC assumed the R0, or “R-naught,” the rate at which each infection leads to other successive infections, at 2.5. They also assumed that asymptomatic carriers were just as likely to spread the virus as symptomatic cases, a factor understood today to be an overestimation.
In early June, a CDC official admitted that “truly asymptomatic” cases of spreading the virus are “very rare.” Of course, the term “asymptomatic” is complex, because we are learning that many cases, as much as 35% by the CDC’s estimate, experience little to no symptoms. In the past, those would have all been under the umbrella of “asymptomatic,” even though those patients may have experienced mild symptoms unworthy of a trip to the doctor, or even enough to spur a COVID-19 serology (antibody) test.
IHME estimated there would be over 80 daily new infections in Maine on June 1; in reality, there were just 24 on June 1. Daily confirmed infections never surpassed 6 per 100,000 people, or just under 80 in absolute terms. These models, like the now-discredited initial doomsday scenario propagated by the Imperial College of London, missed the mark by a factor of 20 or more.
Risk-averse politicians, wary of their public stature in the face of a new threat, quickly called for drastic measures like “Stay-at-Home” orders. Many governors declared states of emergency. This was all done in order to protect crucial healthcare resources and avoid strain on hospitals and medical staff.
In those days, public awareness of the new virus was focused on “flattening the curve,” referencing a bell curve of possible case numbers and the goal to stretch out that curve to the point where its apex did not breach the flat line of health care capacity.
If only this simplistic view could have predicted a novel disease outbreak on a global scale. What we saw in the four months following those initial reports was anything but simple.