The Worst-Case COVID-19 Predictions Turned Out To Be Wrong. So Did the Best-Case Predictions.
An argument for humility in the face of pandemic forecasting unknown unknowns
"Are we battling an unprecedented pandemic or panicking at a computer generated mirage?" I asked at the beginning of the COVID-19 pandemic on March 18, 2020. Back then the Imperial College London epidemiological model's baseline scenario projected that with no changes in individual behaviors and no public health interventions, more than 80 percent of Americans would eventually be infected with novel coronavirus and about 2.2 million would die of the disease. This implies that 0.8 percent of those infected would die of the disease. This is about 8-times worse than the mortality rate from seasonal flu outbreaks.
Spooked by these dire projections, President Donald Trump issued on March 16 his Coronavirus Guidelines for America that urged Americans to "listen to and follow the directions of STATE AND LOCAL AUTHORITIES." Among other things, Trump's guidelines pressed people to "work or engage in schooling FROM HOME whenever possible" and "AVOID SOCIAL GATHERINGS in groups of more than 10 people." The guidelines exhorted Americans to "AVOID DISCRETIONARY TRAVEL, shopping trips and social visits," and that "in states with evidence of community transmission, bars, restaurants, food courts, gyms, and other indoor and outdoor venues where people congregate should be closed."
Let's take a moment to recognize just how blindly through the early stages of the pandemic we—definitely including our public health officials—were all flying at the time. The guidelines advised people to frequently wash their hands, disinfect surfaces, and avoid touching their faces. Basically, these were the sort of precautions typically recommended for influenza outbreaks. On July 9, 2020, an open letter from 239 researchers begged the World Health Organization and other public health authorities to recognize that COVID-19 was chiefly spread by airborne transmission rather than via droplets deposited on surfaces. The U.S. Centers for Disease Control and Prevention (CDC) didn't update its guidance on COVID-19 airborne transmission until May 2021. And it turns out that touching surfaces is not a major mode of transmission for COVID-19.
The president's guidelines also advised, "IF YOU FEEL SICK, stay home. Do not go to work." This sensible advice, however, missed the fact that a huge proportion of COVID-19 viral transmission occurred from people without symptoms. That is, people who feel fine can still be infected and, unsuspectingly, pass along their virus to others. For example, one January 2021 study estimated that "59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms."
The Imperial College London's alarming projections did not go uncontested. A group of researchers led by Stanford University medical professor Jay Bhattacharya believed that COVID-19 infections were much more widespread than the reported cases indicated. If the Imperial College London's hypothesis were true, Bhattacharya and his fellow researchers argued, that would mean that the mortality rate and projected deaths from the coronavirus would be much lower, making the pandemic much less menacing.
The researchers' strategy was to blood test people in Santa Clara and Los Angeles Counties in California to see how many had already developed antibodies in response to coronavirus infections. Using those data, they then extrapolated what proportion of county residents had already been exposed to and recovered from the virus.
Bhattacharya and his colleagues preliminarily estimated that between 48,000 and 81,000 people had already been infected in Santa Clara County by early April, which would mean that COVID-19 infections were "50-85-fold more than the number of confirmed cases." Based on these data the researchers calculated that toward the end of April "a hundred deaths out of 48,000-81,000 infections corresponds to an infection fatality rate of 0.12-0.2%." As I optimistically reported at the time, that would imply that COVID-19's lethality was not much different than for seasonal influenza.
Bhattacharya and his colleagues conducted a similar antibody survey in Los Angeles County. That study similarly asserted that COVID-19 infections were much more widespread than reported cases. The study estimated 2.8 to 5.6 percent of the residents of Los Angeles County had been infected by early April. That translates to approximately 221,000 to 442,000 adults in the county who have had the infection. "That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county by the time of the study in early April," noted the accompanying press release. "The number of COVID-related deaths in the county has now surpassed 600." These estimates would imply a relatively low infection fatality rate of between 0.14 and 0.27 percent.
Unfortunately, from the vantage of 14 months, those hopeful results have not been borne out. Santa Clara County public health officials report that there have been 119,712 diagnosed cases of COVID-19 so far. If infections were really being underreported by 50-fold, that would suggest that roughly 6 million Santa Clara residents would by now have been infected by the coronavirus. The population of the county is just under 2 million. Alternatively, extrapolating a 50-fold undercount would imply that when 40,000 diagnosed cases were reported on July 11, 2020, all 2 million people living in Santa Clara County had been infected by that date.
Los Angeles County reports 1,247,742 diagnosed COVID-19 cases cumulatively. Again, if infections were really being underreported 28-fold, that would imply that roughly 35 million Angelenos out of a population of just over 10 million would have been infected with the virus by now. Again turning the 28-fold estimate on its head, that would imply that all 10 million Angelenos would have been infected when 360,000 cases had been diagnosed on November 21, 2020.
COVID-19 cases are, of course, being undercounted. Data scientist Youyang Gu has been consistently more accurate than many of the other researchers parsing COVID-19 pandemic trends. Gu estimates that over the course of the pandemic, U.S. COVID-19 infections have roughly been 4-fold greater than diagnosed cases. Applying that factor to the number of reported COVID-19 cases would yield an estimate of 480,000 and 5,000,000 total infections in Santa Clara and Los Angeles respectively. If those are ballpark accurate, that would mean that the COVID-19 infection fatality rate in Santa Clara is 0.46 percent and is 0.49 percent in Los Angeles. Again, applying a 4-fold multiplier to take account of undercounted infections, those are both just about where the U.S. infection fatality rate of 0.45 percent is now.
The upshot is that, so far, we have ended up about half-way between the best case and worst case scenarios sketched out at the beginning of the pandemic.
