[Update, March 26 2020, 5:25 PM Eastern Time]
We have received the following response from the lead researcher in this report, Neil Ferguson:
I think it would be helpful if I cleared up some confusion that has emerged in recent days. Some have interpreted my evidence to a UK parliamentary committee as indicating we have substantially revised our assessments of the potential mortality impact of COVID-19. This is not the case. Indeed, if anything, our latest estimates suggest that the virus is more transmissible than previously thought. Our lethality estimates remain unchanged. My evidence to Parliament referred to the numbers of deaths we assess might occur in the UK in the presence of the very intensive social distancing and other public health interventions now in place. Without those interventions, our assessment remains that the UK would see the scale of deaths reported in our study (namely, up to approximately 500 thousand).
[Update, March 26 2020, 4:25 PM Eastern Time] On March 26 2020, the Daily Wire’s Amanda Prestigiacomo published a piece headlined, “Epidemiologist Behind Highly-Cited Coronavirus Model Drastically Revises Model,” which claimed that the primary researcher on Imperial College London’s influential COVID-19 paper was “wrong” and that he had “offered a massive revision” to the globally referenced research.
That claim is patently false.
(On March 24 2020, Prestigiacomo described the report as a “doomsday novel” in a separate effort to downplay its data.)
In “Epidemiologist Behind Highly-Cited Coronavirus Model Drastically Revises Model,” Prestigiacomo cited reporting published by David Adam at NewScientist, as well as several tweets also predicated on Adam’s reporting:
Epidemiologist Neil Ferguson, who created the highly-cited Imperial College London coronavirus model, which has been cited by organizations like The New York Times and has been instrumental in governmental policy decision-making, offered a massive revision to his model on [March 25 2020] … However, after just one day of ordered lockdowns in the U.K., Ferguson is presenting drastically downgraded estimates, revealing that far more people likely have the virus than his team figured.
We clicked through to Adam’s March 25 2020 article, titled “UK has enough intensive care units for coronavirus, expert predicts.” Adam did not seem to report what Prestigiacomo claimed he did:
Neil Ferguson at Imperial College London gave evidence today to the UK’s parliamentary select committee on science and technology as part of an inquiry into the nation’s response to the coronavirus outbreak.
He said that expected increases in National Health Service capacity and ongoing restrictions to people’s movements make him “reasonably confident” the health service can cope when the predicted peak of the epidemic arrives in two or three weeks. UK deaths from the disease are now unlikely to exceed 20,000, he said, and could be much lower.
In fact, Adam’s reporting seemed to indicate factors cited in the influential Imperial College London report were revised upwards, specifically COVID-19’s rate of reproduction. Moreover, Adam reported that Ferguson — who purportedly “backtracked” on his team’s findings — in fact indicated there was “more evidence to support the more intensive social distancing measures”:
New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said.
We contacted Adam to ask whether Prestigiacomo’s piece accurately represented his March 25 2020 article, and he responded quickly. Adam described the Daily Wire’s claims as “dangerous nonsense,” explaining:
[It is] fair to say that the article you linked to and the tweets DO NOT accurately represent my article or indeed Prof Ferguson’s position. To be clear, the revised estimates are because the UK has changed its approach and because it has introduced such stringent social distancing measures. If we hadn’t, then the much higher estimates of deaths would still apply. Essentially, person-to-person transmission SHOULD have massively reduced since the measures were introduced. And because it takes 2-3 week for the worst cases to reach hospital after infection, he’s now saying that the anticipated peak in demand for hospital care will come in 2-3 weeks, and that because the UK is rapidly putting in extra capacity, there should be enough ICU beds.
Other experts on Twitter addressed the harmful inaccuracies in the rapidly spreading “Epidemiologist Behind Highly-Cited Coronavirus Model Drastically Revises Model,” calling it “quite literally fake news”:
Mike Shellenberger described the piece as “inaccurate,” sharing several responses to the item’s spread:
Shellenberger also linked to a March 26 2020 Imperial College London report which reinforced (and did not retract) original projections, and addressed the other citation used in the Daily Wire piece (tweets from Alex Berenson):
On March 17 2020, an Imperial College London paper about coronavirus and COVID-19 trajectory models was referenced widely in the news and on social media, causing a considerable amount of concern and drawing global attention.
Discourse about the Imperial College London COVID-19 modeling was omnipresent — in Facebook posts, in viral Twitter threads, and in articles by nearly every major outlet. References to it were suddenly everywhere, making it clear whatever was in the “startling” report was a big deal. However, the general public was left to piece together a picture of the report’s contents from myriad sources.
Here’s a brief synopsis of the report, its major details, and what they might mean for society as the COVID-19 pandemic continues.
What is the Imperial College London coronavirus report?
On March 17 2020, researchers from Imperial College London published a press release, “COVID-19: Imperial researchers model likely impact of public health measures,” which introduced the broadly referenced report:
Researchers from Imperial have analysed the likely impact of multiple public health measures on slowing and suppressing the spread of coronavirus.
The latest analysis comes from a team modelling the spread and impact COVID-19 and whose data are informing current UK government policy on the pandemic.
That press release was not the sum of Imperial College London researchers’ findings, but it summarized the research, which appeared with the title, “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand,” published here as a summary and here in full as a PDF. In the press release, Professor Neil Ferguson — a public health expert — explained the primary implications of Imperial College London’s findings:
The world is facing the most serious public health crisis in generations. Here we provide concrete estimates of the scale of the threat countries now face.
We use the latest estimates of severity to show that policy strategies which aim to mitigate the epidemic might halve deaths and reduce peak healthcare demand by two-thirds, but that this will not be enough to prevent health systems being overwhelmed. More intensive, and socially disruptive interventions will therefore be required to suppress transmission to low levels. It is likely such measures – most notably, large scale social distancing – will need to be in place for many months, perhaps until a vaccine becomes available.
Why was a report titled “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand” such a big deal in global coronavirus news?
In context, the “impact of non-pharmaceutical interventions (NPIs)” was due to the fact pharmaceutical approaches like vaccines or medications had not yet been developed to combat the novel strain of coronavirus, meaning that public health officials and hospitals around the world were limited to behavioral approaches (such as lockdowns and social distancing) to mitigate the coronavirus pandemic. The report laid bare the fact that the primary and most important factor in reducing the impact of the coronavirus pandemic and to circumvent millions of deaths globally was one based on the actions of individuals worldwide.
Without any sort of effective medicine or vaccine to use to stem the flow of sickness, the sole approaches remaining concerned governments intervening to limit activity among citizens — including school, work, retail environments, gatherings, and other everyday actions most people take for granted as necessarily safe and common.
In other words, the title described the reality of the COVID-19 pandemic — no pharmaceutical interventions existed at all, anywhere, to address the strain — and global public health officials have been forced to act based on that baseline fact.
Where might I have seen the Imperial College London coronavirus report discussed on social media?
A Facebook post shared late on March 17 2020 appeared to paraphrase purported elements of the research:
That post appeared to be a transcription of a very widely-retweeted Twitter thread originally shared by Dixie State University history professor Jeremy C. Young. Young’s tweets appeared to be based on his reading of the report, and he endeavored to summarize its contents.
Young requested corrections if he had “gotten it wrong,” and shared 21 tweets in total describing what he saw in the report:
Young’s tweets were copied into individual Facebook posts like the one linked above, and it was seen by thousands of people across social media platforms. The thread described three specific courses of action with respect to COVID-19 response across the world, and a section about one of the three scenarios — “if the United States did absolutely nothing” — was particularly striking.
Here’s a brief rundown of what I’m seeing in here.
The COVID-19 response team at Imperial College in London obtained what appears to be the first accurate dataset of infection and death rates from China, Korea, and Italy. They plugged those numbers into widely available epidemic modeling software and ran a simulation: what would happen if the United States did absolutely nothing — if we treated COVID-19 like the flu, went about business as usual, and let the virus take its course?
Here’s what would happen: 80% of Americans would get the disease. 0.9% of them would die. Between 4 and 8 percent of all Americans over the age of 70 would die. 2.2 million Americans would die from the virus itself.
It gets worse. Most people who are in danger of dying from COVID-19 need to be put on ventilators. 50% of those put on ventilators still die, but the other 50% live. But in an unmitigated epidemic, the need for ventilators would be 30 times the number of ventilators in the United States. Virtually no one who needed a ventilator would get one. 100% of patients who need ventilators would die if they didn’t get one. So the actual death toll from the virus would be closer to 4 million Americans — in a span of 3 months. 8-15% of all Americans over 70 would die.
How many people is 4 million Americans? It’s more Americans than have died all at once from anything, ever. It’s the population of Los Angeles. It’s four times the number of Americans who died in the Civil War…on both sides combined. It’s two-thirds as many people as died in the Holocaust.
Americans make up 4.4% of the world’s population. So if we simply extrapolate these numbers to the rest of the world — now we’re getting into really fuzzy estimates, so the margin of error is pretty great here — this gives us 90 million deaths globally from COVID-19. That’s 15 Holocausts. That’s 1.5 times as many people as died in World War II, over 12 years. This would take 3-6 months.
Young went on to summarize other modeled COVID-19 outcomes, one for a middle-of-the-road “mitigation” strategy, and one for a total “suppression” strategy. The latter of the three was the least alarming in terms of possible overall mortality for the United States in particular.
That said, Young’s description of the third and most draconian “suppression” approach involved a significant change to the patterns of life, country by affected country. He added that a very swift and comprehensive suppression effort undertaken by governments could prevent the first two scenarios under the researchers’ models from occurring, limiting the number of deaths and a breakdown of healthcare systems globally:
This [third] time it works! The death rate in the US peaks three weeks from [March 16 2020] at a few thousand deaths, then goes down. We hit, but don’t exceed (at least not by very much), the number of available ventilators. The nightmarish death tolls from the rest of the study disappear; COVID-19 goes down in the books as a bad flu instead of the Black Death.
But, he explained, that approach was not without its own limitations. According to Young, until such time a vaccine was developed, restrictions on common activities would stretch into July 2020, and keep returning after they were briefly lifted:
But here’s the catch: if we EVER relax these requirements before a vaccine is administered to the entire population, COVID-19 comes right back and kills millions of Americans in a few months, the same as before. The simulation does indicate that, after the first suppression period (lasting from now until July 2020), we could probably lift restrictions for a month, followed by two more months of suppression, in a repeating pattern without triggering an outbreak or overwhelming the ventilator supply. If we staggered these suppression breaks based on local conditions, we might be able to do a bit better. But we simply cannot ever allow the virus to spread throughout the entire population in the way other viruses do, because it is just too deadly. If lots of people we know end up getting COVID-19, it means millions of Americans are dying. It simply can’t be allowed to happen.
Young concluded by stating that a vaccine was, in a best case scenario, eighteen months in the future:
How quickly will a vaccine be here? Already, medical ethics have been pushed to the limit to deliver one. COVID-19 was first discovered a few months ago. [In early March 2020], three separate research teams announced they had developed vaccines. [On March 16 2020], one of [the vaccines] (with FDA approval) injected its vaccine into a live person, without waiting for animal testing. Now, though, they have to monitor the test subject for fourteen months to make sure the vaccine is safe. This is the part of the testing that can’t be rushed: the plan is to inoculate the entire human population, so if the vaccine itself turned out to be lethal for some reason, it could potentially kill all humans, which is a lot worse than 90 million deaths. Assuming the vaccine is safe and effective, it will still take several months to produce enough to inoculate the global population. For this reason, the Imperial College team estimated it will be about 18 months until the vaccine is available.
That claim about an eighteen-month-long wait for a coronavirus vaccine was not limited to Young’s Twitter thread or copies of it on Facebook. MIT Technology Review’s “We’re not going back to normal,” also published on March 17 2020, reiterated the sobering claims about life in quarantine and the lead time for a coronavirus vaccine:
What counts as “social distancing”? The researchers define it as “All households reduce contact outside household, school or workplace by 75%.” That doesn’t mean you get to go out with your friends once a week instead of four times. It means everyone does everything they can to minimize social contact, and overall, the number of contacts falls by 75%.
Under this [suppression] model, the researchers conclude, social distancing and school closures would need to be in force some two-thirds of the time—roughly two months on and one month off—until a vaccine is available, which will take at least 18 months (if it works at all). They note that the results are “qualitatively similar for the US.”
Referencing the recently-published research, the summary described how the models accounted for different capacities, and why they were unable to find a less disruptive approach. As MIT Technology Review noted, even if infrastructure was fortified to meet the needs of an unchecked pandemic (in the form of more beds and more ventilators), there would never be enough doctors or nurses to make use of them:
Eighteen months!? Surely there must be other solutions. Why not just build more ICUs and treat more people at once, for example?
Well, in the researchers’ model, that didn’t solve the problem. Without social distancing of the whole population, they found, even the best mitigation strategy—which means isolation or quarantine of the sick, the old, and those who have been exposed, plus school closures—would still lead to a surge of critically ill people eight times bigger than the US or UK system can cope with. (That’s the lowest, blue curve in the graph below; the flat red line is the current number of ICU beds.) Even if you set factories to churn out beds and ventilators and all the other facilities and supplies, you’d still need far more nurses and doctors to take care of everyone … and what if we decided to be brutal: set the threshold number of ICU admissions for triggering social distancing much higher, accepting that many more patients would die?
Turns out it makes little difference. Even in the least restrictive of the Imperial College scenarios, we’re shut in more than half the time. This isn’t a temporary disruption. It’s the start of a completely different way of life.
Another common observation on social media was that the contents of the report were responsible for what appeared to be a dramatic reversal of course by world leaders who had previously taken their time addressing COVID-19:
So enough with the summaries — what did the Imperial College London coronavirus modeling actually propose?
Young’s 21-tweet-long summary concerned three scenarios — “doing nothing” and letting COVID-19 spread, “mitigation” to slow but not stop the coronavirus pandemic, and “suppression” to prevent a total breakdown of health care services in the weeks and months after the report was released on March 16 2020.
In its abstract, the paper explained the second two strategies of mitigation and suppression were modeled in their research. Both were first defined; both were said to include “major challenges” with respect to the state of global healthcare and infection rates. Moreover, researchers noted in their summaries that, as described on social media threads, the middle approach of mitigation would cause “hundreds of thousands” of preventable COVID-19 deaths:
Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread – reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option.
Researchers further noted that the UK and United States were its primary contexts in modeling, and the third strategy of suppression was strongly recommended based on their COVID-19 trajectory models. In addition, researchers described a projected timeframe for a safe vaccine to be available — a minimum of eighteen months as of March 2020, or September 2021.
Researchers described intermittent social distancing measures for the duration of time between the report and the anticipated introduction of a COVID-19 vaccine. In that interval of eighteen months or longer, strict social distancing measures were recommended based on “trends in disease surveillance” whenever an uptick in the number of infections was observed:
We show that in the UK and US context, suppression will minimally require a combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members. This may need to be supplemented by school and university closures, though it should be recognised that such closures may have negative impacts on health systems due to increased absenteeism. The major challenge of suppression is that this type of intensive intervention package – or something equivalently effective at reducing transmission – will need to be maintained until a vaccine becomes available (potentially 18 months or more) – given that we predict that transmission will quickly rebound if interventions are relaxed. We show that intermittent social distancing – triggered by trends in disease surveillance – may allow interventions to be relaxed temporarily in relative short time windows, but measures will need to be reintroduced if or when case numbers rebound. Last, while experience in China and now South Korea show that suppression is possible in the short term, it remains to be seen whether it is possible long-term, and whether the social and economic costs of the interventions adopted thus far can be reduced.
What are the five strategies for suppression considered in the report? How did researchers define “social distancing”?
Researchers proposed five non-pharmaceutical approaches to the coronavirus pandemic in the context of public health measures. Their definition of “social distancing” is emphasized below:
- Case isolation in the home, or CI: “Symptomatic cases stay at home for 7 days, reducing nonhousehold contacts by 75% for this period. Household contacts remain unchanged. Assume 70% of household comply with the policy.”
- Voluntary home quarantine, or HQ: “Following identification of a symptomatic case in the household, all household members remain at home for 14 days. Household contact rates double during this quarantine period, contacts in the community reduce by 75%. Assume 50% of household comply with the policy.”
- Social distancing of those over 70 years of age, or SDO: “Reduce contacts by 50% in workplaces, increase household contacts by 25% and reduce other contacts by 75%. Assume 75% compliance with policy.”
- Social distancing of entire population, or SD: “All households reduce contact outside household, school or workplace by 75%. School contact rates unchanged, workplace contact rates reduced by 25%. Household contact rates assumed to increase by 25%.”
- Closure of schools and universities, or PC: “Closure of all schools, 25% of universities remain open. Household contact rates for student families increase by 50% during closure. Contacts in the community increase by 25% during closure.”
What would mitigation and suppression entail in the context of the report?
Many of the research parameters were explained more full in the report itself. After the quoted summary, researchers outlined aims for the suppression and mitigation strategies, respectively (emphasis ours):
(a) Suppression. Here the aim is to reduce the reproduction number (the average number of secondary cases each case generates), R, to below 1 and hence to reduce case numbers to low levels or (as for SARS or Ebola) eliminate human-to-human transmission. The main challenge of this approach is that NPIs (and drugs, if available) need to be maintained – at least intermittently – for as long as the virus is circulating in the human population, or until a vaccine becomes available. In the case of COVID-19, it will be at least a 12-18 months before a vaccine is available. Furthermore, there is no guarantee that initial vaccines will have high efficacy.
(b) Mitigation. Here the aim is to use NPIs (and vaccines or drugs, if available) not to interrupt transmission completely, but to reduce the health impact of an epidemic, akin to the strategy adopted by some US cities in 1918, and by the world more generally in the 1957, 1968 and 2009 influenza pandemics. In the 2009 pandemic, for instance, early supplies of vaccine were targeted at individuals with pre-existing medical conditions which put them at risk of more severe disease. In this scenario, population immunity builds up through the epidemic, leading to an eventual rapid decline in case numbers and transmission dropping to low levels.
In that excerpt, researchers described an underlying objective for the “mitigation” model and approach — to reduce the health impacts of an epidemic. Under that model, population immunity builds slowly, and transmission eventually declines.
The described objective under suppression model involved reducing the number of new cases generated by every extant case to under one. That involved an epidemiological measure known as R0 (or R-naught), which is the number of people each infected person goes on to infect, on average. Early estimates for the novel strain of coronavirus indicated each infected individual went on to infect two or three other people:
For disease outbreaks, epidemiologists have a term for describing the average number of people an infected individual will spread an illness to in a susceptible population: it is known as the basic reproduction number, or R0 (pronounced “R-naught”). If the R0 is less than one, the outbreak will fizzle out. If it is greater than one, the outbreak will continue. Early estimates place the R0 for the 2019 novel coronavirus (2019-nCov) in the range of two to three. For comparison, the R0 of SARS (a related coronavirus) was two to four when it caused a deadly outbreak in 2003, and the R0 of measles is 12 to 18.
Imperial College London researchers used an incubation period of 5.1 days in their model, using a similar R number to the one reported in Scientific American (“a baseline assumption that R0=2.4 but examine values between 2.0 and 2.6”), and assumed that “individuals are assumed to be immune to re-infection in the short term” following recovery from COVID-19. From there, they used existing figures regarding infection and mortality rates:
Infection was assumed to be seeded in each country at an exponentially growing rate (with a doubling time of 5 days) from early January 2020, with the rate of seeding being calibrated to give local epidemics which reproduced the observed cumulative number of deaths in GB or the US seen by 14th March 2020.
Next, researchers discussed a scenario wherein the novel coronavirus strain progressed without intervention, predicting peak mortality three months after the March 2020 report. They noted that such an outcome was “unlikely,” citing the scale of the United States versus the UK as a factor in infection trajectory, as well as a higher population of elderly people in the UK as a factor in mortality rates there.
Even without accounting for reduced health service capacity thanks to strain on each country’s systems, researchers calculated an expected rate of 2.2 million deaths in the U.S. and 510,000 deaths in the UK:
In the (unlikely) absence of any control measures or spontaneous changes in individual behaviour, we would expect a peak in mortality (daily deaths) to occur after approximately 3 months. In such scenarios, given an estimated R0 of 2.4, we predict 81% of the GB and US populations would be infected over the course of the epidemic. Epidemic timings are approximate given the limitations of surveillance data in both countries: The epidemic is predicted to be broader in the US than in GB and to peak slightly later. This is due to the larger geographic scale of the US, resulting in more distinct localised epidemics across states than seen across GB. The higher peak in mortality in GB is due to the smaller size of the country and its older population compared with the US. In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in GB and 2.2 million in the US, not accounting for the potential negative effects of health systems being overwhelmed on mortality.
As discussed in many of the summaries shared on social media, on page ten of the report researchers maintained that mitigation was not viable as a strategy, because of its catastrophic effects on health care systems in both countries. As such, they maintained that the suppression strategy was “likely necessary” where possible:
Given that mitigation is unlikely to be a viable option without overwhelming healthcare systems, suppression is likely necessary in countries able to implement the intensive controls required. Our projections show that to be able to reduce R to close to 1 or below, a combination of case isolation, social distancing of the entire population and either household quarantine or school and university closure are required. Measures are assumed to be in place for a 5-month duration. Not accounting for the potential adverse effect on ICU capacity due to absenteeism, school and university closure is predicted to be more effective in achieving suppression than household quarantine. All four interventions combined are predicted to have the largest effect on transmission. Such an intensive policy is predicted to result in a reduction in critical care requirements from a peak approximately 3 weeks after the interventions are introduced and a decline thereafter while the intervention policies remain in place. While there are many uncertainties in policy effectiveness, such a combined strategy is the most likely one to ensure that critical care bed requirements would remain within surge capacity.
Researchers routinely mentioned (as did the posts above) that implementation of the suppression strategy would never be adhered to at a perfect rate. They accounted for partial but high compliance with a relaxation of recommended restrictions after several months.
What would a suppression strategy mean for schools and workplaces? How long would suppression measures last?
That suppression model included the closure of schools and universities for months in order to delay a subsequent epidemic during the period before a vaccine became available:
Once interventions are relaxed (in the example in Figure 3, from September  onwards), infections begin to rise, resulting in a predicted peak epidemic later in the year. The more successful a strategy is at temporary suppression, the larger the later epidemic is predicted to be in the absence of vaccination, due to lesser build-up of herd immunity.
Given suppression policies may need to be maintained for many months, we examined the impact of an adaptive policy in which social distancing (plus school and university closure, if used) is only initiated after weekly confirmed case incidence in ICU patients (a group of patients highly likely to be tested) exceeds a certain “on” threshold, and is relaxed when ICU case incidence falls below a certain “off” threshold (Figure 4). Case-based policies of home isolation of symptomatic cases and household quarantine (if adopted) are continued throughout.
Under the modeling scenarios used, suppression policies would be shaped by a fluctuating number of cases. Researchers cited a peak number of cases before capacity for health care services would become overwhelmed and break down:
Table 3 illustrates that suppression policies are best triggered early in the epidemic, with a cumulative total of 200 ICU cases per week being the latest point at which policies can be triggered and still keep peak ICU demand below GB surge limits in the case of a relatively high R0 value of 2.6. Expected total deaths are also reduced for lower triggers, though deaths for all the policies considered are much lower than for an uncontrolled epidemic. The right panel of Table 4 shows that social distancing (plus school and university closure, if used) need to be in force for the majority of the 2 years of the simulation, but that the proportion of time these measures are in force is reduced for more effective interventions and for lower values of [rate of reproduction]. Table 5 shows that total deaths are reduced with lower “off” triggers; however, this also leads to longer periods during which social distancing is in place. Peak ICU demand and the proportion of time social distancing is in place are not affected by the choice of “off” trigger.
In the “Discussion” portion of the report, researchers reiterated their recommendations for public health action based on the results of the modeling:
Overall, our results suggest that population-wide social distancing applied to the population as a whole would have the largest impact; and in combination with other interventions – notably home isolation of cases and school and university closure – has the potential to suppress transmission below the threshold of R=1 required to rapidly reduce case incidence. A minimum policy for effective suppression is therefore population-wide social distancing combined with home isolation of cases and school and university closure.
To avoid a rebound in transmission, these policies will need to be maintained until large stocks of vaccine are available to immunise the population – which could be 18 months or more. Adaptive hospital surveillance-based triggers for switching on and off population-wide social distancing and school closure offer greater robustness to uncertainty than fixed duration interventions and can be adapted for regional use (e.g. at the state level in the US). Given local epidemics are not perfectly synchronised, local policies are also more efficient and can achieve comparable levels of suppression to national policies while being in force for a slightly smaller proportion of the time. However, we estimate that for a national GB policy, social distancing would need to be in force for at least 2/3 of the time (for R0=2.4, see Table 4) until a vaccine was available.
What is “flattening the curve,” and how is it relevant to this research?
A phrase you may have seen before (and after) the Imperial College London report’s appearance is “flattening the curve,” a reference to COVID-19’s “epidemic curve,” as it relates to the capacity of any country’s health care resources in times of vastly increased demand.
Social media users probably saw a graph very much like the one below from STATNews; the graph depicts a dashed horizontal line representing the capacity of health care systems in general:
A vertical axis on the graph represented “number of cases,” and a horizontal axis “time since first case.” “Flattening the curve” described spacing out the rate of infections to a degree sufficient that the dotted line — system capacity — would not be exceeded by the number of cases requiring the system’s resources.
By spacing out the rate of infection (or flattening the curve of infections), health care system capacity was less likely to be exceeded. Imperial College London’s research was predicated in part on flattening the curve for COVID-19, and circumventing a scenario in which capacity was exceeded by unchecked person-to-person spread of the virus.
Are these recommendations set in stone?
Researchers noted that because the strain was new, information and knowledge about the novel coronavirus would be in flux until there was more data. Consequently, they maintained that policies of suppression might change over time — but not for “several months” at least. The researchers also warned that premature relaxation of suppression measures could cause cases of COVID-19 to rapidly rebound, once again threatening to overwhelm health care systems.
That risk, again, remained until such time a vaccine became widely available:
However, there are very large uncertainties around the transmission of this virus, the likely effectiveness of different policies and the extent to which the population spontaneously adopts risk reducing behaviours. This means it is difficult to be definitive about the likely initial duration of measures which will be required, except that it will be several months. Future decisions on when and for how long to relax policies will need to be informed by ongoing surveillance.
The measures used to achieve suppression might also evolve over time. As case numbers fall, it becomes more feasible to adopt intensive testing, contact tracing and quarantine measures akin to the strategies being employed in South Korea today. Technology – such as mobile phone apps that track an individual’s interactions with other people in society – might allow such a policy to be more effective and scalable if the associated privacy concerns can be overcome. However, if intensive NPI packages aimed at suppression are not maintained, our analysis suggests that transmission will rapidly rebound, potentially producing an epidemic comparable in scale to what would have been seen had no interventions been adopted.
The report also indicated that the duration of suppression policies required might be too difficult to implement, discussing the relative efficacy of mitigation versus suppression. In a still relatively disruptive mitigation strategy scenario, the high number of projected deaths would be halved, and demand on healthcare services reduced by a still risky two-thirds:
Long-term suppression may not be a feasible policy option in many countries. Our results show that the alternative relatively short-term (3-month) mitigation policy option might reduce deaths seen in the epidemic by up to half, and peak healthcare demand by two-thirds. The combination of case isolation, household quarantine and social distancing of those at higher risk of severe outcomes (older individuals and those with other underlying health conditions) are the most effective policy combination for epidemic mitigation. Both case isolation and household quarantine are core epidemiological interventions for infectious disease mitigation and act by reducing the potential for onward transmission through reducing the contact rates of those that are known to be infectious (cases) or may be harbouring infection (household contacts) … Social distancing of high-risk groups is predicted to be particularly effective at reducing severe outcomes given the strong evidence of an increased risk with age, though we predict it would have less effect in reducing population transmission.
Researchers reiterated a bleak projected outcome with mitigation strategies in place. Although they anticipated a “single” and “relatively short” epidemic, health care capacity would be exceeded eight times over, and deaths would number a quarter of a million or more in the UK, and more than a million in the United States:
Perhaps our most significant conclusion is that mitigation is unlikely to be feasible without emergency surge capacity limits of the UK and US healthcare systems being exceeded many times over. In the most effective mitigation strategy examined, which leads to a single, relatively short epidemic (case isolation, household quarantine and social distancing of the elderly), the surge limits for both general ward and ICU beds would be exceeded by at least 8-fold under the more optimistic scenario for critical care requirements that we examined. In addition, even if all patients were able to be treated, we predict there would still be in the order of 250,000 deaths in GB, and 1.1-1.2 million in the US.
In its final paragraphs, researchers said suppression was “the only viable strategy” as of the publication of their report. They said the measures required to implement suppression were “profound,” but required countries to implement them “imminently”:
We therefore conclude that epidemic suppression is the only viable strategy at the current time. The social and economic effects of the measures which are needed to achieve this policy goal will be profound. Many countries have adopted such measures already, but even those countries at an earlier stage of their epidemic (such as the UK) will need to do so imminently.
Is there any supplemental information on the researchers’ findings?
Several expert responses are published here.
TL;DR and key takeaways
On March 16 2020, researchers from Imperial College London published “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand,” projecting several possible outcomes for the coronavirus pandemic in the following months.
In social media discourse, three of the possible outcomes were discussed — taking no measures or “doing nothing,” mitigation, and a stricter strategy of suppressing COVID-19 cases to a rate of reproduction less than one. Researchers recommended the latter strategy of suppression, going so far as to say it was the only viable strategy as of March 16 2020.
Among the report’s notable conclusions were the following:
- Governments, hospitals, and public health officials were limited to non-pharmaceutical approaches to COVID-19, as no vaccine or medicine existed to treat it as of March 2020;
- In a best-case scenario, a vaccine would be available in September 2021 at the absolute earliest, and until such time, the outlined strategies were strongly recommended;
- Mitigation and suppression were two approaches modeled by researchers, and suppression was considered the “only viable” option of the two;
- Under the most optimistic modeling of mitigation, “the surge limits for both general ward and ICU beds would be exceeded” at least eight times over;
- If mitigation occurred, an estimated million or more Americans would die of COVID-19 in the months after March 2020;
- Bleak projections in the mitigation model did not account for the effects of exceeding healthcare “surge capacity”;
- “Social distancing” for the entire population was defined in the report as follows: “All households reduce contact outside household, school or workplace by 75%. School contact rates unchanged, workplace contact rates reduced by 25%. Household contact rates assumed to increase by 25%.”
In conclusion, the drastic summaries of the Imperial College London coronavirus pandemic report were not exaggerations, although the conclusions it made clearly had not yet come to pass. Researchers predicted more than a million deaths in the United States alone even under the “mitigation” model with many public health restrictions, and they also emphasized that they believed the stricter “suppression” model was the only viable approach. Projections were based in part on the long duration between the start of the coronavirus pandemic and the development of a vaccine, which would take at least eighteen months.