The evolving coronavirus disease 2019 (COVID‐19) pandemic
is certainly cause for concern. Proper communication and optimal decision‐making are
an ongoing challenge, as data evolve. The challenge is compounded, however, by exaggerated
information. This can lead to inappropriate actions. It is important to differentiate
promptly the true epidemic from an epidemic of false claims and potentially harmful
FAKE NEWS AND WITHDRAWN PAPERS
Based on Altmetric scores, the most discussed and most visible scientific paper across
all 20+ million papers published in the last 8 years across all science is a preprint
claiming that the new coronavirus' spike protein bears “uncanny similarity” with HIV‐1
The Altmetric score of this work has reached an astronomical level of 13 725 points
as of 5 March 2020. The paper was rapidly criticized as highly flawed, and the authors
withdrew it within days. Regardless, major harm was already done. The preprint fuelled
conspiracy theories of scientists manufacturing dangerous viruses and offered ammunition
to vaccine deniers. Refutation will probably not stop dispersion of weird inferences.
The first report documenting transmission by an asymptomatic individual was published
in the New England Journal of Medicine on January 30. However, the specific patient
did have symptoms, but researchers had not asked.
Understanding the chances of transmission during the asymptomatic phase has major
implications for what protective measures might work.
Lancet published on February 24 an account from two Chinese nurses of their front‐line
experience fighting coronavirus. The authors soon retracted the paper admitting it
was not a first‐hand account.
These examples show how sensationalism affects even top scientific venues. Moreover,
peer review may malfunction when there is little evidence and strong opinions. Opinion‐based
peer review may even solidify a literature of spurious statements. As outlined below,
for the main features of the epidemic and the response to it, circulating estimates
are often exaggerated, even when they come from otherwise excellent scientists.
EXAGGERATED PANDEMIC ESTIMATES
An early speculation that 40%‐70% of the global population will be infected went viral.
Early estimates of the basic reproduction number (how many people get infected by
each infected person) have varied widely, from 1.3 to 6.5.
These estimates translate into manyfold difference in the proportion of the population
eventually infected and dramatically different expectations on what containment measures
(or even any future vaccine) can achieve. The fact that containment measures do seem
to work, means that the basic reproduction number is probably in the lower bound of
the 1.3‐6.5 range, and can decrease below 1 with proper measures. The originator of
the “40%‐70% of the population” estimate tweeted on March 3 a revised estimate of
“20%‐60% of adults,” but this is probably still substantially exaggerated. Even after
the 40%‐70% quote was revised downward, it still remained quoted in viral interviews.
EXAGGERATED CASE FATALITY RATE (CFR)
Early reported CFR figures also seem exaggerated. The most widely quoted CFR has been
3.4%, reported by WHO dividing the number of deaths by documented cases in early March.
This ignores undetected infections and the strong age dependence of CFR. The most
complete data come from Diamond Princess passengers, with CFR = 1% observed in an
elderly cohort; thus, CFR may be much lower than 1% in the general population, probably
higher than seasonal flu (CFR = 0.1%), but not much so.
Observed crude CFR in South Korea and in Germany,
the countries with most extensive testing, is 0.9% and 0.2%, respectively, as of March
14, and crude CFR in Scandinavian countries is about 0.1%. Some deaths of infected,
seriously ill people will occur later, and these deaths have not been counted yet.
However, even in these countries many infections probably remain undiagnosed. Therefore,
CFR (or, more properly called, infection fatality rate, counting as cases all infected
individuals) may be even lower rather than higher than these crude estimates.
EXAGGERATED EXPONENTIAL COMMUNITY SPREAD
At face value, the epidemic curve of new cases outside China since late February is
compatible with exponential community spread. However, reading this curve is very
difficult. Part of the growth of documented cases could reflect rapid increases in
numbers of coronavirus tests performed. The number of tests done depends on how many
test‐kits are available and how many patients seek testing. Even if bottlenecks in
test availability are eventually removed, the epidemic curve may still reflect primarily
population sensitization and willingness for testing rather than true epidemic growth.
China data are more compatible with close contact rather than wide community spread
being the main mode of transmission.
Under alarming circumstances, extreme measures of unknown effectiveness are adopted.
China initially responded sluggishly, but subsequently locked down entire cities.
School closures, cancellation of social events, air travel curtailment and restrictions,
entry control measures and border closure are applied by various countries. Italy
adopted country‐level lockdown on March 8, and many countries have been following
Evidence is lacking for the most aggressive measures. A systematic review on measures
to prevent the spread of respiratory viruses found insufficient evidence for entry
port screening and social distancing in reducing epidemic spreading.
Plain hygienic measures have the strongest evidence.
Frequent hand washing and staying at home and avoiding contacts when sick are probably
very useful. Their routine endorsement may save many lives. Most lives saved may actually
be due to reduced transmission of influenza rather than coronavirus.
Most evidence on protective measures comes from nonrandomized studies prone to bias.
A systematic review of personal protective measures in reducing pandemic influenza
risk found only two randomized trials, one on hand sanitizer and another on facemasks
and hand hygiene in household members of people infected with influenza.
HARMS FROM NONEVIDENCE‐BASED MEASURES
Given the uncertainties, one may opt for abundant caution and implement the most severe
containment measures. By this perspective, no opportunity should be missed to gain
any benefit, even in the absence of evidence or even with mostly negative evidence.
This reasoning ignores possible harms. Impulsive actions can indeed cause major harm.
One clear example is the panic shopping which depleted supplies of face masks, escalation
of prices and a shortage for medical personnel. Masks, gloves and gowns are clearly
needed for medical personnel, and their lack poses healthcare workers' lives at risk.
Conversely, they are meaningless for the uninfected general population. However, a
prominent virologist's comment
that people should stock surgical masks and wear them around the clock to avoid touching
their nose went viral.
MISALLOCATION OF RESOURCES
Policymakers feel pressure from opponents who lambast inaction. Also, adoption of
measures in one institution, jurisdiction or country creates pressure for taking similar
measures elsewhere under fear of being accused of negligence. Moreover, many countries
pass legislation that allocates major resources and funding to the coronavirus response.
This is justified, but the exact allocation priorities can become irrational.
For example, undoubtedly research on coronavirus vaccines and potential treatments
must be accelerated. However, if only part of resources mobilized to implement extreme
measures for COVID‐19 had been invested towards enhancing influenza vaccination uptake,
tens of thousands of influenza deaths might have been averted. Only 1%‐2% of the population
in China is vaccinated against influenza. Even in the United States, despite improvements
over time, most adults remain unvaccinated every year.
As another example, enhanced detection of infections and lower hospitalization thresholds
may increase demands for hospital beds. For patients without severe symptoms, hospitalizations
offer no benefit and may only infect health workers causing shortage of much‐needed
personnel. Even for severe cases, effectiveness of intensive supportive care is unknown.
Excess admissions may strain health care systems and increase mortality from other
serious diseases where hospital care is clearly effective.
LOCKDOWNS—FOR HOW LONG?
An argument in favour of lockdowns is that postponing the epidemic wave (“flattening
the curve”) gains time to develop vaccines and reduces strain on the health system.
However, vaccines take many months (or years) to develop and test properly. Maintaining
lockdowns for many months may have even worse consequences than an epidemic wave that
runs an acute course. Focusing on protecting susceptible individuals may be preferable
to maintaining countrywide lockdowns longterm.
ECONOMIC AND SOCIAL DISRUPTION
The potential consequences on the global economy are already tangible. February 22‐28
was the worst week for global markets since 2008, and the worse may lie ahead. Moreover,
some political decisions may be confounded with alternative motives. Lockdowns weaponized
by suppressive regimes can create a precedent for easy adoption in the future. Closure
of borders may serve policies focused on limiting immigration. Regardless, even in
the strongest economies, disruption of social life, travel, work and school education
may have major adverse consequences.
The eventual cost of such disruption is notoriously difficult to project. A quote
of $2.7 trillion
is totally speculative. Much depends on the duration of the anomaly. The global economy
and society is already getting a major blow from an epidemic that otherwise (as of
March 14) accounts for 0.01% of all 60 million annual global deaths from all causes
and that kills almost exclusively people with relatively low life expectancy.
CLAIMS FOR ONCE‐IN‐A‐CENTURY PANDEMIC
Leading figures insist that the current situation is a once‐in‐a‐century pandemic.
A corollary might be that any reaction to it, no matter how extreme, is justified.
This year's coronavirus outbreak is clearly unprecedented in amount of attention received.
Media have capitalized on curiosity, uncertainty and horror. A Google search with
“coronavirus” yielded 3 550 000 000 results on March 3 and 9 440 000 000 results on
March 14. Conversely, “influenza” attracted 30‐ to 60‐fold less attention although
this season it has caused so far more deaths
globally than coronavirus.
Different coronaviruses actually infect millions of people every year, and they are
common especially in the elderly and in hospitalized patients with respiratory illness
in the winter. A serological analysis
of CoV 229E and OC43 in 4 adult populations under surveillance for acute respiratory
illness during the winters of 1999‐2003 (healthy young adults, healthy elderly adults,
high‐risk adults with underlying cardiopulmonary disease and a hospitalized group)
showed annual infection rates ranging from 2.8% to 26% in prospective cohorts, and
prevalence of 3.3%‐11.1% in the hospitalized cohort. Case fatality of 8% has been
described in outbreaks among nursing home elderly.
Leaving the well‐known and highly lethal SARS and MERS coronaviruses aside, other
coronaviruses probably have infected millions of people and have killed thousands.
However, it is only this year that every single case and every single death gets red
alert broadcasting in the news.
COMPARISONS WITH 1918
Some fear an analogy to the 1918 influenza pandemic that killed 20‐40 million people.
Retrospective data from that pandemic suggest that early adoption of social distancing
measures was associated with lower peak death rates.
However, these data are sparse, retrospective and pathogen‐specific. Moreover, total
deaths were eventually little affected by early social distancing: people just died
several weeks later.
Importantly, this year we are dealing with thousands, not tens of millions deaths.
LEARNING FROM COVID‐19
The Box 1 summarizes the problems with inaccurate and exaggerated information in the
case of COVID‐19. Even if COVID‐19 is not a 1918‐recap in infection‐related deaths,
some coronavirus may match the 1918 pandemic in future seasons. Thus, we should learn
and be better prepared. Questions about transmission, duration of immunity, effectiveness
of different containment and mitigation methods, the role of children in viral spread,
and assessment of the effectiveness of vaccines and drugs are essential to settle
Problems with early estimates and responses to the COVID‐19 epidemic
A highly flawed nonpeer‐reviewed preprint claiming similarity with HIV‐1 drew tremendous
attention, and it was withdrawn, but conspiracy theories about the new virus became
Even major peer‐reviewed journals have already published wrong, sensationalist items
Early estimates of the projected proportion of global population that will be infected
seem markedly exaggerated
Early estimates of case (infection) fatality rate may be markedly exaggerated
The proportion of undetected infections is unknown but probably varies across countries
and may be very large overall
Reported epidemic curves are largely affected by the change in availability of test
kits and the willingness to test for the virus over time
Of the multiple measures adopted, a few have strong evidence, and many may have obvious
Panic shopping of masks and protective gear and excess hospital admissions may be
highly detrimental to health systems without offering any concomitant benefit
Extreme measures such as lockdowns may have major impact on social life and the economy (and
those also lives lost), and estimates of this impact are entirely speculative
Comparisons with and extrapolations from the 1918 influenza pandemic are precarious,
if not outright misleading and harmful
This research agenda requires carefully collected, unbiased data to avoid unfounded
inferences. Larger‐scale diagnostic testing should help get more unbiased estimates
of cases, basic reproduction number and infection fatality rate. The research agenda
also deserves proper experimental studies. Besides candidate vaccines and drugs, randomized
trials should evaluate also the real‐world effectiveness of simple measures (eg face
masks in different settings), least disruptive social distancing measures and healthcare
management policies for documented cases.
If COVID‐19 is indeed the pandemic of the century, we need the most accurate evidence
to handle it. Open data sharing of scientific information is a minimum requirement.
This should include data on the number and demographics of tested individuals per
day in each country and the demographics and background diseases of patients requiring
hospital care and intensive care and those who die. Proper prevalence studies and
trials are also indispensable.
If COVID‐19 is not as grave as it is depicted, high evidence standards are equally
relevant. Exaggeration and overreaction may seriously damage the reputation of science,
public health, media and policymakers. It may foster disbelief that will jeopardize
the prospects of an appropriately strong response if and when a more major pandemic
strikes in the future.
CONFLICT OF INTEREST