Health News

Fever checks are a flawed way to flag Covid-19 cases. Experts say smell tests might help

Travelers are displayed on a video screen walking past a test system of thermal imaging cameras which check body temperatures at Los Angeles International Airport.

Workplaces do it. Newly reopened public libraries do it. LAX does it. Some restaurants, bars, and retail stores started doing it when governors let them serve customers again: Use temperature checks — almost always with “non-contact infrared thermometers” — to identify people who might have, and therefore spread, the infectious disease.

Unfortunately, temperature checks could well join the long list of fumbled responses to the pandemic, from the testing debacle to federal officials’ about-face on masks.

Because many contagious people have no symptoms, using temperature checks to catch them is like trying to catch tennis balls in a soccer net: way too many can get through. On Tuesday, the head of the Transportation Security Administration told reporters, “I know in talking to our medical professionals and talking to the Centers for Disease Control … that temperature checks are not a guarantee that passengers who don’t have an elevated temperature also don’t have Covid-19.” The reverse is also true: Feverish travelers might not have Covid-19.

In this case, however, a growing body of science suggests a simple fix: make smell tests another part of routine screenings.

Of all the nose-to-toes symptoms of Covid-19, the loss of the sense of smell — also known as anosmia — could work particularly well as an add-on to temperature checks, significantly increasing the proportion of infected people identified by screening in airports, workplaces, and other public places.

“My impression is that anosmia is an earlier symptom of Covid-19 relative to fever, and some infected people can have anosmia and nothing else,” said physician Andrew Badley, who heads a virus lab at the Mayo Clinic. “So it’s potentially a more sensitive screen for asymptomatic patients.”

In a recent study, Badley and colleagues found that Covid-19 patients were 27 times more likely than others to have lost their sense of smell. But they were only 2.6 times more likely to have fever or chills, suggesting that anosmia produces a clearer signal and may therefore be a better Covid-catching net than fever.

There is no definitive study on the predictive value of temperature checks for Covid-19. But there are clues from when that strategy was used during the SARS epidemic of 2003. Deployed at airports, especially in Asia, the devices fell far short of the ideal, an analysis found. Although contactless thermometers are quite accurate if used correctly, many other conditions (including medications and inflammatory disease) can cause fever. As a result, the likelihood that someone with a fever had SARS ranged from 4% to 65%, depending on the underlying prevalence of the disease.

The likelihood that someone with a normal temperature reading was SARS-free was at least 86%. That suggests SARS fever checks didn’t miss many infected people. Unlike SARS, unfortunately, Covid-19 can be contagious even before an infected person runs a fever, which makes missed cases more likely.

As experts have cast around for other screening tools, some have zeroed in on smell tests, which could be as simple asking people to identify a particular scent from a scratch-and-sniff card. Though not a universal symptom, loss of smell is one of the earliest signs of Covid-19 because of how the virus acts. Support cells in the olfactory epithelium, the tissue that lines the nasal cavities, are covered with the receptors that SARS-CoV-2 uses to enter cells. They become infected very early in the disease process, often before the body has mounted the immune response that causes fever.

“These support cells either secrete molecules that shut down the olfactory receptor neurons, or stop working and starve the neurons, or somehow fail to support the neurons,” said Danielle Reed, associate director of Monell Chemical Senses Center, a world leader in the science of taste and smell. As a result, “the [olfactory neurons] either stop working or die.”

In an analysis of 24 individual studies, with data from 8,438 test-confirmed Covid-19 patients from 13 countries, 41% reported that they had lost their sense of smell partly or completely, researchers reported in Mayo Clinic Proceedings. But in studies that used objective measurements of smell rather than simply asking patients, the incidence of anosmia was 2.3 times higher.

A Monell analysis of 47 studies finds that nearly 80% of Covid-19 patients have lost their sense of smell as determined by scratch-and-sniff tests, Reed said. But only about 50% include that in self-reported symptoms. In other words, people don’t realize they have partly or even completely lost their sense of smell. That may be because they’re suffering other, more serious symptoms and so don’t notice this one, or because smell isn’t something they focus on.

In a recent study of 1,480 patients led by otolaryngologist Carol Yan of UC San Diego Health, someone with anosmia was “more than 10 times more likely to have Covid-19 than other causes of infection,” she said. Nasal inflammation from some 200 cold, flu, and other viruses can cause it, she said, but especially during the summer, when those infections are pretty rare, the chance that anosmia is the result of Covid-19 rises.

“Anosmia was quite specific to Covid-19,” she said.

Fever, in contrast, has many possible causes. Temperature checks will therefore flag more people as potentially infected with Covid-19 than smell tests will. The likelihood that anosmia indicates Covid-19, called a test’s positive predictive value, increases as the prevalence of Covid-19 increases, as it is in many areas of the U.S.

A key unanswered question is a smell test’s “negative predictive value”: If someone has a normal sense of smell, the chance that he or she is nevertheless infected and likely contagious. Because at least some people infected with SARS-CoV-2 will have a normal sense of smell, especially early on, even experts who believe that anosmia screening can be widely beneficial — “I hope it will be used as a screening measure for the virus across the world,” Yan said — say it should be added to fever checks or other screening tools, but shouldn’t replace them.

“There is value in evaluating anosmia screening as a way to identify asymptomatic spreaders,” said Badley, the Mayo Clinic researcher.

UC San Diego Health is doing that. It asks about loss of smell (and taste) when it screens visitors and staff before allowing them to enter its buildings.

Because many people are unaware of their anosmia, testing would be even better than asking, Reed said.

The gold-standard test is the University of Pennsylvania Smell Identification Test, called UPSIT. It uses 40 microencapsulated scents — including dill pickle, turpentine, banana, soap, licorice, and cedar — released by scratching with a pencil. The test taker has a choice of four answers for each, and the whole thing takes 10 to 15 minutes.

A screening test for anosmia in the context of Covid-19 could be much simpler, experts say, especially since the idea is to identify whether individuals can smell or not, rather than whether they can discriminate different scents.

“I can see several practical ways is to have people check their sense of smell as a routine matter when entering public areas,” Reed said. Medical offices could “ask people to smell a scratch-and-sniff card and pick the correct odor out of four choices. For workplaces and schools, one way is to ask people to ‘stop and smell the roses’ as they enter buildings and report abrupt reductions in their ratings of odor intensity.”

To avoid cultural bias (not everyone knows what bubblegum or grass smells like), a test for anosmia in Covid-19 could have a standard amount of phenyl-ethyl alcohol (which smells like roses) on a swab or stick and have people sniff it, Reed said. A second stick could have less, testing for a diminished sense of smell. A third stick could be a blank, to identify people who falsely claim they can smell.

CanSino’s COVID-19 Vaccine Gets Approval to be Used for Chinese Military

China’s Central Military Commission has approved a vaccine candidate developed jointly by its research unit, Academy of Military Science (AMS), and CanSino Biologics for use in the military.

The decision was taken after Ad5-nCoV’s clinical trials produced good results, meaning that it was safe and showed efficacy in combating the new coronavirus.

The vaccine candidate has also been approved for human trials in China, Canada, and other countries as well and it is one of the 8 candidates approved for trials.

The Ad5-nCoV is currently limited to military use only and its use cannot be expanded to a broader vaccination range without the approval of the Logistics Support Department.

China had earlier greenlighted the use of two other COVID-19 vaccine candidates for the employees of state-owned firms traveling overseas. CanSino has said that Phase 1 and 2 of the clinical trials of Ad5-nCoV showed the potential to prevent the respiratory disease caused by the novel coronavirus, however, its commercial success cannot be guaranteed.

As of now, no vaccine has been approved for commercial use, however, over a dozen candidates are being tested for human trials across the globe.

What You Need to Know About Asymptomatic Spread of Covid-19

Nearly six months have passed since Chinese officials first reported the emergence of a strange new pneumonia in Wuhan City. Despite months of concerted effort from the world’s scientific community, experts still aren’t certain just how the virus spreads — or who is capable of spreading it.

During a media briefing on June 8, an official at the World Health Organization set off a broad volley of expert rebukes when she said that it “appears to be rare” for an asymptomatic person to transmit the virus to others.

“We have a number of reports from countries who are doing very detailed contact tracing,” said the WHO official, Maria Van Kerkhove, PhD, who is that organization’s Covid-19 technical lead. “They’re following asymptomatic cases, they’re following contacts, and they’re not finding secondary transmission onward. It is very rare.”

In addition to Van Kerkhove’s comments, the WHO also published a report that stated: “Asymptomatically-infected individuals are much less likely to transmit the virus than those who develop symptoms.”

“The message should have been that symptom-free spread has been difficult to detect via contact tracing, not that it isn’t happening.”

If people who do not develop noticeable symptoms are unlikely to spread Covid-19 to others, that would be a significant revelation. It would radically change the way governments and health officials approach quarantines and closures, and it would make tracing and controlling the virus much simpler. Unfortunately, most experts feel there is little evidence to support the WHO’s statements — which the organization itself partly retracted the day after its controversial briefing.

“It was all really unfortunate because it engendered a lot of confusion,” says Eric Topol, MD, a professor of molecular medicine at the Scripps Research Institute in California. “We know that asymptomatic people can infect others.”

Topol has published work on those who contract Covid-19 but do not develop symptoms, and on the “silent spread” that they may unwittingly induce. He says that it’s not clear what proportion of cases stem from people who do not have symptoms, a group that may include 40% or more of those infected with Covid-19. The WHO report mentions one study of 63 asymptomatic patients in China that found that 14% seemed to have passed on Covid-19 to others. But the WHO says that the data on this is poor due in part to the limitations of contact-tracing techniques, which attempt to track a virus by monitoring those who have been in contact with someone who tests positive.

This is an important qualification that other experts echo. “The message should have been that symptom-free spread has been difficult to detect via contact tracing, not that it isn’t happening,” says Jeremy Faust, MD, an instructor at Harvard Medical School and an emergency medicine physician at Brigham and Women’s Hospital.

Faust has published research comparing Covid-19 to seasonal flu. He explains that the WHO’s contract tracing procedures call for monitoring people who are in touch with a Covid-19 carrier within 48 hours of either symptom emergence or a positive test result. “That’s a great approach in infections that do not spread asymptomatically or for very long. SARS-CoV-2, unfortunately, does not play by those rules,” he explains. “Some people shed virus and are contagious further back than just two days before first symptoms.” It’s also very possible that those who test positive for Covid-19 are spreading the virus outside of the WHO’s 48-hour window.

Scripps’s Topol says that another problem surrounding asymptomatic cases is that a lot of the messaging from doctors and the media has unhelpfully downplayed the potential seriousness of these infections. Just because someone is asymptomatic does not mean that the virus is not doing damage.“If you do a CAT scan of these patients’ lungs, you see significant abnormalities tied to Covid, and these are woefully understudied,” he says. “We also know this the virus can go to the heart and kidneys, so that needs to be looked at too.” It’s possible, he adds, that these “below-the-surface problems” may increase a person’s risk for long-term health complications, such as the eventual development of lung disease.

Finally, he points out an issue that has more to do with media coverage of the WHO statements than with the statements themselves. Van Kerkhove, before saying that asymptomatic transmission may be “very rare,” took pains to point out that there’s a difference between people who are asymptomatic — meaning those who never develop noticeable symptoms — and those who are presymptomatic, which refers to those who are infected with Covid-19 and will eventually go on to show symptoms. According to the WHO, it may take as long as 14 days for these presymptomatic carriers to become symptomatic.

“We know presymptomatic people are highly infectious, and they’re responsible for a lot of the virus’s spread,” Topol says. In fact, there’s some evidence that these presymptomatic people may be even more likely to transmit the disease than people who have symptoms, he says. And so the idea that people who don’t have symptoms are unlikely to spread the virus is flawed and dangerous on many levels.

“We’re still in the dark on a lot of things,” Topol adds. “There are a lot of very important questions we need to answer.”

Just what exactly is the “R” number, with regards to COVID?

The reproduction number (R) is the average number of secondary infections produced by 1 infected person.

An R number of 1 means that on average every person who is infected will infect 1 other person, meaning the total number of new infections is stable. If R is 2, on average, each infected person infects 2 more people. If R is 0.5 then on average for each 2 infected people, there will be only 1 new infection. If R is greater than 1 the epidemic is growing, if R is less than 1 the epidemic is shrinking.

R can change over time. For example, it falls when there is a reduction in the number of contacts between people, which reduces transmission.

What is a growth rate?

The growth rate reflects how quickly the number of infections are changing day by day It is an approximation of the change of number infections each day. If the growth rate is greater than zero (+ positive), then the disease will grow. If the growth rate is less than zero (- negative) then the disease will shrink.

The size of the growth rate indicates the speed of change. A growth rate of +5% will grow faster than one with a growth rate of +1%. Likewise, a disease with a growth rate of -4% will be shrinking faster than a disease with growth rate of -1%. Further technical information on growth rate can be found on Plus magazine.

How are growth rates different to R estimates?

R does not tell us how quickly an epidemic is changing. Different diseases with the same R can give epidemics that grow at very different speeds. For instance, a disease with R=2 with infection lasting years will grow much more slowly than a disease with R=2 with infection lasting days.

The growth rate provides us with information on the size and speed of change, whereas the R value only gives us information on the direction of change.

To calculate R, information on the time taken between each generation of infections is needed. That is how long it takes for one set of people in an infected group to infect a new set of people in the next group. This can depend on several different biological, social, and behavioural factors. The growth rate does not depend on the “generation time” and so requires fewer assumptions to estimate.

Neither one measure, R nor growth rate, is better than the other but each provide information that is useful in monitoring the spread of a disease.

The R estimate and growth rates are not the only important measures of the epidemic. Both should be considered alongside other measures of the spread of disease, such as the number of people currently infected. If R equals 1 with 100,000 people currently infected, it is a very different situation to R equals 1 with 1,000 people currently infected. The number of people currently infected with coronavirus (COVID-19) – and so able to pass it on – is therefore very important.

Estimates of the growth rates and R are currently updated on a weekly basis. However, as the numbers of cases decrease, these metrics will become less helpful indicators and other measures need to be considered. These include the number of new cases of the disease identified during a specified time period (incidence), and the proportion of the population with the disease at a given point in time (prevalence), and these will become more important to monitor.

How are R and growth rates estimated?

Individual modelling groups use a range of data to estimate growth rates and R values including:

  • epidemiological data such as hospital admissions, ICU admissions and deaths – it generally takes 2 to 3 weeks for changes in the spread of disease to be reflected in the estimates due to the time delay between initial infection and the need for hospital care
  • contact pattern surveys that gather information on behaviour – these can be quicker (with a lag of around a week) but can be open to bias as they often rely on self-reported behaviour and make assumptions about how the information collected relates to the spread of disease.
  • household infection surveys where swabs are performed on individuals. These can provide estimates of how many people are infected. Longitudinal surveys (where samples are repeatedly taken from the same people) allow a more direct estimate of the growth in infection rates

Different modelling groups use different data sources to estimate these values using mathematical models that simulate the spread of infections. Some may even use all these sources of information to adjust their models to better reflect the real-world situation. There is uncertainty in all these data sources so estimates can vary between different models, so we do not rely on just one model; evidence from several models is considered, discussed, combined, and the growth rate and R are then presented as a range. The most likely true values are somewhere towards the middle of this range.

Who estimates the R and growth rates?

The growth rate and R are estimated by several independent modelling groups based in universities and Public Health England (PHE). The modelling groups discuss their individual R estimates at the Science Pandemic Influenza Modelling group (SPI-M) – a subgroup of SAGE. Attendees compare the different estimates of each and SPI-M collectively agrees a range for which the values are very likely to be within.

Limitations of R

R is an average value that can vary in different parts of the country, communities, and subsections of the population. It cannot be measured directly so there is always uncertainty around its exact value. This becomes even more of a problem when calculating R using small numbers of cases, either due to lower infection rates or smaller geographical areas. This uncertainty may be due to variability in the underlying data, leading to a wider range for R and more frequent changes in the estimates.

Even when the overall UK R estimate is below 1, some regions may have R estimates that include ranges that exceed 1, for example from 0.7 to 1.1; this does not necessarily mean the epidemic is increasing in that region, just that the uncertainty means it cannot be ruled out. It is also possible that an outbreak in one specific place could result in an R above 1 for the whole region.

Estimates of R for geographies smaller than regional level are less reliable and it is more appropriate to identify local hotspots through, for example, monitoring numbers of cases, hospitalisations, and deaths.

Limitations of growth rates

The growth rate is an average value that can vary. When case numbers are low, uncertainty increases. This could happen when only a very small proportion of people are infected, or the geographical area considered has a very small population. A smaller number of cases means that variability in the underlying data makes it difficult to estimate the growth rate; there will be a wider range given for growth rate and frequent changes in the estimates. This will happen for both R and the growth rate; however, the growth rate requires fewer assumptions about the disease when it is calculated than R.

Even when the overall UK growth rate estimate is negative (below 0), some regions may have growth rate estimates that include ranges that are positive (above 0), for example from -4% to +1%; this does not necessarily mean the epidemic is increasing in that region, just that the uncertainty means it cannot be ruled out. It is also possible that an outbreak in one specific place could result in a positive (above 0) growth rate for the whole region.