How deadly is the new coronavirus? Data from the spread of US cases could help answer that.
More data on mild and asymptomatic cases is desperately needed.
As new reports of novel coronavirus cases surface along the U.S. West Coast, new research — and the existing disease surveillance network — may finally shed light on some of the most burning questions about the new virus, called SARS-CoV-2.
Among the most pressing questions: How many cases are asymptomatic, versus mild, moderate or severe? And what is the real rate of fatalities compared with the total number of cases?
Initial reports of the new coronavirus emerged from Wuhan, China, in December 2019, with patients presenting with pneumonia of unknown origin. As of March 2, more than 90,000 cases had been confirmed worldwide, including 45,705 cases that ended with patients recovering and more than 3,000 fatalities. On Feb. 28, U.S. health officials confirmed the first known case of the new coronavirus in a patient in the San Francisco Bay Area who had neither traveled abroad nor been exposed to someone known to have traveled to an area affected by the disease (which is called COVID-19). Since then, testing for the new coronavirus has quickly expanded, bringing the known total of cases to 105 in the U.S. Seven people in the U.S. have died from COVID-19.
Related: Live updates on COVID-19
Tracing U.S. spread
Genetic analysis of the virus circulating on the West Coast suggests that COVID-19 has been transmitting through the region for about six weeks. This community spread was not detected earlier for several reasons. First, about 81% of cases do not require hospitalization, according to data from the outbreak in China. People experiencing symptoms such as a mild fever, cough and congestion are unlikely to visit a doctor. Second, Centers for Disease Control and Prevention (CDC) protocol limited testing to only those with symptoms and a history of travel to an affected region. Finally, there is a lag between virus transmission and fatalities simply because it takes time for the most severe cases to kill. (A World Health Organization report from China found that it took three to six weeks for critical cases to be resolved, either when the patient died or recovered.)
What is not yet clear from the U.S. data is how many people have been infected with the new coronavirus. This number is key for understanding disease severity and the mortality rate — after all, you must know the total number of cases to know what proportion of patients will become severely ill or die. China's best data so far puts the case-fatality rate at 2.3%. But that number may drop with better detection of mild and asymptomatic cases.
Scientists expect to know more about this number in the coming weeks. Broader testing will help, Paul Biddinger, the vice chairman for emergency preparedness in the emergency medicine department of Massachusetts General Hospital, said in a Harvard T.H. Chan School of Public Health webcast on March 2. However, testing in the next days to weeks will still likely be limited to a subset of the sickest patients, Biddinger said.
"We have, right now, so few tests available that we have to prioritize testing for severe illness," he said in the webcast.
The weapons of public health
Another method of ferreting out new coronavirus cases is looking at existing influenza and respiratory illness surveillance. This is the bread-and-butter work of public health, said Jennifer Horney, the director of the epidemiology program at the University of Delaware. Most states have what's called "syndromic surveillance," in which emergency rooms, emergency medical services, poison control centers and other medical centers report occurrences of influenza-like symptoms. Washington state, for example, uses the Rapid Health Information Network (RHINO) to collect data in near real time.
Most states also have specific flu-monitoring networks, which gather reports of diagnosed influenza cases, usually on a weekly basis. All of this is information that state health departments can use to search for hints of undiagnosed COVID-19.
"They'll be able to go back and see, Did we have more than a typical number of influenza-like illnesses, given what we know now?" Horney told Live Science.
Related: 12 coronavirus myths busted by science
The number of cases it takes to raise the alarm depends on the infectious agent, the time of year and the population in a region, Horney said. In a large city like Seattle in the middle of winter, it might take hundreds of extra cases to raise the alarm, but in a less populous area in at the end of the season, it could take just a handful.
Already, researchers are seeking out coronavirus cases in a more active way. The Seattle Flu Study, which uses genetic sequencing to track the transmission of seasonal influenza, has begun testing its samples for possible coronavirus as well as flu. The team has already reported finding a case of coronavirus in a Snohomish County high school student who had tested negative for the flu and who had been sent home to recover from mild respiratory symptoms.
Public health researchers will also seek out cases based on interviews, similar to the way epidemiologists track an outbreak of foodborne illness, Horney said. As cases emerge, researchers reach out to hospitals and clinics in the affected area, searching for patients with telltale symptoms who were not diagnosed at the time of treatment. They can then interview those people to find out everywhere they've been and everyone they've interacted with. In the case of salmonella, a pattern might pop out: Everyone's eaten the same bagged spinach, or the same brand of fruit cup. In the case of COVID-19, the researchers might find that people with symptoms frequented the same stores or worked in the same office park. Already, the Washington state health department has monitoring contacts of the people already confirmed to have the coronavirus.
"If we find that shared exposure, then we can link all those cases, regardless of severity," Horney said.
Pyramid of cases
Tracking people with symptoms — whether mild, moderate or severe — is only the beginning, however. One big question about the new coronavirus is how many people transmit COVID-19 without showing symptoms at all, or showing so few symptoms they hardly realize they are sick, Marc Lipsitch, an epidemiologist at the Harvard T.H. Chan School of Public Health, said in the March 2 webcast. Asymptomatic carriers and people with mild symptoms may be like the base of the iceberg, Lipsitch said. They're hard to detect, but they're very important for modeling how the disease will spread.
"When we model transmission and when we project how many people are going to get infected, the models don't know how many people are 'sick' or 'really sick,' they know how many are infected" regardless of severity, Lipsitch said.
Scientists in China have already started doing studies that look for antibodies to the virus in people's blood, Lipsitch said. These studies are the only surefire way to confirm that someone has been infected with SARS-CoV-2 after the person recovers. The research will take time, but the more researchers know about the speed of the disease's spread, the more they'll be able to say about the likely length of the outbreak.
"What ultimately brings an epidemic under control," Lipsitch said, "is most people in the population becoming immune."
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Originally published on Live Science.
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Stephanie Pappas is a contributing writer for Live Science, covering topics ranging from geoscience to archaeology to the human brain and behavior. She was previously a senior writer for Live Science but is now a freelancer based in Denver, Colorado, and regularly contributes to Scientific American and The Monitor, the monthly magazine of the American Psychological Association. Stephanie received a bachelor's degree in psychology from the University of South Carolina and a graduate certificate in science communication from the University of California, Santa Cruz.
By Robert Lea
Should be very careful using Stanford's data on Santa Clara residents. It has not been peer reviewed and has some serious problems about sampling. People were recruited using Facebook Ad. People wanting to know their status are more motivated to volunteer. Self-selection is a problem. But is no way a random testing. Only people active on Facebook would have participated. This is not a good slice across a population. Lastly, I see no evidence that researcher ascertained whether multiple participants may have come from same household (family, roommates, etc.) If multiple samples from same household, more likely to get infected and make incidence much higher.
Data just reported from NYC from antibody testing indicates that percent of population is 12%-18%. This is significantly lower than Stanford study.
From the best I can tell (maybe someone can set me straight) the flu is not always diagnosed strictly on a positive flu test. You can walk into a doctor's office, present with flu like symptoms in the middle of flu season, and the doctor may diagnose it as flu without a test.
The CDC uses some type of formula to determine how many people actually have the flu - or at least that's how I view it.
In the 2018-2019 season the 35,520,883 number doesn't necessarily mean that 35,520,883 people were tested for the flu and came back as positive. I'm not sure if the 16,520,350 number is positive flu tests. The 490,561 hospitalizations are probably true positive flu tests, or at least I'd come nearer to believe that all of those tested positive for the flu. But there were definitely more than 490,561 that would have tested positive for the flu. But how many? We don't know.
Until you know the number of positive flu tests and how many of that subset died you really can't compare it to the number of positive covid-19 test and how many of that subset died.
Either way, covid-19 has already killed more than the 34,157 the flu killed in 2018-2019. And then consider, that 34,157 number you are using is probably a lot smaller, because you can really only count deaths that came from real positive flu tests.
The only thing you can really compare it to is the mortality rate - number of deaths out of an entire population.
Flu: 34,157 deaths out of 320,000,000 people is a mortality rate of 0.0107%
Covid-19: 49,963 deaths out of 320,000,000 people is a mortality rate of 0.0156%
That might not seem like a lot, but it is more. And it will continue to grow. Is it more than the flu? Yes. Worth the panic? That's debatable.
You also have to consider that there's no vaccine for covid-19, but there is for the flu. The flu has been around for years... Covid-19 is new, so this means that the human body just hasn't seen anything like Covid-19 to base any semblance of an immune response from. So, knowing that, we knew Covid-19 was going to kill more people than the flu. If Covid-19 winds up killing 70,000 to 90,000 people I'm not sure if that is all that unexpected.
And as I've stated several times. The numbers in New York City are way out in left field and really threw the models for a loop. I think there's definitely something going on in New York City (and to some extent the whole New England region) that is spreading this virus for than in other areas of the country. If the number of deaths in New York City had stuck to what was happening throughout the rest of the country, then that would probably be about 10,000 fewer deaths. Obviously that didn't happen in New York City, but it's worth microscoping some of the numbers coming out of New York City and when all is said and done a study probably needs to be done to figure out what happened in New York City with this virus to help prevent that from happening again.