In the case of COVID-19, two recent papers shed new light on the virus and its propensity to defy preconceived ideas. The first paper still in pre-print which means it hasn’t been peer-reviewed, describes antibody response. The second describes a study of Italian post-COVID patients looking at symptom persistence for those who no longer have the virus.
In a collaborative paper written by clinical medical researchers at King’s College London, the Centre for Clinical Infection and Diagnostics Research, Guy’s and St Thomas’ NHS Foundation, and the University of Kent in the United Kingdom, the authors studied antibody responses in infected patients.
They noted that antibodies first appeared in the most infected individuals within 10 to 15 days after symptoms emerged. Serum tests after 94 days revealed that the level of antibody response was affected by the severity of the infection, but that levels declined significantly over the following 60 days suggesting that immunity to COVID-19 may be only transient and not permanent.
In the paper’s conclusions, the authors raise the issue that the number of antibodies present in previously infected people over time, may be insufficient to stop reinfection from occurring.
For those developing vaccines to create a “robust” antibody response, these findings are problematic. For a vaccine to be effective, it may mean frequent repeat inoculations of the population as the only way to ensure long-term protection.
Is this any different from the annual influenza vaccine? Yes and no. With influenza, we have a virus that rapidly evolves from year to year. The vaccines that are administered to the general population are usually a cocktail reflecting the seasonal changes to the virus. What happens in terms of influenza strains in Australia six months before determines the vaccine cocktail administered in the northern hemisphere.
With COVID-19, we as of yet don’t know at what rate the virus will evolve. There is a suggestion that the virus currently devastating the United States, India, Brazil, and South Africa, is already a new strain, more potent in its spread than what first emerged from Hubei Province in China late in 2019. Should COVID-19 exhibit the same evolutionary characteristics of influenza virus strains, then annual inoculations will become the primary means of fighting it.
A research letter published on the JAMA Network authored by doctors from the Fondazione Policlinico Universitario Agostino Gemelli, in Rome, Italy, has described persistent symptoms in patients who seemingly have recovered from COVID-19.
Involving 143 patients from April 21 to May 29, 2020, with a mean age of 56.5 (ranging from 19 to 84-year-olds), 53% women, and an average hospital stay of 13.5 days, the study included subjects that had both non-invasive (15%) and invasive ventilation treatment (5%).
What emerged was a consistent pattern of persistent long-term symptoms in nearly 90% of the patients. The one most commonly described was fatigue and dyspnea (shortness of breath) 60 days after the onset of the infection. Other symptoms included joint and chest pain with 10% still experiencing a cough and another 10% indicating that their sense of smell had not returned. Over half exhibited three symptoms typical of the virus. A mere 13% were symptom-free.
Although statistically a small sample, what can we surmise from this study? Survivors of COVID-19 don’t come out of the infection without baggage.
On a personal note, in my own post-COVID-19 recovery and that of my wife, we contracted the virus early in April, and now in mid-July we continue to have persistent symptoms without any evidence of the virus in our bodies.
How do I know I no longer have COVID-19? Yesterday, I had my fourth COVID-19 test and it came back negative. That’s four tests since April 24th. By now I think it is pretty clear that I am no longer infected.
The symptoms that were COVID-specific have largely moderated. But what has persisted is arrhythmias caused by the virus infecting my heart, pain in my joints, transient skin rashes, and chronic fatigue. All four are within the spectrum of COVID-19 symptoms. The Italian researchers could have added me to their study and I would have fit in.
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Today, a new study presents a good reality check for Mr. Trump and those who want to open the economy quickly before mass testing.
In a COVID-19 antibodies study using serology and involving over 3,000 volunteers in Santa Clara County, California, it found that between 2.5% and 4% of those tested showed they had been infected by the virus.
If you are not familiar with serology, it is a method of determining whether someone has been infected by a bacteria, fungus, or virus. The technology has been around for a century and can detect infection where other forms of testing cannot. What it shows in an infected person is the production of antibodies. What it doesn’t show is if the immune system is producing a sufficient amount of these antibodies to ward off repeat exposures. It also may not show the presence of antibodies until weeks after a person is exposed to an infectious agent. Sometimes the presence of antibodies may not be from something recent, but rather from something that happened further back in time, making tracing back to the source of transmission difficult. As a result, serology tends to be used last in a testing regime.
But the Santa Clara results, at between 2.5% and 4%, suggest that the presence of COVID-19 in that county is 50 to 85 times greater than the number of cases reported so far. If that holds true for the general population of the United States, then the reported viral caseload as of this Sunday morning which stands at over 740,000 is underrepresenting the actual spread of the virus. At 50 times greater, it means 37 million Americans have been exposed, and at 85 times the number it means nearly 63 million have been infected by COVID-19 enough to produce antibodies.
Here’s where the opening of the economy becomes dicey.
The numbers quoted above are huge.
We don’t know how many would have had a relatively benign COVID-19 encounter. It is very much of concern that a vast majority may be asymptomatic or present symptoms not typically associated with the virus. Add to this that COVID-19 infections don’t necessarily produce the common symptoms we’ve read about, cough, persistent fever, shortness of breath, exhaustion, gastrointestinal problems, loss of sense of smell, and muscle aches and pains. And then there is the gestation period which can be anywhere from 5 to 14 days before symptoms present.
Infectious disease modelers earlier this month described COVID-19 as a silent spreader in three categories: asymptomatic, presymptomatic, and mildly symptomatic. Chinese data (whether you believe it or not) clearly shows that positive test results without symptoms have been found in many of the infected with a quarter of those in follow-up continuing to show no symptoms.
The outbreak in the state of Washington in a nursing home showed that 56% of the 82 residents plus the support staff who tested COVID-19 positive in March were asymptomatic. Two weeks later many of them displayed a full-blown presentation of the disease. And in the period between first testing, these residents became silent spreaders increasing the infection rate. Similar results have been reported in other localities where there have been outbreaks of the virus.
And then there is this disturbing fact. A person who is asymptomatic or mildly symptomatic sheds the virus in normal daily activity. Anyone exposed to that person is likely to become infected but how the disease presents itself in them may result in little to no symptoms. And a silent spreader can infect large numbers of people who become far more ill than the initial host of COVID-19.
Before we can hope to go back to the near-normal, or a new normal, the Santa Clara results, along with others doing serology studies, need to be peer-reviewed and assessed for their validity. Only then can governments and the public begin to have confidence in relaxing the restrictions with which we are currently living.
We don’t know whether the Santa Clara study is providing wildly exaggerated results because of those who were tested, or if it is underreporting the real rate of infection. And Santa Clara’s demographics may not be representative of the rest of the country.
So what do those demographics look like? According to the Santa Clara county records the population distribution divides as follows:
Santa Clara County had 1,870 COVID-19 cases as of two days ago.
Compare those demographics to Bergen County, New Jersey, a current COVID-19 hotspot.
Bergen County had 11,863 COVID-19 cases as of 22 hours ago.
Do the demographics make a difference? Undoubtedly they can. For example, and this isn’t seen in the above demographic breakdown, if a population has a high percentage of people over age 60, or if it has a large number of those who are immune-compromised, or saddled with chronic illnesses like diabetes, heart and lung disease, obesity, kidney disease, and others, it is likely that COVID-19 infection rates will be as high or higher than the percentages coming from the Santa Clara study.
The answer is NO!
On March 1, 2020, the CDC reported 75 cases in total in America. Today just six weeks later the CDC data shows more than 740,000 cases.
There remain areas of the country with little reported exposure. For example, as of this morning, the Northern Mariana Islands reported 13 cases, Guam has 136, and Alaska 314.
If the 50 to 85-times estimate applies, even in these areas of limited exposure, COVID-19 is being underreported.
Instead of 13 cases, we are talking about 650 to 1,100 for the Northern Marianas.
Instead of 136 cases, we are talking about between 6,800 and 11,560 on Guam.
And instead of 314 in Alaska, we are talking about 15,700 to 26,690 cases, the vast majority of them silent spreaders.
Without mass testing including the ramping up of serology studies to determine COVID-19 antibody levels in the general population, the silent spreaders will remain unidentified and an ongoing threat as the United States and other countries wrestle with how and when to get back to near-normal in a post-pandemic future.
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