As the COVID-19 pandemic and its far-reaching implications continue to unfold globally and disrupt our normal way of life, scientists and researchers are racing to find an appropriate vaccine or therapeutic drug. This process involves understanding how the virus interacts with the host cell, the dynamics of the host immune response, and the eventual outcome.
One key area that provides vital data on tracking the infection is serology or antibody measurement. Population-based serology can help tell how the virus is spreading, the overall attack rate, and the prevalence of serological conversion. This, in turn, will elucidate how far along each community is towards the goal of herd immunity.
SARS-CoV-2 virus binding to ACE2 receptors on a human cell, the initial stage of COVID-19 infection. Illustration Credit: Kateryna Kon / Shutterstock
Such data is also valuable in modeling viral spread, which in turn is key to developing sensible policies to limit viral transmission and to revive economies simultaneously.
Individual antibody testing can reveal unsuspected past exposure, which could show the presence of the virus in a community much earlier than suspected based on current measures. Moreover, when combined with polymerase chain reaction (PCR) testing, it can significantly enhance the ability to detect both present and past infections. This is all the more important in that viral RNA shedding, which is necessary for PCR testing, occurs during a brief window of infectivity, and because of the differences in PCR sensitivity with the severity of the infection and type of biospecimen.
Since antibody production follows a standard pattern, such as IgM in the acute phase and IgG in the later phase, serology may help to detect the time of infection at least approximately. If neutralizing antibodies are found, they may signal immunity to reinfection.
However, all these serologic applications depend on knowing the pattern of seroconversion over time, the timeline, the first day on which antibodies become detectable, how the different antibodies wax and wane, and duration of antibody response (or antibody decay – the time after which antibodies are no longer detectable).
Currently, there are a large number of serologic studies on the initial immune response to COVID-19. However, they share a common feature – they are based on a remarkable diversity of resources, assay and sampling techniques, and patient groups. For instance, 8 antibody tests, 10 antigens, and 9 antibody levels, besides a study duration ranging from one day to many weeks.
This heterogeneity of data makes it challenging to interpret it into a single picture of antibody responses to and viral RNA shedding in COVID-19. For this type of integration, specialized statistical techniques must be developed to discover the following points of importance:
- Are the assays and antigens in current use equivalent concerning the detection of RNA shedding and antibody production?
- Is the severity of disease and antibody pattern linked?
Now, a new study by researchers at the University of California Los Angeles and published on the preprint server medRxix* focuses on quantified IgM and IgG kinetics, as well as RNA shedding, during and after COVID-19 infection, for up to 60 days after the onset of symptoms. The researchers used 3,200 data points from 500 individuals presenting with a broad spectrum of symptoms, to generate a quantitative synthesis of data.
When Does Seroconversion Occur?
The researchers used 270 data points from 99 people for IgG seroconversion, and 240 from 71 individuals for IgM. They concluded that the mean IgG and IgM seroconversion time is about 13 days after the onset of symptoms in both cases, with ELISA-NP. However, it can occur at day zero, which shows seroconversion can occur before symptoms set in.
The type of assay is not a significant source of error. Seroconversion time variation is marked at about six days for both.
The investigators also found that there is no significant link between disease severity and seroconversion for IgM or IgG. The mean seroconversion time for IgG is 13 days or 16 days for mild to moderate and severe cases, respectively. In the case of IgM, it is 12 and 13 days, respectively.
How probable is it that serologic testing will detect antibodies?
Taking a sample size of approximately 8,000 cases each for IgG and IgM, the researchers found that the probability of detecting the IgG antibody increases to a maximum at around 25-27 days from the earliest symptoms to 98-100%. It remains at this level for up to 60 days, which is the most prolonged duration of this study.
For IgM, the peak is at about 90% at around 25 days from the onset of symptoms but wanes rapidly after that to about 50% probability at 60 days. For neutralizing antibodies, the peak is at about 100% and is rapidly achieved, and remains steady up to at least 60 days.
The probability of detection is similar across the spectrum of clinical presentations. If these antibodies are protective, herd immunity may be attained even by mild or asymptomatic infection.
How probable is RNA detection?
Using sample sizes for 7,400 and 1,700 cases with upper and lower respiratory samples, and 1,200 for fecal samples, the probability of viral DNA detection is 80% to 100% at the onset of symptoms. The highest chances of detection are in lower respiratory tract samples.
The detection probability wanes rapidly, varying with the specimen type. The fastest rate of decline is with the upper respiratory tract samples. At about 30 days from the onset of symptoms, the probability is almost zero.
What does the antibody pattern look like over time?
The antibody changes remain constant across different assays and types. The peak antibody level is around 16-17 days from symptom onset, for both IgG and IgM, by ELISA. This is mostly unaffected by disease severity.
The authors say, “These results provide critical reference information for serological surveys, transmission models, and herd immunity assessments.”
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.