In our recent article, we explore two aspects that could explain the fast spreading of recent variants of the Corona virus: viral load and infectivity.
Initially, we wrote this article in view of the Delta variant but expanded it later to include also Omicron. Keep in mind that data on Omicron is still scarce. We have only limited information on viral loads. Only few preprint publications exist that contain such information and many of these not yet reviewed preprints do not represent the entire population.
So what are viral load and infectivity and why could they be relevant?
Terminology
1) Viral load: Once a cell got infected, it starts to produce copies of the virus and releases them into the surrounding liquid. These copies can be detected with different methods. The term viral load refers to the concentration of viral RNA measured in a part of our body. For SARS-CoV-2, it is usually a sample taken from the nose or throat. The virus concentration can differ a lot between different tissue types. A dream for aerosol researchers would be data on viral load (even better viral infective titer) in different phases of the infection in the airway liquid, especially the lung lining liquid in the bronchioles and near the vocal cord.
2) Infectivity: To infect somebody, viruses need to be able to enter cells and kick off virus production in that cell. Tests in cell cultures allow to determine the number of infective viruses in a sample, often quantified as „plaque forming units“ (PFU). Animal studies suggest that one single PFU is usually not sufficient to infect an organism. The number of PFU needed for an infection differs between pathogens. For SARS-CoV-2, the minimal infective dose seems to be in the range of 10 to 30 PFU. PFU are often reasonably well correlated with the number of RNA virus copies. However, they are far from identical. Not all viruses are correctly configured. Not all cells let viruses enter. Also the presence of molecules such as antibodies in the liquid around the virus will reduce the virus’ ability to enter cells. We also have indications that the ratio of PFU to detectable RNA-copies and the minimal infective dose differ between virus variants.
How can these two factors translate into more spreading?
First, remember that respiratory aerosols are formed in the terminal bronchioles and vocal cords through processes that release tiny droplets of liquid into the air. If this liquid contains viruses, they will become airborne as part of the droplets.
1) Higher viral load: What we observed with new variants is a shift in the distribution of the viral load. Early in the pandemic the distribution showed a large belly on the left of the distribution graph and this belly has now moved to the right side. This means for the aerosol emissions that for the wild-type, only few people emitted large quantities of viruses, while today this is quite common. Let’s take the wild-type: only high emitters and higher were able to generate virus concentrations in the air that result in virus doses sufficient to infect others. High emitters were about 1/10th of the infected population. Or in other words, one of these infected about 20 other people if you assume an R-value of 2. If you now shift the distribution of the viral load, you get more than half being high emitters. „Fun fact“: the R-value will go up even if a high emitter infects the same number of people simply because there are more of these people able to infect others.
In our paper, we simulate the effect of higher viral load. For Delta, we have already fairly reliable population data. For Omicron, small studies in athletes and vaccinated people show a viral load at least as extreme as Delta, while a British population trend suggest that it could be somewhere in the range from equal to up to 100 fold higher than Delta. We thus simulated three different models, one for Delta, one for a hypothetical Omicron distribution 10-times higher and one being 100-times higher, always with an upper limit at the highest viral loads ever reported. The following figure shows these distributions. It becomes evident that for all new variants, the increase in high to super-emitting individuals (the area above the curve) is impressive. Key to this increase is the shift of the „belly“ of the curves.
2) Higher Infectivity: With higher infectivity, a lower number of viruses is needed to infect somebody. In our articles we estimate the critical dose of virus-copies as a ballpark figure above which infections of humans become likely. Keep in mind that virus-copies are only a proxy for the number of infective viruses. For the wild-type (the one from Wuhan) the critical dose seemed to be about 500 virus-copies, for Delta 300 virus-copies, and for Omicron 100 virus-copies. These numbers match quite well the virus-doses calculated for super-spreading events with our indoor scenario simulator, which were often three to ten times above the critical dose. Such a matching is not evident because we derived these critical values totally independent from the spreading events.
This higher infectivity has consequences for the safety in indoor environments but it also changes how many people can infect others. Regarding indoor environments, you suddenly need larger rooms, better ventilation, and better masks to stay safe in presence of an emitter with equal emitter-strength. In our recent article, we examine a few scenarios (see table below) when the source is a „super-emitter“. Note that in our modelling, the number of virus-copies emitted by a super-emitter is the same for all variants. Thus, we compare situations with the same amount of viruses in the air. Already the increased infectivity alone would change many situations from ok with the wild-type to critical with Delta and very critical with Omicron. This also means that for Omicron, more people are able to create critical concentrations in a room compared to the wild-type and Delta even if the viral-load remained totally unchanged.
| Scenario / Interpretation | Dose (virus copies) | Wild-type | Delta variant | Omicron variant |
| 4 hours in small office (50 m3, 1 ACH), 5% talk | 479 | OK | Critical | Very critical |
| 4 hours in open space office (1,000 m3, 1 ACH), 5% talk | 24 | OK | OK | OK |
| 4 hours in open space call centre (1,000 m3, 1 ACH), 60% talk | 100 | OK | OK | Borderline |
| 2 hours in meeting room (100 m3, 3 ACH), 50% talk, 5% loud | 390 | OK | Critical | Very critical |
| 30 minutes in small shop / boutique (100 m3, 3 ACH), 20% talk | 451 | OK | Critical | Very critical |
| 2 hours in restaurant (500 m3, 1 ACH), 20% talk, emitter no mask | 153 | OK | OK | Critical |
| 2 hours in disco (300 m3, 3 ACH), 20% loud, 50% heavy dancing, receiver only FFP2 | 379 | OK | Critical | Very critical |
| 1 hour travel by train (57 m3, 7.1 ACH), 20% talk | 40 | OK | OK | OK |
| 1 hour travel by train (57 m3, 7.1 ACH), 20% talk, emitter no mask | 180 | OK | OK | Critical |
| 30-minute trolleybus ride (100 m3, 2 ACH), 20% talk, emitter no mask | 220 | OK | OK | Critical |
Taken together: For the rapid spread of a disease by aerosols, it is essential to have individuals that carry a high number of infective virus in in the respiratory liquid (or you will not have more than just a few viruses in the air). Once you have many viruses emitted, it will be sufficient to further spread the disease if you increase either (1) the proportion of people emitting high numbers of viruses, or (2) the infectivity. If you combine both factors, the spread can be expected to be extreme. For Omicron, this may well be the case.