Pandemic Science Maps

Virus Superspreading

Mila Nezdoimyshapko

The review was prepared by Mila Nezdoimyshapko, Alla Loseva

A closed space with poor ventilation, a large crowd. People talk on top of the general noise at the wedding. They sing at the choir practice or church service. They breathe rapidly in a Zumba class. And one of them feels unwell – or doesn’t yet, but will in a few days. How many people will they infect?

Answering this question is very different from calculating how many people a typical virus carrier infects. On average, the carrier of the novel coronavirus (absent restrictive measures) infects three people. But each of the situations described above is a real case from the COVID-19 pandemic, where 40–80 people were infected directly by the carrier. This is called a superspreading event – when one person transmits a virus to a disproportionately large number of people.

Although the average indicator, the basic reproduction number (R0), is widely used in research, the shape of the epidemic mainly depends on such atypical situations. Therefore, along with R0, a dispersion parameter k is used to describe how the virus spreads. It reflects the difference in how many people are infected by the virus carriers.

If all the carriers of the virus are lined up according to how many people they have infected, then at the beginning of the row there will be superspreaders, and at the end – those who have not infected anyone. The parameter k will indicate where the group of most active spreaders ends and the group of less active ones begins. When almost all infections originated from a small group of superspreaders, and most carriers did not spread the virus, k is close to zero. When all the carriers have infected approximately the same number of people, so there are no superspreaders, k will rise to 10 and above.

For the Spanish flu pandemic, this parameter was about one, which corresponds to a more uniform spread of the virus. But for the coronavirus epidemics, k was close to zero, reflecting the notable role of superspreading (0.16 for SARS, 0.25 for MERS). This may be due to the airborne and aerosol transmission of coronaviruses, as these viruses are emitted even with loud speech and are suspended in the air long enough to be inhaled by many people.

A COVID-19 preprint gives an even lower estimate of k at 0.1, though earlier studies suggested that the role of superspreading in this pandemic is not higher than for other coronavirus epidemics. Moreover, the vast majority of carriers do not infect anyone.

Virus transmission depends on how carriers behave in different situations. If a person is self-isolating with pronounced disease symptoms, they will most likely not infect others – not only because they do not meet anyone, but also because the longer it is after the onset of symptoms, the less one emits the virus (Bullard et al. 2020; Cheng et al. 2020). But a few days before or after the onset of symptoms, carriers are the most infectious (He et al. 2020). Therefore, if during an asymptomatic period a person spent more than 10 minutes in crowded closed spaces and was not wearing a mask, it is very likely that they have already become a superspreader. Researchers believe that diminishing the likelihood of each virus carrier to appear in such situations, as well as tracking the close contacts of the carriers, can significantly reduce the number of virus transmission (Liu, Eggo, and Kucharski 2020).

To learn more about superspreading, we have performed a systematic search in the scientific literature database Scopus and have built a map of publications based on their reference lists (Figure 1). Proximity in this map and belonging to the same cluster mean that the papers cite the same publications, therefore the papers are likely to consider similar issues. The map is built using VOSviewer software.

Publications on superspreading are split into six clusters:

Figure 1. Bibliographic coupling map on the topic of virus superspreading
Nodes are colored according to the automatically identified clusters. Links indicate overlaps in reference lists between two publications. Proximity in the map and belonging to the same cluster both reflect the higher probability that the papers are devoted to a related subject matter. Node sizes correspond to the citation count of the paper according to Scopus. Only the connected items are included in the map (N = 302). Click on the figure to see the full resolution (opens in a new tab)

The most important paper in the whole map shows that the number of people infected by a virus carrier is very unevenly distributed (Lloyd-Smith et al. 2005). The authors test this hypothesis on the data obtained by tracking contacts during the epidemics of eight different viruses, and it turns out that this is a common feature of all diseases transmitted between people. So, for SARS, in more than 80% of transmission cases, the virus was spread by 20% of carriers. Researchers note that targeted control measures (isolation of virus carriers and tracking their contacts) in such a situation are more effective than general quarantine. The authors also propose a mathematical definition of superspreading to predict the frequency of such cases with known R0 and k.

Light blue cluster: 2003 SARS epidemic

The 2003 SARS epidemic has started in a Hong Kong hotel, where tourists got infected from a sick traveler and spread the virus to different countries. In the article by Shen et al. (2004), SARS superspreading is defined as the transmission of the virus to at least eight people, and researchers at that time discovered four such cases in China.

At the same time, according to statistics, on average, the virus was transmitted to 2.7 people in the first stages of the epidemic and a smaller number afterwards (Riley et al. 2003). Nosocomial transmission of the virus was quite common, and if not for this, the epidemic would have taken on a much smaller scale (Small et al. 2005, Yu et al. 2007).

By Li et al.’s estimations (2004), 71% of infections in Hong Kong and 75% in Singapore occurred during superspreading events. They happened partly because patients went to the doctor no earlier than four days after the onset of symptoms. In this regard, the authors emphasize the importance of early diagnosis and isolation of the infected.

Yellow cluster: 2015 MERS epidemic 

Partly, the superspreading of the MERS coronavirus is also associated with nosocomial infections, since carriers of the virus went to different hospitals or were transferred from one hospital to another. For example, Kang et al. (2017) found no other clinical differences between superspreaders and other carriers of the virus than that superspreaders spent more time non-isolated in a hospital. Besides this, behavioral factors are also mentioned: many people visited the patients, and virus carriers entered large crowds and did not avoid physical contact during greetings (Al-Tawfiq and Memish 2016).

The study by Chowell et al. (2015) compares virus transmission in the case of MERS and SARS. Most cases of MERS transmission occurred because people were in the same hospital as the first infected person. In the case of SARS, more cases were concentrated among healthcare providers. These differences indicate the importance of special patient management to decrease the transmission of the virus.

Navy cluster: ecological and spatial factors

Paull et al. (2012) note that the difference in how many people the carrier of the virus infects is closely related to the differences in the environment, such as population density, temperature and humidity, ecosystem fragmentation, as well as the interaction of the environment and the genetic characteristics of the population. Hawley and Altizer (2011) urge studying the immune system and life history of superspreaders.

In the cluster, virus superspreading during human mobility (Bossak and Welford 2010) and animal migration (Craft et al. 2011) is also discussed.

Purple cluster: social networks

When epidemiologists model society as a network of contacts, they can use three main options for its structure. It can be a random network where everyone has an equal chance of communicating with everyone – this is a simple but not very realistic configuration. Or a scale-free network, where most nodes have few connections and only a small number of so-called network hubs have many contacts. Such a structure, for example, is used in modeling the spread of sexually transmitted diseases, or computer viruses. The third option is a “small-world” network. In this one, people are mainly connected at the local level, inside groups such as school, family, store visitors, metro passengers – but some connections are long-distance ones if a person communicates with several groups.

Depending on which structure fits reality best, proposed measures to identify superspreaders may vary. For example, Liu et al. (2015) discuss various centrality measures in the network, in particular, such a centrality analysis method as k-shell decomposition. In scale-free networks, the most central positions are occupied by hubs, people with a large number of social contacts – it is they who should be proactively tested for the virus and isolated at the first suspicion of illness.

But Masuda, Konno, and Aihara (2004) point out that in the case of SARS, the contact structure was resembling the “small world” model. Superspreaders were not necessarily too socially active. Rather, they led a normal life, but at the time when they were especially contagious, they were connected to groups susceptible to the virus.

Small, Tse, and Walker (2006) share this approach and model containment measures in these settings. As the main factor in the spread of the virus, they consider the period of more than six days, when the carriers of the virus spread the infection (for example, inside the hospital). Researchers suggest controlling the epidemic either by restricting far-reaching connections through partial quarantine or by quickly hospitalizing and isolating people who show symptoms of the disease.

Blue cluster: tuberculosis spreading

The publications of this cluster are devoted to the transmission of bacterial infection, Mycobacterium tuberculosis. It is more difficult to become infected with tuberculosis than coronaviruses, as it requires prolonged or regular contact.

Walker et al. (2013) suggest a new method to reconstruct the transmission pattern of this infection through Mycobacterium genome analysis. Previously, strains of tuberculosis were detected through MIRU-VNTR genotyping, and if the patients had different strains, this excluded the possibility that one of them transmitted the infection to another. However, if the strain was the same, it was difficult to understand how the infection was transmitted because patients often could not report such data. Molecular typing allowed only to indicate the presence or absence of superspreaders, based on the extent to which groups of patients with the same type of bacterium varied in size (Ypma et al. 2013).

The alternative method, whole-genome sequencing, identifies mutations in the genes and thus allows one to establish the pathways of infection. Since mutations most often accumulate, the dynamics of transmission can be traced between patients with different sets of mutations. In this way, it is possible to identify superspreaders that occupy central positions in the structure of the phylogenetic tree. Moreover, transmission from one spreader to many is characterized by a low diversity of Mycobacterium genomes (Comin et al. 2020).

Please proceed to page 2 to see general reviews on superspreading and the description of our data.

General reviews

Data source: Scopus bibliographic database. The search was made by titles, abstracts and keywords of publications using the term superspreading. The search resulted in 474 publications, excluding books.
Search query:
TITLE-ABS-KEY ( super-spread*  OR  superspread* )