Does respiratory co-infection facilitate dispersal of SARS-CoV-2? investigation of a super-spreading event in an open-space office

Background

Super-spreaders are individuals infecting disproportionately large numbers of contacts. They probably play a crucial role in the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe a super-spreading event within a team working in an open-space office and investigate factors potentially having facilitated SARS-CoV-2 transmission.

Methods

In this retrospective cohort study, semi-structured telephone interviews with all team members were carried out to identify symptoms, contacts, and adherence to basic hygiene measures. During site visits, we gathered information about workplace and seating arrangements. The secondary attack rate in office and households was calculated. Potential respiratory viral co-infections were assessed by multiplex PCR. SARS-CoV-2 whole-genome sequencing was performed using a tiled-amplicon sequencing approach.

Results

Of 13 team members, 11 fell ill with Coronavirus disease 2019 (COVID-19). Due to the sequence of events and full genome sequence data, one person was considered the index case for this outbreak, directly infecting 67 to 83% of the teammates. All team members reported repetitive close contacts among themselves during joint computer work, team meetings and a “Happy Birthday” serenade. Two individuals shared nuts and dates. The arrangement of the office and meeting rooms precluded sufficient adherence to physical distancing. The index case and a further individual were diagnosed with an adenovirus serotype 4 co-infection.

Conclusion

We identified several environmental and behavioral factors that probably have facilitated the transmission of SARS-CoV-2. The relevance of the adenovirus co-infection remains unclear and merits further investigation.

Inferring super-spreading from transmission clusters of COVID-19 in Hong Kong, Japan, and Singapore

Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine whether an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R₀) and the dispersion factor (k) from empirical data on clusters of epidemiologically linked coronavirus disease 2019 (COVID-19) cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.

COVID-19 Super-spreaders: Definitional Quandaries and Implications

Uncertainty around the role ‘super-spreaders’ play in the transmission and escalation of infectious disease is compounded by its broad and vague definition. It is a term that has been much used in relation to COVID-19, particularly in social media. On its widest definition, it refers to a propensity to infect a larger than average number of people. Given the biological, behavioural and environmental variables relevant to infectivity, this might be pertinent to almost any infected individual who is not physically isolated from others. Nor is the term confined to individuals with a propensity to spread infectious disease: it can potentially be used to describe events, policies or settings. This article explores the use of the term and considers circumstances in which the wide definition can be problematic. One problem is that it can lead to undeserved apportionment of moral blame to alleged super-spreaders. Another is that it can detract from scientific investigation of the heterogeneity of COVID-19 transmission. The author calls for a clearer epidemiological definition.

The role of super-spreading events in Mycobacterium tuberculosis transmission: evidence from contact tracing

BACKGROUND

In current epidemiology of tuberculosis (TB), heterogeneity in infectiousness among TB patients is a challenge, which is not well studied. We aimed to quantify this heterogeneity and the presence of "super-spreading" events that can assist in designing optimal public health interventions.

METHODS

TB epidemiologic investigation data notified between 1 January 2005 and 31 December 2015 from Victoria, Australia were used to quantify TB patients' heterogeneity in infectiousness and super-spreading events. We fitted a negative binomial offspring distribution (NBD) for the number of secondary infections and secondary active TB disease each TB patient produced. The dispersion parameter, k, of the NBD measures the level of heterogeneity, where low values of k (e.g. k < 1) indicate over-dispersion. Super-spreading was defined as patients causing as many or more secondary infections as the 99th centile of an equivalent homogeneous distribution. Contact infection was determined based on a tuberculin skin test (TST) result of ≥10 mm. A NBD model was fitted to identify index characteristics that were associated with the number of contacts infected and risk ratios (RRs) were used to quantify the strength of this association.

RESULTS

There were 4190 (2312 pulmonary and 1878 extrapulmonary) index TB patients and 18,030 contacts. A total of 15,522 contacts were tested with TST, of whom 3213 had a result of ≥10 mm. The dispersion parameter, k for secondary infections was estimated at 0.16 (95%CI 0.14-0.17) and there were 414 (9.9%) super-spreading events. From the 3213 secondary infections, 2415 (75.2%) were due to super-spreading events. There were 226 contacts who developed active TB disease and a higher level of heterogeneity was found for this outcome than for secondary infection, with k estimated at 0.036 (95%CI 0.025-0.046). In regression analyses, we found that infectiousness was greater among index patients found by clinical presentation and those with bacteriological confirmation.

CONCLUSION

TB transmission is highly over dispersed and super-spreading events are responsible for a substantial majority of secondary infections. Heterogeneity of transmission and super-spreading are critical issues to consider in the design of interventions and models of TB transmission dynamics.

Characterizing super-spreading in microblog: An epidemic-based information propagation model

As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called "super-spreading events". In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.