Super-spreaders of novel coronaviruses that cause SARS, MERS and COVID-19 : A systematic review (preprint)

ABSTRACT OBJECTIVE Most index cases with novel coronavirus infections transmit disease to just 1 or 2 other individuals, but some individuals ‘super-spread’ – they are infection sources for many secondary cases. Understanding common factors that super-spreaders may share could inform outbreak models. METHODS We conducted a comprehensive search in MEDLINE, Scopus and preprint servers to identify studies about persons who were each documented as transmitting SARS, MERS or COVID-19 to at least nine other persons. We extracted data from and applied quality assessment to eligible published scientific articles about super-spreaders to describe them demographically: by age, sex, location, occupation, activities, symptom severity, any underlying conditions and disease outcome. We included scientific reports published by mid June 2021. RESULTS The completeness of data reporting was often limited, which meant we could not identify traits such as patient age, sex, occupation, etc. Where demographic information was available, for these coronavirus diseases, the most typical super-spreader was a male age 40+. Most SARS or MERS super-spreaders were very symptomatic and died in hospital settings. In contrast, COVID-19 super-spreaders often had a very mild disease course and most COVID-19 super-spreading happened in community settings. CONCLUSION Although SARS and MERS super-spreaders were often symptomatic, middle- or older-age adults who had a high mortality rate, COVID-19 super-spreaders often had a mild disease course and were documented to be any adult age (from 18 to 91 years old). More outbreak reports should be published with anonymised but useful demographic information to improve understanding of super-spreading, super-spreaders, and the settings that super-spreading happens in.

Zoonotic Risk Technology Enters the Viral Emergence Toolkit (preprint)

In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions: What are the prerequisites, in terms of open data, equity, and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it, and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?