An open-access database of infectious disease transmission trees to explore superspreader epidemiology

Historically, emerging and reemerging infectious diseases have caused
large, deadly, and expensive multinational outbreaks. Often outbreak
investigations aim to identify who infected whom by reconstructing the
outbreak transmission tree, which visualizes transmission between
individuals as a network with nodes representing individuals and branches
representing transmission from person to person. We compiled a database,
called OutbreakTrees, of 382 published, standardized transmission trees
consisting of 16 directly transmitted diseases ranging in size from 2 to
286 cases. For each tree and disease, we calculated several key
statistics, such as tree size, average number of secondary infections, the
dispersion parameter, and the proportion of cases considered
superspreaders, and examined how these statistics varied over the course
of each outbreak and under different assumptions about the completeness of
outbreak investigations. We demonstrated the potential utility of the
database through 2 short analyses addressing questions about superspreader
epidemiology for a variety of diseases, including Coronavirus Disease 2019
(COVID-19). First, we found that our transmission trees were consistent
with theory predicting that intermediate dispersion parameters give rise
to the highest proportion of cases causing superspreading events.
Additionally, we investigated patterns in how superspreaders are infected.
Across trees with more than 1 superspreader, we found preliminary support
for the theory that superspreaders generate other superspreaders. In sum,
our findings put the role of superspreading in COVID-19 transmission in
perspective with that of other diseases and suggest an approach to further
research regarding the generation of superspreaders. These data have been
made openly available to encourage reuse and further scientific inquiry.