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K. Johnson:
"Disease "Momentum": Estimating the Reproduction Number Under Superspreading";
Vortrag: Forum Junge Statistik Seminar, Universität Wien (online) (eingeladen); 29.11.2021.

A primary quantity of interest in the study of infectious diseases is
the average number of new infections that an infected person produces. This so-called
reproduction number has significant implications for the disease progression. There has been
increasing literature suggesting that superspreading, the significant variability in number of
new infections caused by individuals, plays an important role in the spread of
SARS-CoV-2. In this paper, we consider the effect that such superspreading has on the estimation of
the reproduction number and subsequent estimates of future cases. Accordingly, we employ
a simple extension to models currently used in the literature to estimate the
reproduction number and present a case-study of the progression of COVID-19 in Austria. Our
models demonstrate that the estimation uncertainty of the reproduction number increases
with superspreading and that this improves the performance of prediction intervals. Of
independent interest is the derivation of a transparent formula that connects the extent of
superspreading to the width of credible intervals for the reproduction number. This serves as
a valuable heuristic for understanding the uncertainty surrounding diseases with superspreading.

COVID-19, reproduction number, overdispersion, superspreading