Quantifying superspreading for COVID-19 using Poisson mixture distributions (preprint)

The number of secondary cases is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the number of secondary cases is often modelled using a negative binomial distribution. However, this may not be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the offspring mean and its overdispersion when the data generating distribution is different from the one used for inference. We find that overdispersion estimates may be biased when there is a substantial amount of heterogeneity, and that the use of other distributions besides the negative binomial should be considered. We revisit three previously analysed COVID-19 datasets and quantify the proportion of cases responsible for 80\% of transmission, p80%, while acknowledging the variation arising from the assumed offspring distribution. We find that the number of secondary cases for these datasets is better described by a Poisson-lognormal distribution.

Exposure to cough aerosols and development of pulmonary COVID-19 (preprint)

Half a year after the emergence of COVID-19, research is still going on to gain insight in the importance of different SARS-CoV-2 transmission routes and their impact on the clinical picture of COVID-19. Our findings suggest that coughing is not as important for transmission as initially anticipated and we discuss the potentially important role for loud conversation as a driver for transmission. DisclosureNone of the authors have any financial associations or other possible conflicts of interest to declare.