Count regression models for COVID-19

At the end of 2019, the current novel coronavirus emerged as a severe acute respiratory disease that has now become a worldwide pandemic. Future generations will look back on this difficult period and see how our society as a whole united and rose to this challenge. Many reports have suggested that this new virus is becoming comparable to the Spanish flu pandemic of 1918. We provide a statistical study on the modelling and analysis of the daily incidence of COVID-19 in eighteen countries around the world. In particular, we investigate whether it is possible to fit count regression models to the number of daily new cases of COVID-19 in various countries and make short term predictions of these numbers. The results suggest that the biggest advantage of these methods is that they are simplistic and straightforward allowing us to obtain preliminary results and an overall picture of the trends in the daily confirmed cases of COVID-19 around the world. The best fitting count regression model for modelling the number of new daily COVID-19 cases of all countries analysed was shown to be a negative binomial distribution with log link function. Whilst the results cannot solely be used to determine and influence policy decisions, they provide an alternative to more specialised epidemiological models and can help to support or contradict results obtained from other analysis. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The novel coronavirus outbreak in late 2019 has become a global pandemic, leading to a statistical study on the daily incidence of COVID-19 in eighteen countries worldwide. The best fitting count regression model for new daily COVID-19 cases of all countries analysed was found to be a negative binomial distribution with log link function, providing preliminary results and an overall picture of the global pandemic trends.