Written by Lindsay Morton In this third and final entry in our three-part series on academic credit, we turn our attention to…
This study was conducted by Constantinos Siettos from the Universita degli Studi di Napoli Federico II, Italy and colleagues. The authors used the Susceptible-Infected-Recovered-Dead (SIRD) model with data between January 11 and February 10, 2020 and estimated key epidemiological parameters up until February 29. With these parameters, they forecasted that between 80,000 and 160,000 people would be infected by February 29 – in fact, around 84,000 are known to have become infected in this time period.
Dr Siettos notes, “This is the first study based on a mathematical modelling approach that has provided relatively accurate three- week-ahead forecasts. Importantly, to the best of our knowledge this is the first study based on a mathematical modelling approach suggesting that the actual number of the infections in the total population is of the order of twenty times more than those reported, and that the mortality rate in the total population is about ~0.15% i.e. significantly less than reported 2-3%.Our findings imply that for the case of Hubei (with a 60m population), around 2%-3% of the total population in Hubei has been actually infected by coronavirus.”
Science leaders have recently requested that the publishing community make their content open and re-usable. PLOS already supports, and calls on others to support, COVID-19 research being shared such that it can be centrally text- and data-mined, and also versioned in places where it can be most conveniently discovered by those tackling this crisis in real time. We always push all PLOS content to PubMed Central. We are also forwarding all of our submissions to the World Health Organization for their database of publications on coronavirus disease (COVID-19).
We are compiling all our Coronavirus-related papers into a Collection as they publish. We encourage all researchers investigating pressing issues like disease outbreaks to also consider posting a Preprint. You can find the most recent preprints relating to the coronavirus outbreak – posted across bioRxiv and medRxiv on this search link.