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expert reaction to unpublished preprint paper on modelling deaths from COVID-19 after implementing social distancing measures in different countries

A preprint, posted to MedRxiv, has modelled the possible deaths from COVID-19 after implementing social distancing measures in different countries.


Prof Keith Neal, Emeritus Professor in the Epidemiology of Infectious Diseases, University of Nottingham, said:

“Their predictions are too low for South Korea, they have already reported 139 deaths and 8 yesterday, the lower bound of total deaths they suggest is 140 and only a maximum of 2 deaths per day.  Progress over the next few days will show how accurate their modelling is.”


Prof Jonathan Ball, Professor of Molecular Virology, University of Nottingham, said:

“This is yet another paper predicting likely outcomes of the coronavirus outbreak that is reliant on modelling. Unfortunately, such models are based on major assumptions and often these assumptions are wrong. Until we do on the ground research – go out into the community and test exactly how many people have been infected with this virus –  we will have no idea of the rate of severe disease or what proportion of infected people die. Whilst such models can give an insight into what might happen, they can’t tell us what will happen, and the sooner we realise this the better.”


Dr Simon Clarke, Associate Professor in Cellular Microbiology, University of Reading, said:

“The modelling described in this paper assumes that the coronavirus causing COVID19 spreads identically amongst the populations of different countries, in the absence of social distancing.  With that caveat in mind, the data are highly suggestive that early adoption of control measures has the maximum impact on preventing viral spread.  However, human nature and public resistance to social distancing always need to be born in mind when considering the optimum timing of such rules.”


“An international comparison of the second derivative of COVID-19 deaths after implementation of social distancing measures” by W. T. Pike et al. is an unpublished preprint on MedRxiv



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