Coronavirus- Growth bounds and a model of viral dynamics that agrees with the data.
A In April 2020 we analysed all causes mortality figures with their historic levels first in a number of European countries and then in Brazil.
Based on our calculations of average expected deaths conditional on exceeding previous records, we concluded that deaths observed should not have been unexpected given the data history.
Our new phenomenological model for epidemics makes accurate predictions of the progress of Covid-19 waves available for anyone, using off the shelf software for non-linear regression.
Moreover, our powerful statistical technology gives excellent early stage bounds in each emerging outbreak.
Predicting the Course of Covid-19 and Other Epidemic and Endemic Diseases -29 December 2021
We present a new model for the accurate prediction of Covid-19 and other epidemics. In particular our projections of healthcare requirements have been reliable enough to allow planning for: hospital admissions, intensive care admissions, ventilator usage, peak loads and duration.
Predicting the Unpredictable in New York City -14 April 2022
The only possible test of efficacy of a lockdown is whether it caused a decline in infections. If any interventions occurred after infections had already peaked they cannot have been the cause of the decline. New York City infections peaked no later than 18 March 2020–two days before Governor Cuomo announced that the “New York State on PAUSE” executive order would go into effect on 22 March 2020.
Predicting the Unpredictable -5 April 2022
A non technical version of our work. Our model exemplifies the power of mathematics to make forecasts, predicting the course of Covid-19 waves with enough accuracy to manage healthcare demands. It also describes how the ‘lockdowns’, from Wuhan onwards, all came after infections had already peaked.
Omega Analysis in Health: Coronavirus Introduction to Growth Bounds and Epidemiological Model -27 February 2022.
This is a brief introduction to the use of our model in navigating Covid-19 waves. It is applied to the Omicron waves in England and in Ontario, where flawed estimates of its severity caused significant planning problems.
In addition to illustrating the way in which the Gompertz Function makes accurate predictions of healthcare demands we also show how our Extreme Value Statistics can be used to remove uncertainties in the transition between the linear growth and Gompertz Function growth phases.
Colloquium on Public Health -25 January 2022
The Fields Institute- Colloquium on Mathematics for Public Health- Canada 25 Jan 2022
We discuss the Complete Gompertz Model, from the epidemic phase to the endemic phase, emphasis in hospitalisations for Ontario, Canada, in Nov and Dec 2021.
Accidents Waiting to Happen- Coronavirus Edition -28 April 2020, updated 15 May 2020
There is tremendous variability in historic records of deaths from all causes in Denmark, the Netherlands, Sweden and the U.K. Many recent analyses have compared weekly deaths recorded in March and April 2020 withy a 5 year average for the same periods. These comparisons are meaningless without an understanding of the distribution of above average data points. Our standard statistical tools applied to samples of deaths above the average rate shows the none of the events so far should have been unexpected given the data history.
Accidents Waiting to Happen- Coronavirus Brazil Edition -26 May 2020
Analysis of weekly deaths from all causes in Brazil puts into context the headline number of deaths attributed to Covid-19. It appears that corona virus deaths are consistent with those of the above average influenza-pneumonia season which should be expected.