The COVID-19 intervention problem: exponential growth magnifies errors

From Dylan H. Morris, Fernando W. Rossine blog post here

During the initial epidemic spread of a novel zoonotic disease like COVID-19 – a disease to which almost no-one in the population is immune – infectious cases increase near-exponentially.

Exponential growth magnifies implementation errors. If an intervention that is too little or too late, there may not be time to course-correct before the epidemic spikes to a large peak number of infectious individuals. If there is an insufficiently controlled epidemic peak, hospitals and emergency services can be overwhelmed, case-fatality rates rise, and the overall epidemic morbidity and mortality rise dramatically. Social and economic costs are also magnified.

Since verbal arguments do not always stand up to more rigorous analysis, we have developed a simple mathematical model to encode the basic problem of an optimal short intervention aimed and reducing the highest peak in an epidemic.

This blog post

We aim here at a semi-technical presentation of our work. We wish to provide sufficient detail – and code – to allow researchers to assess the work. But as this is a blog post, we will try to provide enough guidance for the non-specialist reader to make things accessible. That said, this is not intended as a tutorial on epidemiological modeling, and some sections (in particular the specification and analysis of the mathematical model) are dense and technical. A number of particularly technical points are deferred to an appendix at the end of the post... continues at Morris blog...more at link  here