Coronavirus – forecasting numbers

A few people might have see the Johns Hopkins University Medical School chart on Covid-19 infection rates in different countries. This particular chart (they have produced many different outputs, some of them interactive world incidence models – see https://coronavirus.jhu.edu/map.html for more) usefully compares some various national growth rates with straight lines representing different periods over which the number of cases might double – 1 day, 2 days, 3 days and 7 days. It’s a kind of log chart to base 2.

Johns Hopkins University national trends, log base 2 chart

I’ve been beginning to simulate the outcomes for 2 input data items:

your chosen number of days (x) since the outbreak (defined at 100 cases on day zero to give a base of calculation);

your chosen rate of growth of cases, expressed by an assumed number of days for doubling the cases number (z), and then;

the output, the number of cases (y) on day x.

This spreadsheet allows you , in the last columns, to enter x and z in order to see the outcome, y.

Of course this is only an output model, it knows nothing about the veracity of assumptions – but the numbers (y) get VERY large for small doubling periods (z).

Try it. Only change the x and z numbers, please.

https://docs.google.com/spreadsheets/d/1kE_pNRlVaFBeY5DxknPgeK5wmXNeBuyslizpvJmoQDY/edit?usp=sharing

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