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We have used simple methods to analyse COVID-19 data and estimate Ro. See:
Ro (R0, R-Naught, R-Zero) is the number of people one person may on average infect if there is no isolation/quarantine.
You may be interested to see some of the methods the experts may use to analyse the data. e.g, The S-I-R model.
See the methods column in the table in the link below for other methods experts may use:
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In this post we shall look at ways experts analyse the spread of COVID-19.
My own mathematical techniques are simple but have produced some surprising results.
Analysis can quite often involve solving systems of differential equations (see below).
How Ro is estimated
‘A typical epidemiological model by which R0 is estimated is based on three factors: individual Susceptibility to the infection, the rate at which infections actually occur (Infectivity), and the rate of Removal of infection from the population, by either recovery or death. This is the so-called S-I-R model.The model involves the solution of simultaneous linear differential equations and was described as long ago as 1927 by W. O. Kermack and A. G. McKendrick. A version of their model is shown in Figure 3.’ See:
https://www.cebm.net/covid-19/when-will-it-be-over-an-introduction-to-viral-reproduction-numbers-r0-and-re/
Here is a set of differential equations for the CovidSim model. See:
report_for_moh_-_covid-19_pandemic_nz_final
There are other methods that can be used as well.
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Green Bottles, Alan's Ark & COVID Odyssey: Alan Grace's vir[tu]al journey 