Chapter 11 of the textbook, “Epidemiology”, highlights the SIR Model. This acronym takes into consideration the people who are susceptible(s), infectious(i) and recovered(r) in a population in their equation.
\[\frac{ds}{dt} = -\beta si\] \[\frac{ds}{dt} = -\beta si - \gamma i\] \[\frac{ds}{dt} = \gamma i\] The SIR mmodel is a compartment model because it devides the population into discrete categories, ie compartments, and therefore describes the trasition of the population from one compartment to another. Compartments are also called stocksand transitions between them are called flows.
When it comes to COVID-19 the total population should be comparmented into eight stages of diesease, : S, susceptible (uninfected); I, infected (asymptomatic or pauci-symptomatic infected, undetected); D, diagnosed (asymptomatic infected, detected); A, ailing (symptomatic infected, undetected); R, recognized (symptomatic infected, detected); T, threatened (infected with life-threatening symptoms, detected); H, healed (recovered); E, extinct (dead).
For me math is second, computer science comes first. When it all comes together it is the most satisfying thing about these assignments.
def alpha_func(t):
intercept = 0.02
slope = -0.00022
y = 1970
return intercept + slope * (t-y)
ts = linrange(1960, 2021)
test_model = TimeSeries(alpha_func(ts), ts)