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This model estimates a simple "SIR" model that has 4 population segments:
Susceptible: Not yet infected, but may be eventually.
Infected: Have the infection.
Recovered: Recovered from infection, not eligible to be re-infected.
Dead: No longer part of total population.
The possible transfers each period are:
S to I: Prob{ Susceptible person gets infected} = alpha * I/(S + I + R) + beta,
where beta is the probability of a spontaneous infection,
so total expected number transferred = alpha * S * I/( S + I + R) + beta * S.
If beta= 0 then Prob{a given susceptible gets infected} is proportional to total population that is infected.
S to D: total transferred is = gamma* S,
I to R: total transferred = gamma * I, so mean time infected = 1/gamma,
I to D: total transferred = delta * S,
R to D: total transferred = zeta * R.
The estimation problem is to find the alpha, beta, gamma, delta, and zeta that best fit the data.
You need What'sBest! from www.lindo.com to solve the estimation/fitting problem.