Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

Raj Dandekar; George Barbastathis

Here, β, σ and γ are the exposure, infection and recovery rates, respectively, and are assumed to be constant in time. The total population N = S(t) + E(t) + I(t) + R(t) is seen to remain constant as well; that is, births and deaths are neglected. The recovered population is to be interpreted as those who can no longer infect others; so it also includes individuals deceased due to the infection. The possibility of recovered individuals to become reinfected is accounted for by SEIS models (Mukhopadhyay & Bhattacharyya 2008 ), but we do not use this model here, as the reinfection rate for Covid-19 survivors is considered to be negligible as of now. The simpler SIR model neglects exposure, assuming instead direct transition from susceptible to infected; it is described by three coupled nonlinear ordinary differential equations as