intro.md

Stochastic SEIRD model

The model is SEIRD model, where infected individuals can survive or die at different rates, with waning immunity. The full model specification is:

• $S$: susceptibles
• $E$: exposed, i.e. infected but not yet contagious
• $I_R$: infectious who will survive
• $I_D$: infectious who will die
• $R$: recovered
• $D$: dead

There are no birth of natural death processes in this model. Parameters are:

• $\beta$: rate of infection
• $\delta$: rate at which symptoms appear (i.e inverse of mean incubation period)
• $\gamma_R$: recovery rate
• $\gamma_D$: death rate
• $\mu$: case fatality ratio (proportion of cases who die)
• $\epsilon$: import rate of infected individuals (applies to $E$ and $I$)
• $\omega$: rate waning immunity

The model will be written as:

$$ S_{t+1} = S_t - \beta \frac{S_t (I_{R,t} + I_{D,t})}{N_t} + \omega R_t $$

$$ E_{t+1} = E_t + \beta \frac{S_t (I_{R,t} + I_{D,t})}{N_t} - \delta E_t + \epsilon $$

$$ I_{R,t+1} = I_{R,t} + \delta (1 - \mu) E_t - \gamma_R I_{R,t} + \epsilon $$

$$ I_{D,t+1} = I_{D,t} + \delta \mu E_t - \gamma_D I_{D,t} + \epsilon $$

$$ R_{t+1} = R_t + \gamma_R I_{R,t} - \omega R_t $$

$$ D_{t+1} = D_t + \gamma_D I_{D,t} $$