dacca | R Documentation |

`dacca`

constructs a ‘pomp’ object containing census and cholera
mortality data from the Dacca district of the former British province of
Bengal over the years 1891 to 1940 together with a stochastic differential
equation transmission model.
The model is that of King et al. (2008).
The parameters are the MLE for the SIRS model with seasonal reservoir.

```
dacca(
gamma = 20.8,
eps = 19.1,
rho = 0,
delta = 0.02,
deltaI = 0.06,
clin = 1,
alpha = 1,
beta_trend = -0.00498,
logbeta = c(0.747, 6.38, -3.44, 4.23, 3.33, 4.55),
logomega = log(c(0.184, 0.0786, 0.0584, 0.00917, 0.000208, 0.0124)),
sd_beta = 3.13,
tau = 0.23,
S_0 = 0.621,
I_0 = 0.378,
Y_0 = 0,
R1_0 = 0.000843,
R2_0 = 0.000972,
R3_0 = 1.16e-07
)
```

`gamma` |
recovery rate |

`eps` |
rate of waning of immunity for severe infections |

`rho` |
rate of waning of immunity for inapparent infections |

`delta` |
baseline mortality rate |

`deltaI` |
cholera mortality rate |

`clin` |
fraction of infections that lead to severe infection |

`alpha` |
transmission function exponent |

`beta_trend` |
slope of secular trend in transmission |

`logbeta` |
seasonal transmission rates |

`logomega` |
seasonal environmental reservoir parameters |

`sd_beta` |
environmental noise intensity |

`tau` |
measurement error s.d. |

`S_0` |
initial susceptible fraction |

`I_0` |
initial fraction of population infected |

`Y_0` |
initial fraction of the population in the Y class |

`R1_0` , `R2_0` , `R3_0` |
initial fractions in the respective R classes |

Data are provided courtesy of Dr. Menno J. Bouma, London School of Tropical Medicine and Hygiene.

`dacca`

returns a ‘pomp’ object containing the model, data, and MLE
parameters, as estimated by King et al. (2008).

2008

More examples provided with pomp:
`blowflies`

,
`childhood_disease_data`

,
`compartmental_models`

,
`ebola`

,
`gompertz()`

,
`ou2()`

,
`pomp_examples`

,
`ricker()`

,
`rw2()`

,
`verhulst()`

More data sets provided with pomp:
`blowflies`

,
`bsflu`

,
`childhood_disease_data`

,
`ebola`

,
`parus`

```
# takes too long for R CMD check
po <- dacca()
plot(po)
## MLE:
coef(po)
plot(simulate(po))
```

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