aeddo {aeddo} | R Documentation |

This function performs automated an early detection of disease outbreaks, (aeddo), on a time series data set. It utilizes hierarchical models in an innovative manner to infer one-step ahead random effects. In turn, these random effects are used directly to characterize an outbreak.

```
aeddo(
data = data.frame(),
formula = formula(),
k = integer(),
sig_level = 0.95,
exclude_past_outbreaks = TRUE,
init_theta = numeric(),
lower = numeric(),
upper = numeric(),
method = "BFGS"
)
```

`data` |
A tibble containing the time series data, including columns 'y' for observed values,'n' for population size, and other covariates of interest. |

`formula` |
A model formula for the fixed effects in the hierarchical model to fit to the data. |

`k` |
An integer specifying the rolling window size employed for parameter estimation. |

`sig_level` |
The quantile from the random effects distribution used for defining the for outbreak detection threshold, a numeric value between 0 and 1. |

`exclude_past_outbreaks` |
logical value indicating whether past outbreak related observations should be excluded from future parameter estimation. |

`init_theta` |
Initial values for model parameters in optimization. |

`lower` |
Lower bounds for optimization parameters. |

`upper` |
Upper bounds for optimization parameters. |

`method` |
The optimization method to use, either "BFGS" (default) or "L-BFGS-B". |

A tibble-like 'aedseo' object containing:

'window_data': A list of tibble, each representing the data for this windowed parameter estimation.

'reference_data': A list of tibble, each representing the data for the reference time point.

'phi': The dispersion parameter.

'lambda': The estimated outbreak intensity.

'u': The one-step ahead random effect.

'u_probability': The probability of observing the one-step ahead random effect.

'outbreak_alarm': Logical. Indicates if an outbreak is detected.

```
# Create an example aedseo_tsd object
aeddo_data <- data.frame(
time = as.Date(c(
"2023-01-01",
"2023-01-02",
"2023-01-03",
"2023-01-04",
"2023-01-05",
"2023-01-06"
)),
y = c(100, 120, 180, 110, 130, 140),
n = 1
)
# Supply a model formula
fixed_effects_formula <- y ~ 1
# Choose a size for the rolling window
k <- 2
# ... and quantile for the threshold
sig_level <- 0.9
# Employ the algorithm
aeddo_results <- aeddo(
data = aeddo_data,
formula = fixed_effects_formula,
k = k,
sig_level = sig_level,
exclude_past_outbreaks = TRUE,
init_theta = c(1, 0),
lower = c(-Inf, 1e-6),
upper = c(Inf, 1e2),
method = "L-BFGS-B"
)
# Print the results
print(aeddo_results)
```

[Package *aeddo* version 0.1.1 Index]