ears_t_reweighted {epitweetr} R Documentation

## algorithm for outbreak detection, extends the EARS algorithm

### Description

The simple 7 day running mean version of the Early Aberration Reporting System (EARS) algorithm is extended as follows:

• proper computation of the prediction interval

• downweighting of previous signals, similar to the approach by Farrington (1996)

### Usage

```ears_t_reweighted(
ts,
alpha = 0.025,
alpha_outlier = 0.05,
k_decay = 4,
no_historic = 7L,
same_weekday_baseline = FALSE
)
```

### Arguments

 `ts` A numeric vector containing the counts of the univariate time series to monitor. The last time point in ts is investigated `alpha` The alpha is used to compute the upper limit of the prediction interval: (1-alpha) * 100%, default: 0.025 `alpha_outlier` Residuals beyond 1-alpha_outlier quantile of the the t(n-k-1) distribution are downweighted, default: 0.05 `k_decay` Power k in the expression (r_star/r_threshold)^k determining the weight, default: 4 `no_historic` Number of previous values i.e -1, -2, ..., no_historic to include when computing baseline parameters, default: 7 `same_weekday_baseline` whether to calculate baseline using same weekdays or any day, default: FALSE

### Details

for algorithm details see package vignette.

### Value

A dataframe containing the monitored time point, the upper limit and whether a signal is detected or not.

### Author(s)

Michael HÃ¶hle <https://www.math.su.se/~hoehle>

### Examples

```if(FALSE){
library(epitweetr)
#Running the modifies version of the ears algorithm for a particular data series
ts <- c(150, 130, 122, 160, 155, 128, 144, 125, 300, 319, 289, 277, 500)
show(ears_t_reweighted(ts))
}
```

[Package epitweetr version 0.1.28 Index]