Peak estimation

Tim Taylor

library(outbreaks)
library(incidence2)
library(i2extras) 

Bootstrapping and finding peaks

We provide functions to return the peak of the incidence data (grouped or ungrouped), bootstrap from the incidence data, and estimate confidence intervals around a peak.

bootstrap()

dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = gender)
#> 10 missing observations were removed.
bootstrap(x)
#> An incidence2 object: 67 x 3
#> 126 cases from 2013-02-19 to 2013-07-27
#> interval: 1 day
#> cumulative: FALSE
#> 
#>    date_index gender count
#>    <date>     <fct>  <int>
#>  1 2013-02-19 m          1
#>  2 2013-02-27 m          0
#>  3 2013-03-07 m          1
#>  4 2013-03-08 m          2
#>  5 2013-03-09 f          1
#>  6 2013-03-13 f          1
#>  7 2013-03-17 m          1
#>  8 2013-03-19 f          1
#>  9 2013-03-20 f          0
#> 10 2013-03-20 m          1
#> # … with 57 more rows

find_peak()

dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = gender)
#> 10 missing observations were removed.

# peaks across each group
find_peak(x)
#> # A tibble: 2 x 3
#>   gender date_index count
#>   <fct>  <date>     <int>
#> 1 f      2013-04-11     3
#> 2 m      2013-04-03     6

# peak without groupings
find_peak(regroup(x))
#> # A tibble: 1 x 2
#>   date_index count
#>   <date>     <int>
#> 1 2013-04-03     7

estimate_peak()

Note that the bootstrapping approach used for estimating the peak time makes the following assumptions:

dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = date_of_onset, groups = province)
#> 10 missing observations were removed.

# regrouping for overall peak
estimate_peak(regroup(x))
#> Estimating peaks from bootstrap samples:
#> 
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#> # A tibble: 1 x 6
#>   observed_peak observed_count bootstrap_peaks  lower_ci   median     upper_ci  
#>   <date>                 <int> <list>           <date>     <date>     <date>    
#> 1 2013-04-03                 7 <tibble[,2] [10… 2013-03-28 2013-04-06 2013-04-15

# across provinces and with progress bar suppressed
estimate_peak(x, progress = FALSE)
#> # A tibble: 13 x 7
#>    province  observed_peak observed_count bootstrap_peaks  lower_ci   median    
#>    <fct>     <date>                 <int> <list>           <date>     <date>    
#>  1 Anhui     2013-03-09                 1 <tibble[,2] [10… 2013-03-09 2013-03-28
#>  2 Beijing   2013-04-11                 1 <tibble[,2] [10… 2013-04-11 2013-04-11
#>  3 Fujian    2013-04-17                 1 <tibble[,2] [10… 2013-04-17 2013-04-21
#>  4 Guangdong 2013-07-27                 1 <tibble[,2] [10… 2013-07-27 2013-07-27
#>  5 Hebei     2013-07-10                 1 <tibble[,2] [10… 2013-07-10 2013-07-10
#>  6 Henan     2013-04-06                 1 <tibble[,2] [10… 2013-04-06 2013-04-06
#>  7 Hunan     2013-04-14                 1 <tibble[,2] [10… 2013-04-14 2013-04-14
#>  8 Jiangsu   2013-03-19                 2 <tibble[,2] [10… 2013-03-08 2013-03-21
#>  9 Jiangxi   2013-04-15                 1 <tibble[,2] [10… 2013-04-15 2013-04-19
#> 10 Shandong  2013-04-16                 1 <tibble[,2] [10… 2013-04-16 2013-04-16
#> 11 Shanghai  2013-04-01                 4 <tibble[,2] [10… 2013-03-17 2013-04-01
#> 12 Taiwan    2013-04-12                 1 <tibble[,2] [10… 2013-04-12 2013-04-12
#> 13 Zhejiang  2013-04-06                 5 <tibble[,2] [10… 2013-03-29 2013-04-10
#> # … with 1 more variable: upper_ci <date>