library(outbreaks)
library(incidence2)
library(i2extras) 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 rowsfind_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 7estimate_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>