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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")
bootstrap(x)
#> # incidence: 67 x 4
#> # count vars: date_of_onset
#> # groups: gender
#> date_index gender count_variable count
#> * <date> <fct> <chr> <int>
#> 1 2013-02-19 m date_of_onset 3
#> 2 2013-02-27 m date_of_onset 1
#> 3 2013-03-07 m date_of_onset 2
#> 4 2013-03-08 m date_of_onset 1
#> 5 2013-03-09 f date_of_onset 3
#> 6 2013-03-13 f date_of_onset 1
#> 7 2013-03-17 m date_of_onset 2
#> 8 2013-03-19 f date_of_onset 3
#> 9 2013-03-20 f date_of_onset 2
#> 10 2013-03-20 m date_of_onset 4
#> # … with 57 more rowsfind_peak()dat <- fluH7N9_china_2013
x <- incidence(dat, date_index = "date_of_onset", groups = "gender")
# peaks across each group
find_peak(x)
#> # incidence: 2 x 4
#> # count vars: date_of_onset
#> # groups: gender
#> date_index gender count_variable count
#> * <date> <fct> <chr> <int>
#> 1 2013-04-11 f date_of_onset 3
#> 2 2013-04-03 m date_of_onset 6
# peak without groupings
find_peak(regroup(x))
#> # incidence: 1 x 3
#> # count vars: date_of_onset
#> date_index count_variable count
#> * <date> <chr> <int>
#> 1 2013-04-03 date_of_onset 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")
# regrouping for overall peak (we suspend progress bar for markdown)
estimate_peak(regroup(x), progress = FALSE)
#> # A data frame: 1 × 7
#> count_variable observed_peak observ…¹ boots…² lower_ci median upper_ci
#> <chr> <date> <int> <list> <date> <date> <date>
#> 1 date_of_onset 2013-04-03 7 <df> 2013-03-29 2013-04-08 2013-04-17
#> # … with abbreviated variable names ¹observed_count, ²bootstrap_peaks
# across provinces
estimate_peak(x, progress = FALSE)
#> # A data frame: 13 × 8
#> province count…¹ observed…² obser…³ boots…⁴ lower_ci median upper_ci
#> <fct> <chr> <date> <int> <list> <date> <date> <date>
#> 1 Anhui date_o… 2013-03-09 1 <df> 2013-03-09 2013-03-28 2013-04-14
#> 2 Beijing date_o… 2013-04-11 1 <df> 2013-02-19 2013-04-11 2013-05-21
#> 3 Fujian date_o… 2013-04-17 1 <df> 2013-04-17 2013-04-18 2013-04-29
#> 4 Guangdong date_o… 2013-07-27 1 <df> 2013-02-19 2013-07-27 2013-07-27
#> 5 Hebei date_o… 2013-07-10 1 <df> 2013-02-19 2013-07-10 2013-07-10
#> 6 Henan date_o… 2013-04-06 1 <df> 2013-04-06 2013-04-08 2013-04-17
#> 7 Hunan date_o… 2013-04-14 1 <df> 2013-02-19 2013-04-14 2013-04-23
#> 8 Jiangsu date_o… 2013-03-19 2 <df> 2013-03-08 2013-03-21 2013-04-19
#> 9 Jiangxi date_o… 2013-04-15 1 <df> 2013-04-15 2013-04-17 2013-05-03
#> 10 Shandong date_o… 2013-04-16 1 <df> 2013-02-19 2013-04-16 2013-04-27
#> 11 Shanghai date_o… 2013-04-01 4 <df> 2013-03-17 2013-04-01 2013-04-10
#> 12 Taiwan date_o… 2013-04-12 1 <df> 2013-02-19 2013-04-12 2013-04-12
#> 13 Zhejiang date_o… 2013-04-06 5 <df> 2013-03-29 2013-04-08 2013-04-15
#> # … with abbreviated variable names ¹count_variable, ²observed_peak,
#> # ³observed_count, ⁴bootstrap_peaksThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.