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Peak estimation

Tim Taylor

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")
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 rows

`find_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      7

`estimate_peak()`

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

the total number of event is known (no uncertainty on total incidence);
dates with no events (zero incidence) will never be in bootstrapped datasets; and
the reporting is assumed to be constant over time, i.e. every case is equally likely to be reported.

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_peaks

These 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.