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Automatic readme update
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@actions-user @joshwlambert
actions-user authored and joshwlambert committed Jan 4, 2024
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134 changes: 67 additions & 67 deletions README.md
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Expand Up @@ -110,20 +110,20 @@ linelist <- sim_linelist(
onset_to_death = onset_to_death
)
head(linelist)
#> id case_name case_type gender age date_onset date_admission
#> 1 1 Brian Murphy confirmed m 87 2023-01-01 <NA>
#> 2 2 Benjamin Brooks probable m 13 2023-01-01 <NA>
#> 3 3 Zoe Turner probable f 6 2023-01-01 <NA>
#> 4 4 Hannah Butler confirmed f 32 2023-01-02 <NA>
#> 5 5 Daris Villalobos-Corral confirmed m 50 2023-01-03 <NA>
#> 6 6 Zachary Lapre probable m 34 2023-01-01 <NA>
#> date_death date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> 27.9
#> 2 <NA> 2023-01-01 2023-01-05 NA
#> 3 <NA> 2022-12-31 2023-01-05 NA
#> 4 <NA> 2022-12-30 2023-01-03 27.9
#> 5 <NA> 2022-12-27 2023-01-03 27.9
#> 6 <NA> 2023-01-02 2023-01-05 NA
#> id case_name case_type gender age date_onset date_admission date_death
#> 1 1 Justin Murphy suspected m 8 2023-01-01 <NA> <NA>
#> 2 2 Mahmood al-Badie confirmed m 66 2023-01-02 <NA> <NA>
#> 3 3 Hannah Dice suspected f 81 2023-01-01 <NA> <NA>
#> 4 4 Simey Vu confirmed f 55 2023-01-02 <NA> <NA>
#> 5 5 Ira Francis confirmed m 26 2023-01-03 <NA> <NA>
#> 6 6 Jacob Sumners probable m 13 2023-01-03 <NA> <NA>
#> date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> NA
#> 2 2022-12-31 2023-01-02 22.7
#> 3 2023-01-01 2023-01-03 NA
#> 4 2023-01-01 2023-01-04 22.7
#> 5 2023-01-02 2023-01-06 22.7
#> 6 2023-01-03 2023-01-06 NA
```

In this example, the line list is simulated using the default values
Expand All @@ -143,26 +143,26 @@ linelist <- sim_linelist(
)
head(linelist)
#> id case_name case_type gender age date_onset date_admission
#> 1 1 Luc James confirmed m 43 2019-12-01 <NA>
#> 2 2 Ernestina Espinoza confirmed f 24 2019-12-02 <NA>
#> 3 3 Nicholas Krishnaswamy confirmed m 53 2019-12-01 <NA>
#> 4 4 Ian Lewis probable m 24 2019-12-02 <NA>
#> 5 5 Antonio Alvarez probable m 11 2019-12-02 <NA>
#> 6 6 Abdul Haleem al-Youssef suspected m 45 2019-12-03 <NA>
#> 1 1 Cheyenne Espino Aguilar probable f 5 2019-12-01 <NA>
#> 2 2 Muhjar al-Nawaz confirmed f 37 2019-12-01 <NA>
#> 3 3 Crystal Spleen probable f 41 2019-12-01 <NA>
#> 4 4 Nohemy Montano suspected f 36 2019-12-01 <NA>
#> 5 5 Malik Fuller suspected m 5 2019-12-01 <NA>
#> 6 6 Lavelle Snow probable m 86 2019-12-01 <NA>
#> date_death date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> 25.2
#> 2 <NA> 2019-12-04 2019-12-07 25.2
#> 3 <NA> 2019-12-02 2019-12-03 25.2
#> 4 <NA> 2019-12-04 2019-12-06 NA
#> 5 <NA> 2019-11-30 2019-12-02 NA
#> 1 2019-12-26 <NA> <NA> NA
#> 2 <NA> 2019-12-01 2019-12-08 26.3
#> 3 <NA> 2019-12-02 2019-12-04 NA
#> 4 <NA> 2019-12-02 2019-12-05 NA
#> 5 <NA> 2019-11-29 2019-12-03 NA
#> 6 <NA> 2019-12-01 2019-12-04 NA
```

To simulate a table of contacts of cases (i.e. to reflect a contact
tracing dataset) we can use the same serial interval defined for the
example above. We additionally need a contact distribution, which
represents the probability that each person in the population will be a
given number of contacts on a given day.
represents the probability that each person that infected an individual,
also had a given number of contacts that did not become infected.

``` r
contact_distribution <- epiparameter::epidist(
Expand All @@ -179,20 +179,20 @@ contacts <- sim_contacts(
contact_distribution = contact_distribution
)
head(contacts)
#> from to cnt_age cnt_gender date_first_contact
#> 1 Jacob Camacho Fiona Dall 55 f 2022-12-31
#> 2 Jacob Camacho Chandan Vang 36 m 2022-12-30
#> 3 Jacob Camacho Troy Choi 70 m 2023-01-01
#> 4 Jacob Camacho Fateena al-Hamdan 74 f 2023-01-04
#> 5 Jacob Camacho Abdul Baasid el-Asad 48 m 2023-01-03
#> 6 Fiona Dall Jeremy Moreno 83 m 2022-12-31
#> date_last_contact was_case status
#> 1 2023-01-06 Y case
#> 2 2023-01-01 N under_followup
#> 3 2023-01-02 N under_followup
#> 4 2023-01-06 N under_followup
#> 5 2023-01-04 N under_followup
#> 6 2023-01-04 Y case
#> from to cnt_age cnt_gender date_first_contact
#> 1 Lauren Gurreri Raeanna Riley 9 f 2022-12-30
#> 2 Lauren Gurreri Breann Huff 53 f 2023-01-04
#> 3 Lauren Gurreri Talaal el-Attar 83 m 2023-01-01
#> 4 Lauren Gurreri Austin Martinez 47 m 2022-12-29
#> 5 Lauren Gurreri Theodora Fox 37 f 2022-12-30
#> 6 Lauren Gurreri Ashleigh Reiter 89 f 2023-01-01
#> date_last_contact was_case status
#> 1 2023-01-04 Y case
#> 2 2023-01-05 Y case
#> 3 2023-01-04 Y case
#> 4 2023-01-02 N under_followup
#> 5 2023-01-03 N under_followup
#> 6 2023-01-03 N lost_to_followup
```

If both the line list and contacts table are required, they can be
Expand All @@ -210,35 +210,35 @@ outbreak <- sim_outbreak(
contact_distribution = contact_distribution
)
head(outbreak$linelist)
#> id case_name case_type gender age date_onset date_admission
#> 1 1 Amanda Mills suspected f 72 2023-01-01 <NA>
#> 2 2 Anthony San Miguel confirmed m 32 2023-01-01 <NA>
#> 3 3 Calista Silva confirmed f 57 2023-01-03 <NA>
#> 4 4 Alia Strover confirmed f 1 2023-01-01 2023-01-03
#> 5 5 Justin Burchfield probable m 48 2023-01-03 <NA>
#> 6 6 Cody Nguyen confirmed m 1 2023-01-06 <NA>
#> id case_name case_type gender age date_onset date_admission
#> 1 1 Celsa Davis probable f 86 2023-01-01 2023-01-06
#> 2 2 Tatyanna Yarbrough-Montoya suspected f 56 2023-01-01 2023-01-01
#> 3 3 Erika Cordova suspected f 84 2023-01-01 <NA>
#> 4 4 Nakheel al-Amara confirmed f 48 2023-01-01 <NA>
#> 5 5 Ayanna Roberts probable f 65 2023-01-01 <NA>
#> 6 6 Matthew Apodaca confirmed m 11 2023-01-02 <NA>
#> date_death date_first_contact date_last_contact ct_value
#> 1 <NA> <NA> <NA> NA
#> 2 <NA> 2023-01-01 2023-01-04 23.5
#> 3 <NA> 2023-01-01 2023-01-05 23.5
#> 4 <NA> 2022-12-31 2023-01-05 23.5
#> 5 <NA> 2023-01-03 2023-01-07 NA
#> 6 <NA> 2023-01-01 2023-01-04 23.5
#> 2 <NA> 2023-01-02 2023-01-03 NA
#> 3 <NA> 2022-12-29 2023-01-02 NA
#> 4 <NA> 2023-01-02 2023-01-06 25.4
#> 5 <NA> 2022-12-30 2023-01-03 NA
#> 6 <NA> 2022-12-29 2023-01-02 25.4
head(outbreak$contacts)
#> from to cnt_age cnt_gender date_first_contact
#> 1 Amanda Mills Anthony San Miguel 32 m 2023-01-01
#> 2 Amanda Mills Corey Dunlap 37 m 2023-01-01
#> 3 Amanda Mills Fendi Saldanha 82 f 2023-01-01
#> 4 Anthony San Miguel Calista Silva 57 f 2023-01-01
#> 5 Anthony San Miguel Alia Strover 1 f 2022-12-31
#> 6 Anthony San Miguel Justin Burchfield 48 m 2023-01-03
#> date_last_contact was_case status
#> 1 2023-01-04 Y case
#> 2 2023-01-04 N lost_to_followup
#> 3 2023-01-03 N lost_to_followup
#> 4 2023-01-05 Y case
#> 5 2023-01-05 Y case
#> 6 2023-01-07 Y case
#> from to cnt_age cnt_gender
#> 1 Celsa Davis Tatyanna Yarbrough-Montoya 56 f
#> 2 Celsa Davis Erika Cordova 84 f
#> 3 Celsa Davis Mawzoona al-Vaziri 71 f
#> 4 Celsa Davis Masood al-Ramin 82 m
#> 5 Celsa Davis Akshitsinh Vang 16 m
#> 6 Tatyanna Yarbrough-Montoya Nakheel al-Amara 48 f
#> date_first_contact date_last_contact was_case status
#> 1 2023-01-02 2023-01-03 Y case
#> 2 2022-12-29 2023-01-02 Y case
#> 3 2023-01-01 2023-01-03 N under_followup
#> 4 2023-01-01 2023-01-03 N under_followup
#> 5 2023-01-05 2023-01-08 N under_followup
#> 6 2023-01-02 2023-01-06 Y case
```

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