Date: 2023-12-06 11:56:43
Author: T.Gibson
Run Name: Sim_2


Three scenarios for site surveillance to detect non indigenous species (NIS) introduction are presented and the time to detection is reported.


Input Parameters

##           Parameter Name    Input Value
## 1                   user       T.Gibson
## 2               run_name          Sim_2
## 3                   seed           2022
## 4                num_sim          10000
## 5              num_years             30
## 6              num_sites            100
## 7         establish_risk random uniform
## 8             intro_risk random uniform
## 9         establish_prob            0.8
## 10            intro_prob            0.8
## 11       mean_visit_rate              1
## 12        detect_dynamic       constant
## 13              det_prob            0.8
## 14          det_prob_min              0
## 15          det_prob_max              1
## 16                seed_n              1
## 17        detect_summary           last
## 18             start_pop              1
## 19         start_possion              F
## 20                 pop_R              2
## 21          growth_model    exponential
## 22               pop_cap            500
## 23                  APrb             10
## 24       Abund_Threshold           1000
## 25            Prob_Below            0.1
## 26            Prob_Above            0.8
## 27  sensitivity_analysis          FALSE
## 28   elasticity_analysis          FALSE
## 29 elasticity_proportion            0.1
## 30              defaults        default
## 31              defaults            100
## 32              defaults             30
## 33              defaults random uniform
## 34              defaults            0.8
## 35              defaults            0.8
## 36              defaults random uniform
## 37              defaults              1
## 38              defaults            0.8
## 39              defaults            0.9
## 40              defaults              1
## 41              defaults            0.1
## 42              defaults       constant
## 43              defaults              1
## 44              defaults             10
## 45              defaults              1
## 46              defaults              F
## 47              defaults              2
## 48              defaults    exponential
## 49              defaults            500
## 50              defaults             10
## 51              defaults           1000
## 52              defaults            0.1
## 53              defaults            0.8


Introduction and establishment probabilities across sites

For all three surveillance scenarios a probability of establishment, probability of introduction, and probability of introduction and establishment are randomly generated or calculated per site. These are displayed below.


## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).


Site-specific visit rate for each surveillance scenario

Note that for each of these surveillance strategies the overall visit rate, as determined in the input parameters mean_visit_rate (in this run: 1) remains the same. The site visit rate is artificially increased for sites with higher probability of introduction and establishment in risk-based surveillance and lowered for those with lower probability of introduction and establishment. These plots demonstrate this.


## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).


Time to detection for each site

The time it takes in years to visit a site where the NIS has been introduced and also detect it is illustrated in the histograms below. Where an NIS introduction is not detected within the surveillance period of 30 the time to detection reported is 1000 years.


## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Warning: Removed 15 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Warning: Removed 749 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 2 rows containing missing values (`geom_bar()`).


The time it takes in years to visit a site where the NIS has been introduced is also shown per simulation below.


## Warning: Removed 15 rows containing missing values (`geom_point()`).
## Warning: Removed 749 rows containing missing values (`geom_point()`).


Probability of NIS Detection Over Time Depending on Surveillance Method

The probability of detection an NIS introduction over time using the three surveillance methods is also shown for quick visual comparison.


## Warning: Removed 764 rows containing missing values (`geom_line()`).


Summary of detection times:

A: Random surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000014  0.371364  0.889730  1.279498  1.766252 11.968989


B: Risk-based surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000017  0.231568  0.564472  1.152412  1.244430 29.753959


C: Heavy risk-based surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000108  0.172804  0.517811  1.903354  1.627596 29.857621


Simulations with No Detections (%):

A: Random surveillance

## [1] 0

B: Risk-based surveillance

## [1] 0.15

C: Heavy risk-based surveillance

## [1] 7.49

Version

The version (sha) of https://github.com/CefasRepRes/NIS-intro-detect-sim repository used was: 885e3d4e15b15628ccfa6a548f6359967cb7bb6e

## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8  LC_CTYPE=English_United Kingdom.utf8   
## [3] LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] dplyr_1.1.2       data.table_1.14.8 ReIns_1.0.12      EnvStats_2.7.0    patchwork_1.1.2   ggplot2_3.4.2    
##  [7] gtools_3.9.4      reshape2_1.4.4    truncnorm_1.0-9   here_1.0.1        yaml_2.3.7       
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0  xfun_0.39         bslib_0.5.0       splines_4.2.2     lattice_0.20-45   colorspace_2.1-0 
##  [7] vctrs_0.6.2       generics_0.1.3    htmltools_0.5.5   utf8_1.2.3        survival_3.4-0    rlang_1.1.1      
## [13] pillar_1.9.0      jquerylib_0.1.4   glue_1.6.2        withr_2.5.0       foreach_1.5.2     lifecycle_1.0.3  
## [19] plyr_1.8.8        stringr_1.5.0     munsell_0.5.0     gtable_0.3.3      codetools_0.2-18  evaluate_0.21    
## [25] labeling_0.4.2    knitr_1.43        fastmap_1.1.1     doParallel_1.0.17 parallel_4.2.2    fansi_1.0.4      
## [31] highr_0.10        Rcpp_1.0.10       scales_1.2.1      cachem_1.0.8      jsonlite_1.8.5    farver_2.1.1     
## [37] digest_0.6.31     stringi_1.7.12    grid_4.2.2        rprojroot_2.0.3   cli_3.6.1         tools_4.2.2      
## [43] magrittr_2.0.3    sass_0.4.6        tibble_3.2.1      pkgconfig_2.0.3   Matrix_1.5-1      rmarkdown_2.22   
## [49] rstudioapi_0.14   iterators_1.0.14  R6_2.5.1          compiler_4.2.2