Outbreak Magnitude

Summarize the number of farms and total cattle infected in simulated outbreaks

Infection Summary

summarize_infections() summarizes infection metrics by iteration.

Code 
## Summarize infection metrics for each iteration
infect_summary <- summarize_infections(infection)

View a subset of infect_summary

Code 
## Filter data to scenario
infect_summary_select <- infect_summary %>%
  filter(region == "central") %>%
  filter(scenario_type == "suboptimal") %>%
  filter(preclinical == 2)

## Select and order columns to display
infect_summary_select <- infect_summary_select[c("iteration", "farms_infected", "cattle_infected", "first_infect", "last_infect")]

infect_summary_select <- infect_summary_select %>%
    mutate(across(c("iteration", "farms_infected", "cattle_infected", "first_infect", "last_infect"), comma))

## Check data
head(infect_summary_select)
iteration farms_infected cattle_infected first_infect last_infect
1 82 18,980 10 54
2 13 4,893 10 27
3 179 51,364 10 85
4 10 2,271 10 36
5 20 6,115 10 42
6 116 54,607 10 111

Summary Statistics

generate_infect_statistics() returns summary statistics for each modeling scenario. Results are grouped by region, scenario_type, and preclinical. The output is filtered by summary and region to compare farms_infected and cattle_infected between scenarios.

Code 
infect_config_summary <- generate_infect_statistics(infect_summary)

## Filter to farms
farms_inf_summary <- infect_config_summary %>%    
  filter(summary == "farms_infected")

## Filter to cattle
cattle_inf_summary <- infect_config_summary %>%    
  filter(summary == "cattle_infected")

Infected Farms

The number of infected farms

Code 
## Filter to western region
farms_western_summary <- farms_inf_summary %>%
  filter(region == "western")

## Select and order columns
farms_western_select <- farms_western_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

farms_western_select <- farms_western_select %>%
  mutate_if(is.numeric, round, digits = 2) %>%
  mutate_if(is.numeric, ~format(.x, nsmall = 2, big.mark = ","))

farms_western_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 8.29 2.00 3.00 4.00 8.00 31.10
optimal 1 16.63 2.00 4.00 7.00 18.00 66.00
optimal 2 34.95 2.00 5.75 14.00 45.25 150.10
optimal 3 60.04 3.00 8.00 26.00 88.50 212.15
suboptimal 0 47.28 4.00 12.00 30.00 65.00 137.20
suboptimal 1 92.23 6.00 25.00 58.00 135.25 267.05
suboptimal 2 159.91 8.00 43.00 114.50 235.25 461.05
suboptimal 3 303.24 19.90 107.75 237.50 423.25 800.00
low-virulence 6 1,567.76 155.85 725.75 1,449.50 2,288.00 3,455.30
Code 
## Filter to western region
farms_central_summary <- farms_inf_summary %>%
  filter(region == "central")

## Select and order columns
farms_central_select <- farms_central_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

farms_central_select <- farms_central_select %>%
  mutate_if(is.numeric, round, digits = 2) %>%
  mutate_if(is.numeric, ~format(.x, nsmall = 2, big.mark = ","))

farms_central_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 7.35 2.00 2.00 3.00 6.00 20.05
optimal 1 12.71 2.00 3.00 5.00 11.00 56.00
optimal 2 23.37 2.00 4.00 9.00 22.00 94.00
optimal 3 72.51 3.00 7.00 15.50 67.00 372.35
suboptimal 0 46.92 4.00 9.00 22.00 55.25 176.05
suboptimal 1 106.39 6.00 15.00 41.00 140.25 408.15
suboptimal 2 250.57 7.00 24.00 81.00 369.75 939.20
suboptimal 3 578.08 9.00 45.75 312.50 1,008.25 1,729.50
low-virulence 6 3,722.94 1,491.70 3,034.50 3,917.00 4,595.00 5,520.00
Code 
## Filter to eastern region
farms_eastern_summary <- farms_inf_summary %>%
  filter(region == "eastern")

## Select and order columns
farms_eastern_select <- farms_eastern_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

farms_eastern_select <- farms_eastern_select %>%
  mutate_if(is.numeric, round, digits = 2) %>%
  mutate_if(is.numeric, ~format(.x, nsmall = 2, big.mark = ","))

farms_eastern_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 6.56 2.00 2.00 4.00 7.00 20.05
optimal 1 13.59 2.00 3.00 6.00 13.00 57.05
optimal 2 30.40 2.00 5.00 10.00 26.00 124.05
optimal 3 54.78 2.00 7.00 15.00 61.25 237.10
suboptimal 0 41.67 4.00 10.00 21.00 51.00 134.05
suboptimal 1 89.45 5.00 16.00 41.00 94.75 330.70
suboptimal 2 186.16 6.00 26.00 74.00 268.25 684.10
suboptimal 3 457.09 9.00 43.00 250.50 655.50 1,620.15
low-virulence 6 3,101.59 61.85 2,435.75 3,412.50 4,187.25 4,993.10

Total Cattle

The total number of cattle on infected farms

Code 
## Filter to western region
cattle_western_summary <- cattle_inf_summary %>%
  filter(region == "western")

## Select and order columns
cattle_western_select <- cattle_western_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

cattle_western_select <- cattle_western_select %>%
    mutate(across(c(mean, q05, q25, q50, q75, q95), comma))

cattle_western_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 26,882 4,332 4,348 5,286 25,412 127,976
optimal 1 52,327 4,332 4,562 15,731 72,506 208,088
optimal 2 98,475 4,332 6,841 46,188 157,810 322,540
optimal 3 152,194 4,408 16,035 85,879 243,585 464,729
suboptimal 0 131,046 5,026 27,389 96,664 201,537 362,921
suboptimal 1 218,642 5,972 68,208 182,701 327,252 560,498
suboptimal 2 347,062 10,812 123,388 277,594 472,179 872,234
suboptimal 3 572,168 45,177 244,216 457,094 765,672 1,445,881
low-virulence 6 2,782,496 266,320 1,041,360 2,119,552 4,499,446 6,416,444
Code 
## Filter to central region
cattle_central_summary <- cattle_inf_summary %>%
  filter(region == "central")

## Select and order columns
cattle_central_select <- cattle_central_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

cattle_central_select <- cattle_central_select %>%
    mutate(across(c(mean, q05, q25, q50, q75, q95), comma))

cattle_central_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 4,347 1,627 1,627 1,708 2,899 13,059
optimal 1 7,215 1,627 1,676 2,090 6,008 30,945
optimal 2 13,340 1,627 1,804 3,940 11,030 51,068
optimal 3 44,454 1,652 2,390 7,635 33,766 233,235
suboptimal 0 25,980 1,756 4,074 11,906 28,268 103,639
suboptimal 1 62,846 1,873 7,052 18,708 72,517 275,944
suboptimal 2 156,363 2,106 10,802 41,478 201,033 666,540
suboptimal 3 455,723 2,608 20,375 167,748 666,591 1,930,263
low-virulence 6 3,243,922 913,215 2,260,734 3,449,833 4,241,387 5,265,010
Code 
## Filter to eastern region
cattle_eastern_summary <- cattle_inf_summary %>%
  filter(region == "eastern")

## Select and order columns
cattle_eastern_select <- cattle_eastern_summary[c("scenario_type", "preclinical", "mean", "q05", "q25", "q50", "q75", "q95")]

cattle_eastern_select <- cattle_eastern_select %>%
    mutate(across(c(mean, q05, q25, q50, q75, q95), comma))

cattle_eastern_select
scenario_type preclinical mean q05 q25 q50 q75 q95
optimal 0 2,687 1,164 1,164 1,247 2,236 8,903
optimal 1 5,315 1,164 1,209 1,843 4,832 21,955
optimal 2 11,115 1,164 1,356 2,824 8,958 47,171
optimal 3 17,873 1,164 1,767 4,779 21,290 79,301
suboptimal 0 16,143 1,268 2,524 6,678 18,888 57,100
suboptimal 1 31,848 1,453 4,203 13,356 33,844 126,666
suboptimal 2 62,893 1,606 6,739 23,120 83,049 253,791
suboptimal 3 153,263 1,933 13,766 74,902 215,762 601,651
low-virulence 6 1,019,391 14,089 799,123 1,161,486 1,366,618 1,600,988

Significance Test

Perform significance testing on optimal and suboptimal detection scenarios.

Code 
## Filter out low virulence scenarios
no_LV <- infection %>%
  filter(scenario_type != "low-virulence")

no_LV_summary <- summarize_infections(no_LV)

Linear Model

Code 
## Filter to western region
no_LV_western_summary <- no_LV_summary %>%
  filter(region == "western")

model_western_cattle <- lm(log(cattle_infected) ~ preclinical * scenario_type, 
                       data = no_LV_western_summary)

summary(model_western_cattle)

Call:
lm(formula = log(cattle_infected) ~ preclinical * scenario_type, 
    data = no_LV_western_summary)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4149 -0.9527  0.1667  1.0284  3.4413 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                  10.21850    0.04302 237.527   <2e-16 ***
preclinical1                  0.59910    0.06084   9.847   <2e-16 ***
preclinical2                  1.18004    0.06084  19.396   <2e-16 ***
preclinical3                  1.70577    0.06084  28.037   <2e-16 ***
scenario_type.L               1.26145    0.06084  20.734   <2e-16 ***
preclinical1:scenario_type.L  0.05175    0.08604   0.601    0.548    
preclinical2:scenario_type.L -0.07861    0.08604  -0.914    0.361    
preclinical3:scenario_type.L  0.02548    0.08604   0.296    0.767    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.36 on 3992 degrees of freedom
Multiple R-squared:  0.3943,    Adjusted R-squared:  0.3933 
F-statistic: 371.3 on 7 and 3992 DF,  p-value: < 2.2e-16
Code 
## Filter to central region
no_LV_central_summary <- no_LV_summary %>%
  filter(region == "central")

model_central_cattle <- lm(log(cattle_infected) ~ preclinical * scenario_type, 
                       data = no_LV_central_summary)

summary(model_central_cattle)

Call:
lm(formula = log(cattle_infected) ~ preclinical * scenario_type, 
    data = no_LV_central_summary)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2343 -0.8894 -0.2982  1.0457  4.4918 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   8.60372    0.04628 185.898  < 2e-16 ***
preclinical1                  0.46313    0.06545   7.076 1.75e-12 ***
preclinical2                  1.01288    0.06545  15.475  < 2e-16 ***
preclinical3                  1.83846    0.06545  28.088  < 2e-16 ***
scenario_type.L               1.08133    0.06545  16.521  < 2e-16 ***
preclinical1:scenario_type.L  0.19539    0.09256   2.111   0.0348 *  
preclinical2:scenario_type.L  0.39906    0.09256   4.311 1.66e-05 ***
preclinical3:scenario_type.L  0.59686    0.09256   6.448 1.27e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.464 on 3992 degrees of freedom
Multiple R-squared:  0.4032,    Adjusted R-squared:  0.4022 
F-statistic: 385.3 on 7 and 3992 DF,  p-value: < 2.2e-16
Code 
## Filter to eastern region
no_LV_eastern_summary <- no_LV_summary %>%
  filter(region == "eastern")

model_eastern_cattle <- lm(log(cattle_infected) ~ preclinical * scenario_type, 
                       data = no_LV_eastern_summary)

summary(model_eastern_cattle)

Call:
lm(formula = log(cattle_infected) ~ preclinical * scenario_type, 
    data = no_LV_eastern_summary)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7779 -0.8537 -0.2152  0.9186  4.4417 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   8.21104    0.04188 196.060  < 2e-16 ***
preclinical1                  0.47570    0.05923   8.032 1.25e-15 ***
preclinical2                  0.95705    0.05923  16.159  < 2e-16 ***
preclinical3                  1.59329    0.05923  26.901  < 2e-16 ***
scenario_type.L               0.99160    0.05923  16.742  < 2e-16 ***
preclinical1:scenario_type.L  0.10213    0.08376   1.219  0.22280    
preclinical2:scenario_type.L  0.21745    0.08376   2.596  0.00947 ** 
preclinical3:scenario_type.L  0.46956    0.08376   5.606 2.21e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.324 on 3992 degrees of freedom
Multiple R-squared:  0.3794,    Adjusted R-squared:  0.3783 
F-statistic: 348.6 on 7 and 3992 DF,  p-value: < 2.2e-16

ANOVA

Code 
anova(model_western_cattle)
Df Sum Sq Mean Sq F value Pr(>F)
preclinical 3 1624.908557 541.636186 292.6562847 0.0000000
scenario_type 1 3180.762434 3180.762434 1718.6261576 0.0000000
preclinical:scenario_type 3 4.753417 1.584472 0.8561205 0.4631525
Residuals 3992 7388.229011 1.850759 NA NA
Code 
anova(model_central_cattle)
Df Sum Sq Mean Sq F value Pr(>F)
preclinical 3 1873.93075 624.643584 291.61346 0
scenario_type 1 3804.16224 3804.162244 1775.96463 0
preclinical:scenario_type 3 99.43042 33.143473 15.47296 0
Residuals 3992 8550.96743 2.142026 NA NA
Code 
anova(model_eastern_cattle)
Df Sum Sq Mean Sq F value Pr(>F)
preclinical 3 1391.58709 463.862363 264.46615 0e+00
scenario_type 1 2826.86883 2826.868825 1611.70891 0e+00
preclinical:scenario_type 3 61.25672 20.418906 11.64162 1e-07
Residuals 3992 7001.79806 1.753957 NA NA

Plot Cattle Numbers

plot_infected_cattle() returns a plot with the duration of incubation phase transmission on the x-axis and the median number of infected cattle (log10) on the y-axis.

Code 
central_cattle_plot <- plot_infected_cattle(cattle_central_summary, "central")

central_cattle_plot

Code 
eastern_cattle_plot <- plot_infected_cattle(cattle_eastern_summary, "eastern")

eastern_cattle_plot