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")]

## Check data
head(infect_summary_select)

iteration	farms_infected	cattle_infected	first_infect	last_infect
1	82	18980	10	54
2	13	4893	10	27
3	179	51364	10	85
4	10	2271	10	36
5	20	6115	10	42
6	116	54607	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

Central U.S.
Eastern U.S.

Code

## Filter to central region
farms_central_summary <- farms_inf_summary %>%
  filter(region == "central")

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

farms_central_summary

scenario_type	preclinical	mean	q05	q25	q50	q75	q95
optimal	0	7.352	2.0	2.00	3.0	6.00	20.05
optimal	1	12.714	2.0	3.00	5.0	11.00	56.00
optimal	2	23.366	2.0	4.00	9.0	22.00	94.00
optimal	3	72.506	3.0	7.00	15.5	67.00	372.35
suboptimal	0	46.924	4.0	9.00	22.0	55.25	176.05
suboptimal	1	106.394	6.0	15.00	41.0	140.25	408.15
suboptimal	2	250.570	7.0	24.00	81.0	369.75	939.20
suboptimal	3	578.078	9.0	45.75	312.5	1008.25	1729.50
low-virulence	6	3722.938	1491.7	3034.50	3917.0	4595.00	5520.00

Code

## Filter to eastern region
farms_eastern_summary <- farms_inf_summary %>%
  filter(region == "eastern")

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

farms_eastern_summary

scenario_type	preclinical	mean	q05	q25	q50	q75	q95
optimal	0	6.564	2.00	2.00	4.0	7.00	20.05
optimal	1	13.590	2.00	3.00	6.0	13.00	57.05
optimal	2	30.400	2.00	5.00	10.0	26.00	124.05
optimal	3	54.782	2.00	7.00	15.0	61.25	237.10
suboptimal	0	41.674	4.00	10.00	21.0	51.00	134.05
suboptimal	1	89.450	5.00	16.00	41.0	94.75	330.70
suboptimal	2	186.164	6.00	26.00	74.0	268.25	684.10
suboptimal	3	457.092	9.00	43.00	250.5	655.50	1620.15
low-virulence	6	3101.590	61.85	2435.75	3412.5	4187.25	4993.10

Total Cattle

The total number of cattle on infected farms

Central U.S.
Eastern U.S.

Code

## Filter to central region
cattle_central_summary <- cattle_inf_summary %>%
  filter(region == "central")

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

cattle_central_summary

scenario_type	preclinical	mean	q05	q25	q50	q75	q95
optimal	0	4347.228	1627.00	1627.00	1708.5	2899.25	13058.80
optimal	1	7215.244	1627.00	1675.75	2089.5	6008.25	30945.40
optimal	2	13340.340	1627.00	1803.50	3940.5	11030.25	51068.45
optimal	3	44453.892	1651.85	2390.25	7635.0	33766.50	233234.75
suboptimal	0	25979.990	1755.60	4074.25	11906.5	28267.75	103639.20
suboptimal	1	62846.180	1873.15	7052.00	18708.5	72516.75	275944.45
suboptimal	2	156362.888	2106.40	10801.75	41477.5	201033.25	666540.50
suboptimal	3	455723.392	2607.75	20374.75	167747.5	666590.75	1930262.85
low-virulence	6	3243921.747	913215.30	2260734.25	3449833.0	4241386.75	5265010.15

Code

## Filter to eastern region
cattle_eastern_summary <- cattle_inf_summary %>%
  filter(region == "eastern")

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

cattle_eastern_summary

scenario_type	preclinical	mean	q05	q25	q50	q75	q95
optimal	0	2686.652	1164.00	1164.00	1247.0	2236.25	8903.30
optimal	1	5315.418	1164.00	1208.75	1843.0	4832.50	21955.40
optimal	2	11114.532	1164.00	1355.50	2824.0	8958.25	47171.35
optimal	3	17873.322	1164.00	1766.75	4779.0	21289.75	79301.35
suboptimal	0	16142.812	1267.80	2524.25	6678.5	18888.50	57099.95
suboptimal	1	31848.112	1453.00	4203.00	13355.5	33844.50	126665.70
suboptimal	2	62893.166	1606.45	6739.00	23120.0	83048.75	253791.15
suboptimal	3	153263.466	1933.45	13765.75	74901.5	215762.00	601651.45
low-virulence	6	1019390.812	14089.10	799123.25	1161486.0	1366618.00	1600987.95

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)

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

Central U.S.
Eastern U.S.

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.

Central U.S.
Eastern U.S.

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