A dataframe containing results used to compare degrees of introduction,
spread, prevalence, persistence and cumulative infections of SARS-CoV-2 in
simulated white-tailed deer populations. 1000 iterations (run_id) were run
for each scenario (Context). Scenarios included captive deer in outdoor ranch
facilities, captive deer in intensive facilities, wild deer in rural areas,
and wild deer in suburban areas. Setting indicates whether deer were captive
or wild. r0, or basic reproductive number, indicates the number of secondary
infections caused by a single infectious deer over the course of it's
infection. FOI, or Force-Of-Infection, is a hazard rate of a deer becoming
infected from infectious humans, per day. Prevalence is the percent of a
population infected, averaged over a simulated 120-day projection.
Persistence is a logical condition indicating if equilibrium determined by
SIRS ODE equations and run_steady() from the rootSolve package predicts at
least 1 in 1,000 deer infected at equilibrium. Cumulative infections reports
the total proportion of the population infected over the course of the
120-fall projection, and can exceed 1, indicating that all individuals were
infected at least once during the fall season.
Format
scenario_results
A data frame with 8 columns and 4000 rows storing the results of outbreak
simulations. Each row corresponds to an iteration of the simulation, with a
specific Context and random draw of epidemiological parameters:
- run_id
Identifier of run iteration
- Context
Scenario of focus for iteration
- Setting
Iteration is taking place with wild or captive deer
- r0
Basic reproductive number of iteration
- FOI
Force-of-Infection from human spillover
- Prevalence
Average daily proportion of deer population that is infected
- Persist
Logical value for an outbreak persisting in a population at equilibrium
- Cumulative_infections
Total proportion of deer infected over the course of the 120-day fall simulation
Examples
if (FALSE) {
head(scenario_results)
}