| Type: | Package |
| Title: | Vaccine Coverage and Outbreak Risk Analysis |
| Version: | 0.1.0 |
| Description: | Provides tools to analyze vaccine coverage data and simulate potential disease outbreak scenarios. It allows users to calculate key epidemiological metrics such as the effective reproduction number (Re), outbreak probabilities, and expected infection counts based on county-level vaccination rates, disease characteristics, and vaccine effectiveness. The package includes historical kindergarten vaccination data for Florida counties and offers functions for generating summary tables, visualizations, and exporting the underlying plot data. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, tidyr, ggplot2, scales, purrr, tibble, openxlsx |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 3.5) |
| NeedsCompilation: | no |
| Packaged: | 2025-09-10 00:41:47 UTC; TopSoarer |
| Author: | Peiyu Liu [aut, cre], Matt Hitchings [ctb], Ira Longini [ctb] |
| Maintainer: | Peiyu Liu <pyliu0620@outlook.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-15 07:30:02 UTC |
Calculate the expected number of infections in a cohort
Description
Calculate the expected number of infections in a cohort
Usage
calc_expected_infections(vc, disease, kindergarten_size, VE, r0_custom = NULL)
Arguments
vc |
Vaccination coverage rate (proportion, 0 to 1). |
disease |
Character string: "Measles", "Pertussis", "Chickenpox", or "Custom". |
kindergarten_size |
Integer, the size of the school cohort. |
VE |
Vaccine effectiveness (proportion, 0 to 1). |
r0_custom |
The basic reproduction number (R0) for a custom disease. Required only if 'disease' is "Custom". |
Value
The expected number of infected individuals.
Examples
calc_expected_infections(vc = 0.85, disease = "Measles", kindergarten_size = 200, VE = 0.97)
calc_expected_infections(
vc = 0.90, disease = "Custom", kindergarten_size = 150, VE = 0.90, r0_custom = 7
)
Calculate the effective reproduction number (Re)
Description
Calculate the effective reproduction number (Re)
Usage
calc_re(vc, disease, VE, r0_custom = NULL)
Arguments
vc |
Vaccination coverage rate (proportion, 0 to 1). |
disease |
Character string: "Measles", "Pertussis", "Chickenpox", or "Custom". |
VE |
Vaccine effectiveness (proportion, 0 to 1). |
r0_custom |
The basic reproduction number (R0) for a custom disease. Required only if 'disease' is "Custom". |
Value
The effective reproduction number (Re).
Examples
# For a standard disease
calc_re(vc = 0.92, disease = "Measles", VE = 0.97)
# For a custom disease like mumps (R0 approx. 10-12)
calc_re(vc = 0.88, disease = "Custom", VE = 0.85, r0_custom = 11)
Kindergarten Vaccination Coverage for Florida Counties
Description
A dataset containing the percentage of kindergarten students who have completed required immunizations for school entry in Florida counties.
Usage
florida_vaccine_coverage
Format
A data frame with 748 rows and 3 variables:
- County
The name of the county in Florida, plus a statewide "Florida" total.
- Year
The calendar year of the report, from 2016 to 2024.
- Coverage_Rate
The proportion (0 to 1) of kindergarten students with complete immunizations.
Source
Florida Department of Health, Bureau of Immunization.
Plot Historical Vaccination Coverage Rate
Description
Plot Historical Vaccination Coverage Rate
Usage
plot_coverage_history(county_name, save_data_to = NULL)
Arguments
county_name |
A character vector of county names to plot. |
save_data_to |
Optional file path to save the plot's data. |
Value
A ggplot object.
Examples
plot_coverage_history(county_name = c("Florida", "Miami-Dade", "Liberty"))
Plot Outbreak Probability vs. Coverage
Description
Plot Outbreak Probability vs. Coverage
Usage
plot_outbreak_prob(
disease = "Measles",
VE = NULL,
r0_custom = NULL,
save_data_to = NULL
)
Arguments
disease |
The disease to model. Default is "Measles". |
VE |
Vaccine effectiveness. If NULL (the default), a sensible default is used for the chosen disease (e.g., 0.97 for Measles). A user-provided value (0 to 1) will override the default. |
r0_custom |
The basic reproduction number (R0) for a custom disease. Required only if 'disease' is "Custom". |
save_data_to |
Optional file path to save the plot's data. |
Value
A ggplot object.
Examples
# Plot with default VE for Pertussis (~0.85)
plot_outbreak_prob(disease = "Pertussis")
# Override default VE for Pertussis
plot_outbreak_prob(disease = "Pertussis", VE = 0.91)
Plot the Risk Curve of Expected Infections
Description
Plot the Risk Curve of Expected Infections
Usage
plot_risk_curve(
disease = "Measles",
kindergarten_size = 200,
VE = NULL,
r0_custom = NULL,
save_data_to = NULL
)
Arguments
disease |
The disease to model. Default is "Measles". |
kindergarten_size |
The size of the school cohort. Default is 200. |
VE |
Vaccine effectiveness. If NULL (the default), a sensible default is used for the chosen disease (e.g., 0.97 for Measles). A user-provided value (0 to 1) will override the default. |
r0_custom |
The basic reproduction number (R0) for a custom disease. Required only if 'disease' is "Custom". |
save_data_to |
Optional file path to save the plot's underlying data. |
Value
A ggplot object.
Examples
# Plot with default VE for Pertussis (~0.85)
plot_risk_curve(disease = "Pertussis")
# Plot for a custom disease, requires VE and r0_custom
plot_risk_curve(disease = "Custom", VE = 0.85, r0_custom = 12)
Calculate the probability of a major outbreak
Description
Calculate the probability of a major outbreak
Usage
prob_major_outbreak(Re)
Arguments
Re |
The effective reproduction number. |
Value
The probability of a major outbreak (1 - 1/Re).
Examples
prob_major_outbreak(Re = 2.05)
Calculate the probability of at least one secondary infection
Description
Calculate the probability of at least one secondary infection
Usage
prob_sec_case(Re)
Arguments
Re |
The effective reproduction number. |
Value
The probability of at least one secondary infection.
Examples
prob_sec_case(Re = 2.05)
Summarize Vaccination Coverage for a Given Year
Description
Retrieves and sorts the vaccination coverage rates for all Florida counties for a specified year.
Usage
summary_coverage(yr)
Arguments
yr |
The year to summarize (e.g., 2024). |
Value
A tibble with columns 'County' and 'Coverage_Rate' (as a numeric proportion), sorted in descending order of the coverage rate.
Examples
# Get a summary of vaccine coverage for 2024
summary_coverage(yr = 2024)
Generate an Outbreak Risk Simulation Table
Description
Creates a summary table of outbreak risk for a given year. The analysis includes scenarios for the statewide average coverage rate as well as for counties at the minimum, maximum, and quartile coverage rates. This function can model pre-set diseases or a custom disease with a user-specified R0.
Usage
summary_infection_risk(
yr,
disease = "Measles",
kindergarten_size = 200,
VE = NULL,
r0_custom = NULL
)
Arguments
yr |
The year for the analysis (e.g., 2024). |
disease |
The disease to model. Choose from "Measles", "Pertussis", "Chickenpox", or "Custom". Default is "Measles". |
kindergarten_size |
The size of the school cohort. Default is 200. |
VE |
Vaccine effectiveness. If NULL (the default), a sensible default is used for the chosen disease (e.g., 0.97 for Measles). A user-provided value (0 to 1) will override the default. |
r0_custom |
The basic reproduction number (R0) for a custom disease. This parameter is required and must be a positive number if 'disease' is set to "Custom". |
Value
A data frame containing the formatted risk analysis table.
Examples
# Example 1: Standard analysis for Measles in 2024 (uses default VE of 0.97)
summary_infection_risk(yr = 2024, disease = "Measles")
# Example 2: Analysis for Pertussis, overriding the default VE
summary_infection_risk(yr = 2024, disease = "Pertussis", VE = 0.91)
# Example 3: Analysis for a custom disease (e.g., Mumps-like)
summary_infection_risk(yr = 2024, disease = "Custom", VE = 0.88, r0_custom = 11)