Serial Interval and Case Reproduction Number Estimation
Calculate Bootstrap Confidence Intervals for R Estimates
Calculate Reproduction Number Estimates
Calculate Serial Interval Probability Matrix
Calculate Right-Truncation Correction Factors
Convolution of the triangular distribution with the mixture component ...
Create Day Difference Matrix
Calculate serial interval mixture density assuming underlying gamma di...
Calculate serial interval mixture density assuming underlying normal d...
Calculate f0 for Different Components
Calculate flower for Different Components
Calculate fupper for Different Components
Generate Bootstrap Sample of Case Incidence
Generate Synthetic Epidemic Data Using the Renewal Equation
Integrate Serial Interval Component Functions for Likelihood Calculati...
Compute Serial Interval Component Integrals for All Transmission Route...
mitey: Serial Interval and Case Reproduction Number Estimation
Visualize Serial Interval Distribution Fit to Outbreak Data
Estimate Serial Interval Distribution Using the Vink Method
Apply Moving Average Smoothing to R Estimates
Estimate Time-Varying Case Reproduction Number Using Wallinga-Lipsitch...
Calculate Sample Weighted Variance
Calculate Weighted Negative Log-Likelihood for Gamma Distribution Para...
Provides methods to estimate serial intervals and time-varying case reproduction numbers from infectious disease outbreak data. Serial intervals measure the time between symptom onset in linked transmission pairs, while case reproduction numbers quantify how many secondary cases each infected individual generates over time. These parameters are essential for understanding transmission dynamics, evaluating control measures, and informing public health responses. The package implements the maximum likelihood framework from Vink et al. (2014) <doi:10.1093/aje/kwu209> for serial interval estimation and the retrospective method from Wallinga & Lipsitch (2007) <doi:10.1098/rspb.2006.3754> for reproduction number estimation. Originally developed for scabies transmission analysis but applicable to other infectious diseases including influenza, COVID-19, and emerging pathogens. Designed for epidemiologists, public health researchers, and infectious disease modelers working with outbreak surveillance data.
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