predict_future_infections: Prediction of future infections per day

In smidm: Statistical Modelling for Infectious Disease Management

Prediction of future infections per day

Description

Predicts how many people are expected to develop symptoms on each day after the last reported infection after a group event.

Usage

predict_future_infections(
  last_day_reported_infection,
  total_reported_infections,
  total_expected_infections,
  meanlog = 1.69,
  sdlog = 0.55
)

Arguments

last_day_reported_infection	Number of days the last infection was reported after the event (0 = event day).
total_reported_infections	Number of reported symptomatic infections so far.
total_expected_infections	Number of expected symptomatic infections in total.
meanlog	Number, the parameter of mean from the log-normal distribution.
sdlog	Number, the parameter of sd from the log-normal distribution.

Details

meanlog and sdlog are the log-normal distribution parameters derived from the incubation period characteristics described in Xin et al. (2021).

Value

Vector with expected future infections per day after the event.

References

Xin H, Wong JY, Murphy C et al. (2021) "The Incubation Period Distribution of Coronavirus Disease 2019: A Systematic Review and Meta-Analysis". Clinical Infectious Diseases, 73(12): 2344-2352.

Examples

predict_future_infections(last_day_reported_infection = 3,
                          total_reported_infections = 5,
                          total_expected_infections = 15)

smidm documentation built on Aug. 27, 2022, 9:06 a.m.