EpiEstim (version 2.2-4)

Flu2009: Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.

Description

This data set gives:

1. the daily incidence of onset of acute respiratory illness (ARI, defined as at least two symptoms among fever, cough, sore throat, and runny nose) among children in a school in Pennsylvania during the 2009 H1N1 influenza pandemic (see source and references),

2. the discrete daily distribution of the serial interval for influenza, assuming a shifted Gamma distribution with mean 2.6 days, standard deviation 1.5 days and shift 1 day (see references).

3. interval-censored serial interval data from the 2009 outbreak of H1N1 influenza in San Antonio, Texas, USA (see references).

Format

A list of three elements:

• incidence: a dataframe with 32 lines containing dates in first column, and daily incidence in second column (Cauchemez et al., 2011),

• si_distr: a vector containing a set of 12 probabilities (Ferguson et al, 2005),

• si_data: a dataframe with 16 lines giving serial interval patient data collected in a household study in San Antonio, Texas throughout the 2009 H1N1 outbreak (Morgan et al., 2010).

References

Cauchemez S. et al. (2011) Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proc Natl Acad Sci U S A 108(7), 2825-2830.

Ferguson N.M. et al. (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437(7056), 209-214.

Examples

# NOT RUN {
## load data on pandemic flu in a school in 2009
data("Flu2009")

## estimate the reproduction number (method "non_parametric_si")
res <- estimate_R(Flu2009$incidence, method="non_parametric_si",
          config = make_config(list(si_distr = Flu2009$si_distr)))
plot(res)
## the second plot produced shows, at each each day,
## the estimate of the reproduction number
## over the 7-day window finishing on that day.

# }
# NOT RUN {
## Note the following examples use an MCMC routine
## to estimate the serial interval distribution from data,
## so they may take a few minutes to run

## estimate the reproduction number (method "si_from_data")
res <- estimate_R(Flu2009$incidence, method="si_from_data",
          si_data = Flu2009$si_data,
          config = make_config(list(mcmc_control = make_mcmc_control(list(
                                 burnin = 1000,
                                 thin = 10, seed = 1)),
                      n1 = 1000, n2 = 50,
                      si_parametric_distr = "G")))
plot(res)
## the second plot produced shows, at each each day,
## the estimate of the reproduction number
## over the 7-day window finishing on that day.
# }
# NOT RUN {

# }