Description Usage Format Source References See Also Examples
Data on the 188 cases in the measles outbreak among children in the German city of Hagelloch (near Tübingen) 1861. The data were originally collected by Dr. Albert Pfeilsticker (1863) and augmented and re-analysed by Dr. Heike Oesterle (1992).
1 | data("hagelloch")
|
Loading data("hagelloch")
gives two objects:
hagelloch
and hagelloch.df
. The former is an
"epidata"
object for use with twinSIR
containing the entire SIR event history of the outbreak in the
population of 188 children.
The latter is the original data.frame
of 188 rows
with individual information for each infected child.
The covariate information in hagelloch.df
is as follows:
patient number
patient name (as a factor)
family index
house number
age in years
gender of the individual (factor: male, female)
Date
of prodromes
Date
of rash
class (factor: preschool, 1st class, 2nd class)
Date
of death (with missings)
number of patient who is the putative source of infection (0 = unknown)
serial interval = number of days between dates of prodromes of infection source and infected person
complications (factor: no complicatons, bronchopneumonia, severe bronchitis, lobar pneumonia, pseudocroup, cerebral edema)
duration of prodromes in days
number of cases in family
number of initial cases
generation number of the case
day of max. fever (days after rush)
max. fever (degree celsius)
x coordinate of house (in meters). Scaling in metres is obtained by multiplying the original coordinates by 2.5 (see details in Neal and Roberts (2004))
y coordinate of house (in meters). See also the above
description of x.loc
.
Time of prodomes (first symptoms) in days after the start of the epidemic (30 Oct 1861).
Time upon which the rash first appears.
Time of death, if available.
Time at which the infectious period of the individual is assumed to end. This unknown time is calculated as
tR[i] = min(tDEAD[i],tERU[i]+d0),
where – as in Section 3.1 of Neal and Roberts (2004) – we use d0=3.
Time at which the individual is assumed to become infectious. Actually this time is unknown, but we use
tI[i] = tPRO[i] - d1,
where d1=1 as in Neal and Roberts (2004).
The time variables describe the transitions of the individual in an Susceptible-Infectious-Revovered (SIR) model. Note that in order to avoid ties in the event times resulting from daily interval censoring, the times have been jittered uniformly within the respective day. The time point 0.5 would correspond to noon of 30 Oct 1861.
The hagelloch
"epidata"
object only retains some of
the above covariates to save space. Apart from the usual
"epidata"
event columns, hagelloch
contains a number of
extra variables representing distance- and covariate-based weights for
the force of infection. These have been computed by specifying f
and w
arguments in as.epidata
at conversion (see
the Examples below):
the number of currently infectious children in the same
household (including the child itself if it is currently
infectious), corresponding to function(u) u == 0
in f
.
the number of currently infectious children
outside the household, corresponding to function(u) u > 0
in f
.
the number of children infectious during the respective
time block and being members of class 1 and 2, respectively; but
the value is 0 if the individual of the row is not herself a
member of the respective class. See the Examples below for
the corresponding function definitions in w
.
Thanks to Peter J. Neal, University of Manchester, for providing us with these data, which he again became from Niels Becker, Australian National University. To cite the data, the main references are Pfeilsticker (1863) and Oesterle (1992).
Pfeilsticker, A. (1863). Beiträge zur Pathologie der Masern mit besonderer Berücksichtigung der statistischen Verhältnisse, M.D. Thesis, Eberhard-Karls-Universität Tübingen. Available as http://www.archive.org/details/beitrgezurpatho00pfeigoog.
Oesterle, H. (1992). Statistische Reanalyse einer Masernepidemie 1861 in Hagelloch, M.D. Thesis, Eberhard-Karls-Universitäat Tübingen.
Neal, P. J. and Roberts, G. O (2004). Statistical inference and model selection for the 1861 Hagelloch measles epidemic, Biostatistics 5(2):249-261
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | data("hagelloch")
head(hagelloch.df) # original data documented in Oesterle (1992)
head(as.data.frame(hagelloch)) # derived "epidata" object
### How the "epidata" 'hagelloch' was created from 'hagelloch.df'
stopifnot(all.equal(hagelloch,
as.epidata(
hagelloch.df, t0 = 0, tI.col = "tI", tR.col = "tR",
id.col = "PN", coords.cols = c("x.loc", "y.loc"),
f = list(
household = function(u) u == 0,
nothousehold = function(u) u > 0
),
w = list(
c1 = function (CL.i, CL.j) CL.i == "1st class" & CL.j == CL.i,
c2 = function (CL.i, CL.j) CL.i == "2nd class" & CL.j == CL.i
),
keep.cols = c("SEX", "AGE", "CL"))
))
### Basic plots produced from hagelloch.df
# Show case locations as in Neal & Roberts (different scaling) using
# the data.frame (promoted to a SpatialPointsDataFrame)
coordinates(hagelloch.df) <- c("x.loc","y.loc")
plot(hagelloch.df, xlab="x [m]", ylab="x [m]", pch=15, axes=TRUE,
cex=sqrt(multiplicity(hagelloch.df)))
# Epicurve
hist(as.numeric(hagelloch.df$tI), xlab="Time (days)", ylab="Cases", main="")
### SIR model information for population & individuals
(s <- summary(hagelloch))
plot(s, col=c("green","red","darkgray"))
stateplot(s, id=c("187"))
## Not run:
# Show a dynamic illustration of the spread of the infection
animate(hagelloch,time.spacing=0.1,legend.opts=list(x="topleft"),sleep=1/100)
## End(Not run)
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