Description Usage Arguments Details Value References Examples
This function fits a FluMoDL object. This is a distributed lag nonlinear model (DLNM), of quasipoisson family and with log link, which estimates the association between mortality (as outcome) and daily mean temperatures and typespecific influenza incidence proxies (as exposures), adjusted for covariates.
1 2  fitFluMoDL(deaths, temp, dates, proxyH1, proxyH3, proxyB, yearweek,
proxyRSV = NULL, smooth = TRUE, periodic = TRUE)

deaths 
A vector of daily deaths, of equal length to argument 
temp 
A vector of daily mean temperatures, of equal length to argument 
dates 
A vector of dates (of class 
proxyH1 
A vector of weekly influenza A(H1N1)pdm09 incidence proxies, of equal
length to argument 
proxyH3 
A vector of weekly influenza A(H3N2) incidence proxies, of equal
length to argument 
proxyB 
A vector of weekly influenza B incidence proxies, of equal
length to argument 
yearweek 
An integer vector of weeks, in yyyyww format 
proxyRSV 
An optional vector of weekly RSV incidence proxies, of equal
length to argument 
smooth 

periodic 
Should a periodic Bspline term be included in the model?
Defaults to 
Objects of class 'FluMoDL' contain the model, the associated data, estimates of the predicted
associations and other information.
These objects can be further used as input for function attrMort
, to calculate
influenzaattributable and temperatureattributable mortalities for any period in the data
(and any temperature range). Methods print()
, coef()
and vcov()
have
been defined for objects of class 'FluMoDL' (see below),
and also summary()
.
FluMoDL uses a DLNM with the daily number of deaths as the outcome. Covariates include the following:
A crossbasis matrix
for temperature. The exposureresponse
relationship is modelled with a quadratic Bspline with internal knots placed at the
10th, 75th and 90th percentile of the temperatures distribution. The lagresponse
relationship is modelled with a natural cubic spline with three internal knots
equidistant in the log scale.
Three crossbasis matrices
for influenza incidence proxies for
each type/subtype: A(H1N1)pdm09, A(H3N2) and B. These normally are equal to a
sentinel InfluenzaLike Illness (ILI) rate, times the laboratory swab samples Percentage
Positive (
implying an approximately constant case fatality ratio for each influenza type. The
lagresponse relationship is specified as above (for temperature).
A periodic Bspline term to model seasonality, with three equidistant internal
knots according to day of the year. Can optionally be suppressed by setting argument
periodic
to FALSE
.
A linear trend, and a factor variable for day of the week.
Optionally, a crossbasis matrix
for an RSV
incidence proxy, with specification identical to those for influenza. If given,
it will be included in the model and output, and it will be possible to calculate
mortality attributable to RSV with attrMort
. This is an experimental feature;
it might be removed in the future.
An object of class 'FluMoDL'. This is a list containing the following elements:
A data.frame
with the data used to fit the model. Rows correspond to
days in argument dates
. The columns are named: yearweek
, dates
,
deaths
, temp
, (for temperature), proxyH1
, proxyH3
, proxyB
,
t
(linear trend, with values 1:nrow(m$data)
), doy
(day of year,
use to calculate the periodic Bspline term to model seasonality) and dow
(day of
the week). Also column proxyRSV
if the relevant argument is provided.
The fitted model; an object of class glm
and of 'quasipoisson' family
with log link.
A list with names 'temp', 'proxyH1', 'proxyH3' and 'proxyB' (and proxyRSV,
if provided in the function arguments), containing the
crossbasis matrices that are used as exposures in the model. See crossbasis
.
The Minimum Mortality Point, i.e. the temperature where mortality is lowest.
A list with names 'temp', 'proxyH1', 'proxyH3' and 'proxyB' (and 'proxyRSV'
if provided in the function arguments), containing
predictions (in the form of crosspred
objects) for each exposure.
These can be plotted in both the exposureresponse and lagresponse dimensions, see
crosspred
, plot.crosspred
and the examples below.
This element is NULL when creating the object, but can receive a
summary.FluMoDL
object that contains Best Linear Unbiased Predictor
(BLUP) coefficients, to be used when estimating attributable mortality. Can be
retrieved or set with the blup.FluMoDL
method
Objects of class 'FluMoDL' have methods print()
, coef()
and vcov()
.
coef()
returns a list of numeric vectors, with names 'proxyH1', 'proxyH3'
and 'proxyB' (and 'proxyRSV' if provided in the function arguments), containing the model
coefficients for these crossbasis terms. Similarly vcov()
returns a list
of variancecovariance matrices for the same terms.
Lytras T, Pantavou K, Mouratidou E, Tsiodras S. Mortality attributable to seasonal influenza in Greece, 2013 to 2017: variation by type/subtype and age, and a possible harvesting effect. Euro Surveill. 2019;24(14):pii=1800118 (PubMed)
Gasparrini A, Armstrong B, Kenward MG. Distributed lag nonlinear models. Stat Med 2010;29(21):2224–34.
Gasparrini A, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 2015 Jul 25;386(9991):369–75.
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  data(greece) # Use example surveillance data from Greece
m < with(greece, fitFluMoDL(deaths = daily$deaths,
temp = daily$temp, dates = daily$date,
proxyH1 = weekly$ILI * weekly$ppH1,
proxyH3 = weekly$ILI * weekly$ppH3,
proxyB = weekly$ILI * weekly$ppB,
yearweek = weekly$yearweek))
m
# Plot the association between A(H1N1)pdm09 activity and mortality
# and the overall temperaturemortality association:
plot(m$pred$proxyH1, "overall")
plot(m$pred$temp, "overall")
# Add the Minimum Mortality Point to the plot:
abline(v=m$MMP)
# Check the lagresponse dimension for the A(H1N1)pdm09  mortality
# association, for all proxy values, and for an indicative value of 30.
plot(m$pred$proxyH1) # Produces a 3D plot, see ?plot.crosspred
plot(m$pred$proxyH1, var=30)
# Have a look at the data associated with this FluMoDL:
str(m$data)
tail(m$data)

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