Memo
To:
Tetsuo NAKAZAWA
Chief, World Weather Research Division
Research Department
From:
Rajul PANDYA, UCAR
Thomas HOPSON, NCAR
Benjamin LAMPTEY, Regional Maritime University
Re:
Request to use TIGGE Data in near real time for meningitis prediction pilot study
Date: 27 September 2011
Objectives of the project
The project aims to demonstrate that accurate weather forecasts can improve the
management of meningitis in the Sahel region of Africa.
Meningitis epidemics occur every 7-10 years in parts of the Sahel, and always during
the dry season. Using past epidemiological and weather data, we’ve demonstrated
that a number of meteorological variables, including surface relative humidity and
temperature, influence the number of cases of meningitis in a give region. Further,
we’ve developed a statistical model that can be used within a season to predict both
the number of future cases of meningitis and the date of the end of the epidemic
season if the inputs include the number of current cases as well as recent, current,
and predicted values of temperature and relative humidity (and to a lesser extent,
wind speed). We would like to use TIGGE data during this upcoming dry season to
predict the number of cases of meningitis as accurately and as timely as possible.
If the THORPEX International Project Office feels our project could potentially serve
as a THORPEX program demonstration project, we would certainly welcome this
opportunity.
Specific utilization of TIGGE ensemble forecast data
Our statistical analysis was carried out using reanalysis (deterministic) products to
define the relationships between weather variables and meningitis likelihood. In
particular, these relationships quantify the probability of exceeding 0, 5, 10, and 15
reported cases per 100,000 individuals in districts across the meningitis belt, as a
function of changes in weather. The solid line in the right panel in Figure 1, in
particular, shows how the likelihood (y-axis) of exceeding 10cases/100,000 changes
strictly as a function of relative humidity (x-axis); the solid horizontal line shows the
“climatological” likelihood, of about 2% if one did not have weather information
(note that other weather variables also have an impact, not shown here).
With these statistical relationships, actual forecasts of meningitis likelihood are
produced by running individual TIGGE weather forecast ensemble members into
these meningitis statistical models. For each day, the TIGGE forecasts are temporally
averaged into 1week- and 2week-ahead forecasts (from 6hr – 7days, and 7-days –
14days, respectively). Due to biological and administrative lags, a 1week-ahead
weather forecast translates into a 3week-ahead meningitis forecast. In Figure 2 we
show an example of using only a 1week-ahead ECMWF ensemble mean relative
humidity (RH) forecast initialized on March 27th 2011 00Z to produce a 3week
ahead meningitis risk of exceeding 10cases/100,000 across the meningitis belt
(bottom panel). The benefits of utilizing the full TIGGE set of ensembles is that, after
calibration, we have a set of individual ensembles that represent equally-likely
weather scenarios and thus a range of meningitis outcomes that thus is an indicator
of forecast uncertainty.
In addition to producing meningitis belt-wide forecasts of exceeding thresholds of
cases/100,000 1week, 2weeks, 3weeks, and 4weeks ahead, we also produce end-ofseason meningitis forecasts. Here, we forecast the date beyond which the risks of an
outbreak are lower than the climatological risk, and remain lower over a given
number of weeks (we currently use 6weeks). Here, we also explicitly use the
implied uncertainty information contained in the range of TIGGE ensemble
forecasts. This is accomplished by calculating the accumulative probability of not
reaching epidemic thresholds for each of the next 6 weeks, done by randomlysampling (e.g. bootstrapping) the calibrated TIGGE ensemble-based meningitis
outbreak likelihoods 1week to 4weeks in advance, and then historical reanalysis for
the last 2 weeks (for a total of 6weeks), generating a range of possible end-of-season
dates for each location. As a result, we continually monitor and update the date at
which point outbreak risk is sufficiently low enough that health managers and
vaccine dissemination programs could assume regional safety from a possible
outbreak.
Relation to the commercial grants/sponsors
The work is sponsored by google.org, and the results of the trial will be made
available to them, as well as an analysis of the socio-economic, e.g. health, benefit.
We will also make a prototype forecast system/data integration system available,
but not the underlying data. Finally, our prototype system will be accessible and
open to any interested party. As part of our testing of this system, we would like to
work with ACMAD, the World Health Organization, and others to explore whether
this can work in an African context.
Specific value of TIGGE ensemble forecast data to sponsors
In relation to the TIGGE products, note that our prototype meningitis forecast
system explicitly uses the TIGGE weather forecast ensembles to provide both the
most-likely scenario and the associated degree of uncertainty in meningitis
outbreak likelihoods and expected end-of-season dates, as explained in the previous
section.
Reason to request relaxation of the 48h delay
Our goal is to make the forecasts available to vaccination decision makers in a trial
mode so they can apply it and provide feedback about its utility. The most accurate
possible and timely weather data would provide the most useful trial and the most
relevant feedback. Since the ultimate use of the meningitis forecasts depends on
their perceived value in this trial, we’d like to have the most valuable, accurate, and
timely input data.
We request the relaxation of the 48h delay primarily because our pilot program is
designed to provide to health care and monitoring officials the most up-to-date
probabilities of exceeding meningitis outbreak thresholds (and end-of-season dates)
across the meningitis belt during the upcoming dry season (nominal start time in
November-December). Although we believe our information would still have utility
if it were 2day delayed in its release, we also believe our information could show
even more potential utility if these delays were reduced, and hopefully present an
even stronger case for the utility of ensemble medium-range weather products in
this particular application.
Because in general, ensemble weather forecast information needs to be calibrated,
we use the past year’s ensemble data from 8 of the TIGGE-reporting weather centers
as representative hindcasts to train our statistical post-processing algorithms
(based on quantile regression). The post-processing is applied individually for each
forecast lead-time (6hr, 12hr, …, 336hr), using reanalysis as “truth”. We show an
example where we have used forecast information from March 2011 from ECMWF
and the NCAR archive in Figure 2 below.
Which data are requested
Surface data, particularly temperature and humidity, as well as wind speed and
precipitation (the latter as possible surrogates for atmospheric dust content) are
requested. Please note that from these fields, we extract useful information from the
ensemble data primarily as discussed in the first section above: we utilize the
ensemble forecast data to generate the most-likely scenario of meningitis outbreak
over the meningitis belt, as well as provide the range of uncertainty in these
forecasts. In, particular, this is explicitly represented in the way we calculate our
end-of-meningitis-outbreak season dates, where we bootstrap over our calibrated
TIGGE-generated ensemble set to produce a range of possible end-of-season dates,
which are continually refined and improved as the meningitis season progresses.
Duration of data access (up to 1 year, extendable)
We would like to use the data from now until the end of the dry season, late June
2012, with a possible extension into the December 2012 - June 2013 dry season.
Any contacts made with data centre / provider(s)
NCAR would be the natural place to get the data, because we are also located at
NCAR and have been using the archive located here. Doug Schuster at NCAR has
been assisting us. We have not contacted other data or forecast centres with respect
to medium-range time scale data access for this project.
Contact points
Raj Pandya, UCAR, pandya@ucar.edu, +1 303 497 2650
Thomas Hopson, NCAR, hopson@ucar.edu +1 303 497 2706
Benjamin Lamptey, Regional Maritime University, bllamptey@gmail.com,
+233 265 121 750
Figure 1: World Health Organization districts across part of the meningitis belt
shown in the left-most panel, with the meningitis epidemic (here, as defined by 10
cases/100,000) likelihood dependence on relative humidity shown in the rightmost
panel.
Figure 2: Top panel shows the one-week ahead average relative humidity (RH)
ensemble mean forecast over the meningitis belt, initialized on March 27, 2011. The
bottom panel shows how this RH forecast converts to a 3-week ahead meningitis
forecast across the belt, represented as probability of exceeding 10 cases/100,000
reported over that week.