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-of-season 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 randomly-sampling (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.

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