Short-term weather forecasts to help allocate meninigitis vaccine Abudulai Adams-Forgor, Anaïs Columbini, Mary Hayden, Abraham Hodgson, Thomas Hopson, Benjamin Lamptey, Jeff Lazo, Roberto Mera, Raj Pandya, Jennie Rice, Fred Semazzi, Madeleine Thomson, Sylwia Trazka, Tom Warner, Tom Yoksas NC STATE UNIVERSITY 1

Outline: Short weather forecasts to help allocate scarce Meningitis vaccine Project goals: 1.Minimize meningitis incidence by providing 1-14 day weather forecasts to target dissemination of scarce vaccine 2.Contribute to general understanding of the disease (e.g. economic impact, factors that increase risk) which can contribute to proactive strategies Activities: 1.Understand and model the current vaccination decision process 2.Build an information system to support decisions 3.Integrate relevant weather forecasts into the information system 4.Examine other factors that influence meningitis 5.Evaluate the benefit of improved weather prediction

Health Care Costs Vaccination Costs Value New Weather Research/ Modeling Research Costs Weather Forecast Population Immunity Social Factors Active Serogroup Vaccine Effectiveness Population Size Size of Outbreak Morbidity (ST & LT) Mortality Attack Rate Respiratory Health % Vaccinated Onset of Wet Season Dry Season Weather Morbidity Rate Fatality Rate Carriage Population Movement = Decision= Uncertainty/Data= Decision Value Influence Diagram # of Early Cases Meningitis Vaccination Program (type of vaccine, timing, scope, etc.) Status of Neighbors

Activity 4) Survey purpose: better understand socio-ecological determinants of transmission and economic impacts at household level Explore generalizability and transferability of methods across meningitis belt Collaboration with Navrango Health Research Centre Survey Approach …

Why Ghana? Extends MERIT partnership to Ghana Amazing Epidemiological and Demographic Data Local Expertise –Abraham Hodgson: cooking indoors increases susceptibility to Meningitis –Abudulai Adams-Forgor: Outbreaks of Meningococcal Meningitis are proceeded by outbreaks of Pneumococcal Meningitis Navrongo Health Research Centre Main Entrance (above) and Region of surveillance (below) Navrongo

Survey Status: KN district – upper East Region of Ghana Survey ongoing (2 weeks) 400 households 100 cases 2005-present, rest controls Administered to head of household in preferred language Households geo-coded for mapping to met data Household N-S transect will have 20 pairs of “HOBOS” (T and Td one in one out); 10 cases, 10 controls Preliminary results MERIT Nov 2010 meeting Pretested in Feb, couple hurdles, hoped to start during meningitis season –(hurdles Ghanaian IRB (instit rev board), US & Ghanaian customs for GPS’s )

Survey Major Topics: KAP – Knowledge, Attitudes, and Practices (everyone) –Knowledge of Meningitis –Personal and Family Experience with Meningitis –Customs and Practices –Attitudes about diseases Cost of Illness (only cases – or parents) –Identification of the Case –Costs of the case –Costs due to After-Effects Socio-demographics (everybody) –Education-literacy (health behavior) –Occupation (=> travel) –Housing (ventilation, sleeping arrangements) –Cooking, Water, Waste, Etc. (Abraham’s) –Food Security (immune system, time to seek treatment => cost of illness) –Etc.

Activity 2: Build an information system that can support decision processes Vision: an integrated information system that is open and transparent and locally-owned (e.g. ACMAD) can help anticipate future epidemics and help mitigate existing –Work so Far: a prototype architecture that can accept multiple kinds of geo- referenced data and integrate with googleEarth Demo system that integrates meteorological data and epidemiological data for –Yet to do: Refine the prototype with decision makers integrate new data types (soil, moisture, etc)

A role for weather forecasts Meningitis epidemics are observed to occur in the dust season and end with the onset of the rains –Can we predict the onset of the rains with enough spatial resolution ad enough lead time so that decision makers can prioritize allocation of vaccines to those districts likely to remain dry. 11

Possible simple model (MRSA) – Susceptible-Colonized-Infected reservoirs (over?-) simplifications: assume can develop a meningitis model that applies for all assume homogenous mixing over whole district same model applied to all available districts S t, C t, I t represent numbers of people in each district β coefficients depend on many factors Thanks to Vanja Dukic

Possible simple model (cont) - treat I t as sum of previous 2 weeks of cases (after 2 weeks, no longer infected) => I t = I t-1 + I t-2 weekly time increment, so model everything as weekly averages (met variables) Simplifying to: only observations are I t (actually positive change in I t ), and Population P => model last equation only => treat S t and C t as roughly fixed ratios of total population across all countries (a)(b)(c) (d) (a) (b) (c)

Possible simple model (cont) - => Population dependence … look at accumulated # of cases over vs P over all districts Correlation 0.22

Possible simple model (cont) - => Population per Area dependence … look at accumulated # of cases over over all districts Correlation 0.02 n A + m B → C + D

Logistic Regression for probability of occurrence -

… followed by Quantile Regression (QR) for severity (cases) … E.g. Our application Fitting T quantiles using QR conditioned on: 1)Ranked forecast ens 2)ensemble mean 3)ensemble median 4) ensemble stdev 5) Persistence

Using ‘Quantile Regression’ to better calibrate ensembles Without Quantile Regression: Observations outside range of ensembles With Quantile Regression: Ensembles bracket observations From Tom Hopson

Forecasting: Thorpex-Tigge “grand ensemble” -

Forecast “calibration” or “post- processing” Probability calibration Flow rate [m 3 /s] Probability Post-processing has corrected: the “on average” bias as well as under-representation of the 2nd moment of the empirical forecast PDF (i.e. corrected its “dispersion” or “spread”) “spread” or “dispersion” “bias” obs Forecast PDF Forecast PDF Flow rate [m 3 /s] Our approach: under-utilized “quantile regression” approach probability distribution function “means what it says” daily variation in the ensemble dispersion directly relate to changes in forecast skill => informative ensemble skill-spread relationship

Calibration Procedure For each quantile: 1)Perform a “climatological” fit to the data 2)Starting with full regressor set, iteratively select best subset using “step-wise cross-validation” –Fitting done using QR –Selection done by: a)Minimizing QR cost function b)Satisfying the binomial distribution 2nd pass: segregate forecasts into differing ranges of ensemble dispersion, and refit models => ensure ensemble has skill-spread information Probability Temperature [K]  obs Forecast PDF T [K] Time Forecastsobserved Regressors for each quantile: 1) ranked forecast ensemble member 2) ens mean 4) ens stdev 5) persistence

Activity 3: Integrate relevant weather forecasts into the information System What we’ve done: –Preliminary identification of relevant weather variables –Improved prediction for humidity and precipitation High-resolution modeling (please see Mera et al. Poster) Ensemble methods Yet to be done: –Complete analysis of Navrongo district epidemiological and meteorological records (Dr. Forgor) –Quantifying the weather-meningitis connection through a) statistical areal data models, and b) point-process models

New Weather Research/ Modeling Weather Forecast = Decision= Uncertainty/Data= Decision Value Influence Diagram of Vaccination Program: Integrating Weather # of Early Cases Meningitis Vaccination Program (type of vaccine, timing, scope, etc.) Status of Neighbors % Vaccinated

Questions about weather/health relationship Which is more important in ending an epidemic – sustained high-humidity or rain events? How does the disease work? –Consensus?: irritation of the pharynx that allows the bacteria (which may already be there) to enter the body Consistent with dust, cooking smoke, and pneumococcal as risk factors. What is the trigger for the end of the epidemic? –Is it physical changes (e.g. rains remove and suppress dust) –Is it the impact of humidity on the health of pharynx –Is it behavior change associated with rain?

Activity 4: Examine other factors that influence meningitis What we’ve done: –developed a draft “knowledge, attitudes and practices” survey to be administered in Navrongo, which investigates factors like Use of traditional practices to treat meningitis Cooking practices and living conditions Local environmental conditions Demographic and migration patterns Yet to do: –Finish IRB approval –Survey to be administered this and next year to ~200 cases and 400 case-matched controls –Install data loggers in 4 regions of K-N district to provide one year of continuous data on humidity (indoor and outdoor)

New Weather Research/ Modeling Weather Forecast Population Immunity Social Factors Active Serogroup Vaccine Effectiveness Population Size Size of Outbreak Morbidity (ST & LT) Mortality Attack Rate Respiratory Health % Vaccinated Onset of Wet Season Dry Season Weather Morbidity Rate Fatality Rate Carriage Population Movement = Decision= Uncertainty/Data= Decision Value Influence Diagram: Other Factors # of Early Cases Meningitis Vaccination Program (type of vaccine, timing, scope, etc.) Status of Neighbors

Activity 5: Evaluate the benefit of improved weather prediction What we’ve done –Developed a pilot cost-of-illness survey to measure household level impacts Designed to complement Colombini’s work in Burkina, with her help To be administered to ~200 cases this year Yet to do –Estimate the decrease in meningitis from improved decisions –Develop a Willingness-to-Pay (WTP) scenarios for next year –Do a regression analysis on the NHRC socio-economic data to look for impact of past meningitis cases on current income –Analyze the economics of a proactive vaccination campaign (Forgor)