1. An update on the google- funded UCAR Meningitis Weather Project Abudulai Adams-Forgor, Patricia Akweongo, Anaïs Columbini, Vanja Dukic, 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

2. Outline: Short-term 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 better understanding of disease transmission with a focus on intervenable factors Activities: 1.Predict district level onset of high humidity, a factor that may contribute to the end of an epidemic 2.Verify and quantify the historical relationship between weather and meningitis 3.Build an information system to support vaccination decisions in real time 4.Examine human-environmental factors that influence meningitis 5.Evaluate the economic benefit of improved weather prediction

3. Humidity and meningitis In April 2009, the Kano epidemic stopped after relative humidity crossed above a 40% threshold Attack rates fell in D’jamena and Gaya when average relative humidity for the week rose above 40%. Slide from Roberto Mera

4. Modeling meningitis-weather dependence Uses a differential equation-based model of MRSA Adds physical insight into meningitis transmission Numerous assumptions: –Number of cases small compared to overall population –District population is constant –Carriage is proportional to population –Proximity to neighboring districts with cases influences the chances of having a case –Same mechanisms determine transmission and infection across belt –The disease cycle is less than two weeks –Weather in the centroid of the district is representative of district- wide weather Slide from Vanja Dukic and Tom Hopson

5. Data from Clement Lingani (via Stéphane Hugonnet) Vapor pressure (current, lagged by 1 and 2 weeks) correlated with probability of case occurrence. Other variables such as temp, wind or wind from the NE not significantly correlated with probability of cases (stochastic data set)

6. Forecasting the end of an epidemic 1.Use relationship between (current and lagged) VP and probability of epidemic : To determine which districts show historic variance in epidemic end time as predicted by vapor pressure For those districts, to predict a vapor pressure at which the epidemic typically declines 2.Predict vapor pressure using quantile regression and global models 3.Use those forecasts of vapor pressure to predict the probable end of epidemic

7. Using ‘Quantile Regression’ to better predict vapor pressure from global ensembles Without Quantile Regression: Observations outside range of ensembles With Quantile Regression: Ensembles bracket observations From Tom Hopson

8. Surveys KN district – upper East Region of Ghana Administered in preferred language Goal –100 cases 2007-present –300 age-, gender-, location-matched controls So Far –66 cases, 134 control surveys completed To Do –Geo-code all surveyed households Temperature and humidity measured hourly along a N-S transect in 20 households –10 cases, 10 controls –each site has one inside and one inside

9. Knowledge, Attitudes, and Practices Survey Administered to all cases and controls Part I: KAP –Knowledge of meningitis –Personal and household experience with meningitis –Customs and practices –Attitudes about diseases Part II: Socio-demographics –Education-literacy –Occupation (travel) –Housing (ventilation, sleeping arrangements) –Cooking, water, waste, etc. –Household assets; food security

10. Cost of Illness Survey Administered only to cases –Costs of the case Medicine, transport to and from hospital, provision of meals, cost of treatment –Costs in terms of missed work (either directly or for caregiver) –Costs due to sequelae –Limitations – recall bias in earlier cases To do –Estimate costs borne by government

13. Thank you! mhayden@ucar.edu pandya@ucar.edu hopson@ucar.edu vanja.dukic@colorado.edu

14. Fitting all Weather Variables together … Step-wise forward selection used logistic regression and cross-validation with Brier score cost function RHVPAIRTVP/TTOTWINDNEWIND current const010000 lag1 const010000 lag2 const110000 current P000000 lag1 P000000 lag2 P000000 current P/A000000 lag1 P/A000000 lag2 P/A000000 current Pr/r000100 lag1 Pr/r000100 lag2 Pr/r000000 current Pr/r/Ar000000 lag1 Pr/r/Ar000000 lag2 Pr/r/Ar000000