an update on the google- funded ucar meningitis weather project abudulai adams-forgor, patricia...
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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 UNIVERSITY1
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 vaccine2. 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 epidemic2. Verify and quantify the historical relationship between weather and
meningitis3. 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
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
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
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)
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
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
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
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
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
Fitting all Weather Variables together …
Step-wise forward selection used logistic regression and cross-validation with Brier score cost function
RH VP AIRT VP/T TOTWIND NEWINDcurrent const 0 1 0 0 0 0lag1 const 0 1 0 0 0 0lag2 const 1 1 0 0 0 0
current P 0 0 0 0 0 0lag1 P 0 0 0 0 0 0lag2 P 0 0 0 0 0 0
current P/A 0 0 0 0 0 0lag1 P/A 0 0 0 0 0 0lag2 P/A 0 0 0 0 0 0
current Pr/r 0 0 0 1 0 0lag1 Pr/r 0 0 0 1 0 0lag2 Pr/r 0 0 0 0 0 0
current Pr/r/Ar 0 0 0 0 0 0lag1 Pr/r/Ar 0 0 0 0 0 0lag2 Pr/r/Ar 0 0 0 0 0 0