Weather and meningitis in Ghana Benjamin Lamptey,PhD (Meteorologist) Regional Maritime University, Accra Ghana

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 2 Slide from Mary Hayden

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

Humidity and meningitis In April 2009, the Kano epidemic stopped after relative humidity crossed above a 40% threshold Attack rates fell in Djamena 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

Map of Ghana

Ghana (I) UCAR currently has a statistical model that makes use of THORPEX Interactive Grand Global Ensemble (TIGGE) data to predict meningitis cases within the season (summary from earlier slides) UCAR team currently using the Kasena-Nankana district in Navrongo a city in the Upper East Region of Ghana for pilot studies. Previous work on weather and meningitis had been done by some researchers at the Navrongo Health Research Institute (NHRI). So at least 10 years of district level data available.

Ghana (II) Customize WRF: The Weather Research and Forecast (WRF) model is to be installed and run within Ghana to generate higher resolution data than the coarser TIGGE data. The model must first be customized for the region. Hindcast: Use WRF to simulate a retrospective season (e.g or 2011) for the meningitis belt and use that high resolution data and meningitis data to look at the relationship between meningitis and weather variables Semi-operational WRF: The WRF output will enable forecasts of weather and meningitis outbreak likelihoods (as WRF output will be used as input to the statistical model) to be generated for use by WHO and other interested institutions

Climate Change related work What is the probability of a meningitis outbreak in the future compared to current conditions. Does the probability increase or decrease and by how much? Given the mortality rate of the disease, what is the change in the number of deaths as the climate across the meningitis belt changes?

Way forward Have infrastructure to access satellite dust products, dust model output Explore relation between dust and meningitis using available model data as well as observation. Start with testing various hypothesis Eventually have a dust model – run as stand alone – Coupled to WRF to explore what value can be added with regards to dust prediction? Couple an air quality model to WRF (e.g. coupled WRF-CMAQ system) Investigate the contribution of dust to meningitis outbreak through modeling Analyze CCSM data and MERRA reanalysis data for climate change work

Thank you for your attention