In January 2008 connectivity between the Research and Education Communities in Europe and Sub-Saharan Africa was established with a 1 Gbps between the UbuntuNet Alliance and GÉANT.
Andrew Morse, University of Liverpool (coordinator)
Main/Partner organisations
African participants
University of Malawi - College of Medicine and Polytechnic - Malawi Centre de Suivi Ecologique, Dakar - Senegal University Cheikh Anta Diop, Dakar - Senegal International Livestock Research Institute - Kenya Kwame Nkrumah University of Science and Technology (KNUST) - Ghana University of Pretoria - South Africa Institut Pasteur de Dakar - Senegal
EU participants
University of Liverpool - UK European Centre for Medium-Range Weather Forecasting - UK Consejo Superior de Investigaciones Cientificas - Spain Fundació Privada Institut Català de Ciències del Clima - Spain Abdus Salam International Centre for Theoretical Physics - Italy Universitaet zu Koeln - Germany
Contact Details
A.P.Morse [AT] liverpool.ac.uk
Objectives and short summary of activities
Further understanding of how climate modelling can help predict the spread of infectious diseases in Africa.
One of the most dramatic and immediate impacts of climate variation is that on disease, especially the vector-borne diseases that disproportionately affect the poorest people in Africa. Although we can clearly see that, for example, an El Nino event triggers Rift Valley Fever epidemics, we remain poor at understanding why particular areas are vulnerable and how this will change in coming decades, since climate change is likely to cause entirely new global disease distributions. This applies to most vector-borne diseases. At the same time, we do not know currently the limit of predictability of the specific climate drivers for vector-borne disease using state-of-the-art seasonal forecast models, and how to best use these to produce skilful infection-rate predictions on seasonal timescales. The QWeCI project thus aims to understand at a more fundamental level the climate drivers of the vector-borne diseases of malaria, Rift Valley Fever, and certain tick-borne diseases, which all have major human and livestock health and implications in Africa, in order to assist with their short-term management and make projections of their future likely impacts. QWeCI will develop and test the methods and technology required for an integrated decision support framework for health impacts of climate and weather. Uniquely, QWeCI will bring together the best in world integrated weather climate forecasting systems with health impacts modelling and climate change research groups in order to build an end-to-end seamless integration of climate and weather information for the quantification and prediction of climate and weather on health impacts in Africa.
Target/Beneficiary community
Poor communities in the target regions in Malawi, Ghana and Senegal. For systems developed here to be adopted and used in other similar regions
Technology used, standards and services employed
WP1.1 - Disease Database Objectives • To complete the African dimension of the Liverpool University (UNILIV) Pathogen database by adding records of human and animal pathogens that occur in Africa from published literature. • Expanding the database (in conjunction with the ERA NET Env Health project on ‘risk assessment of climate change on human health’ funded project) by the addition of vector species and known climate and/or environmental sensitivities in an African context. • Entering into the database published relationships between climate and diseases, details of the pilot experiment and details of their climate pathogen-vector relationships. • To develop an interface for African registered researcher interrogation of the database and for addition to the database. • To map known pathogen-climate relationships for use as a visualization tool for possible areas of risk. • To develop and display via a website mapped outputs from the whole project e.g. current climate sensitivities of disease and projection of future distributions; for regions in Africa and for selected diseases pan-African plots will be produced.
WP2.1 Development of dynamic disease models Objectives • To develop a more generalized host-pathogen-vector dynamic model from an existing dynamic malaria model for selected one-host vector-borne diseases to run directly with climate data sets on centralized climate data archives. Initial model tests will be undertaken running the generalized model as a malaria model for testing • To develop more complex models from the above generalized model for diseases with more complex transmission cycles e.g. Rift Valley Fever, tick-borne diseases etc; but falling back to simpler semi-dynamic modelling approaches where data sets allow the testing and the development of these modelling systems • To run the existing dynamic malaria model to test and validate against data sets provided through the project, to confirm it can be run in different countries and to test parameter settings • To use data from the project and other sources to develop parameter setting for the model for set diseases and to develop and test more complex dynamic or semi-dynamic approaches. • In regions where significant disease data exist the dynamic model will be run against other modelling approaches
WP3.1 Downscaled and calibrated seamless seasonal atmospheric forecasts Objectives • To develop a seamless prediction system from medium-range to seasonal scales. • Provide the regional seasonal predictions necessary for the other tasks of the project applying state-of-the-art methodologies and using existing global simulations (mainly from ECMWF ) complemented by the regional simulations performed by University of Pretoria (UP), focusing on southern Africa. • Production of regional seasonal predictions as a multi-model multi-downscaling unified approach, including also an estimation of the uncertainty.
WP3.2: Seamless decadal predictions and projections Objectives
Describe the characteristics of African temperature and precipitation in interannual and decadal time scales and assess and improve the state-of-the-art forecast quality with dynamical and statistical models.
WP4.1: Seamless climate-disease model integrations Objectives • To integrate a dynamic disease model within a up to seasonal time scales within a seamless ensemble prediction system using monthly and seasonal hindcasts • To move the integrated modeling system into the experimental decadal ensemble prediction system hindcasts and projections • To work on the initial stages of developing a multi agent based modeling system for use in Senegal
WP5.1: Development of integrated information and decision support systems Objectives • Identification of requirements of stakeholders and decision makers with regard to health impacts of climate and weather in the low income countries of Ghana, Senegal, and Malawi. • Development of a multi agency system based on the monthly to seasonal and decadal climate disease simulations. • Formation of a Disease Early Warning System based on seasonal forecasts. • Definition of a Monitoring Tool (MT) for Standing Water in Senegal based on remotely sensed data sources. • Development of a Disease Operation System that targets on the epidemiology of malaria and Rift Valley Fever (RVF) in Senegal. WP5.2 – Ghana pilot project: peri-urban malaria Objectives • Entomological and parasitological survey of malaria transmission in rural, peri-urban and urban settings of Kumasi, Ghana. • Characterization of mosquito larval and adult habitats in the different study areas. • Analysis and mapping of possible malaria risk areas using GPS and GIS tools. • Assessment of the impact of climate variability (rainfall patterns, temperature and relative humidity) and human ecological and environmental factors (e.g. land use, buildings, road constructions, topography, vegetation cover etc.) on the incidence of malaria in the target groups and different settings based on statistical methods. • Validation of the Liverpool Malaria Model (LMM) and improvement of the LMM with WP2.1 • Validation of single point location Ensemble Prediction System (EPS) seasonal malaria forecasts performed in WP 4.1. • Development of Decision Support System (in collaboration with WP5.1) that serves as an early warning system and that assesses the effectiveness of intervention and control measures.
WP5.3 Senegal pilot project: RVF and malaria Objectives • To identify more precisely the roles of meteorological and environmental variables in patterns and diffusion of Rift Valley Fever (RVF) and malaria in the Sahelian bio-geographic domain of Senegal • To evaluate and to quantify the impact of rainfall and other climatic factors (temperature, relative humidity, wind (direction and velocity)) on the dynamics of malaria and RVF vector populations • To characterize the impacts of the intra-seasonal variability in the West African monsoon on malaria and RVF (detection and forecast dry spell and extreme events, climate model downscaling) • To examine the impact of rainfall, hydrology and pond dynamics on malaria and RVF vector populations • To document additional pond hydrology at Barkedji and their impact on vectors population dynamic (using in situ and remote sensing data) • To validate the dynamic malaria and a new model for RVF in Senegal with a focus on the role of climate and hydrologic parameters at different timescales (rainfall estimation by satellite or directly infrared brightness temperature at different thresholds will also be tested) • To quantify the climate change using IPCC scenarios and its impact on malaria and RVF transmission and diffusion • To validate hazard (dynamic map of mosquitoes density), vulnerability and risk maps developed though the EU FP6 AMMA project • To establish a regional set of tools for these two diseases (RVF and malaria) using weather and climate forecasts for predicting timing and Health Early Warning System (HEWS).
WP5.4 – Malawi pilot project: disease risk dissemination by long-range WiFi technology Objectives
The Objectives of this work package are to: • Determine and implement hardware modification and software requirements to collect incidence data from Zomba and Mangochi near-real time and log on a central database centrally at Blantyre using long-range WiFi. • Determine forecast format suited to end-user needs in Zomba and Mangochi clinics. • Disseminate malaria forecasts using the low-cost long-range WiFi network in place and provide training on their use. • Monitor the use of these forecasts and determine potential improvements; and • Possibly extend the forecast suite to include RVF and tick-borne diseases if circumstances allow.
Research activities carried and out and scientific data generated
Develop integrated climate model-health model early warning systems to run over time scales of months to decades. For such systems, or parts of them, to be used in decision support systems at national and regional levels.
Principal outcomes and documentation (plus link to case studies)
Expected outputs 1. Climate health relations: disease database, atmospheric database, climate-disease associations 2. Development of dynamic disease models 3. Seamless atmospheric integrations: downscaled and calibrated seamless seasonal atmospheric forecasts, seamless decadal predictions and projections 4. Coupled climate-health projections: seamless climate-disease model integrations 5. Integrated decision support systems in three pilot projects: Ghana (Peri-urban malaria), Senegal (Rift Valley Fever and malaria), Malawi (disease risk dissemination by long range WiFi technology) 6. Dissemination, training and assessment
