WP2 - Dynamic Health Models

• 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

Main participants in each task are listed.

Task 2.1a: Evaluate current dynamic Liverpool Malaria Model (LMM) and its semi-dynamic versions in Ghana, Senegal and Malawi using observational and reanalysis database to drive model in test locations. (UNILIV, UOC, IC3, CSE, IPD, UCAD, KNUST, UNIMA, UP)

This task will involve testing the malaria model with both on modelled reanalysis and other gridded data sets and observed data for selected regions and comparing with known disease records to modify parameter settings in the model and to investigate the significance of climate variability in malaria incidence in these regions.

Task 2.1b: Generalize the current dynamic Liverpool Malaria Model for any simple single vector, single host vector borne disease.  (UNILIV, IC3)

The initial generalized model will be for malaria to check the generalized model against the original model.  The main task here will be re-writing the model in NCL (NCAR Command Language http://www.ncl.ucar.edu/ ) scripting language or inserting the model within an  NCL wrapper to make it much more portable and much easier to use with climate model databases.  The second step will be to develop the model to run as a simple Rift Valley Fever model for a single animal host.

Task 2.1c To develop the malaria model - for two or multi-host diseases. This will only be developed if Task 2.1b is fully successful and this new task carries elements of risk as the ability to develop this model will depend on its epidemiology, the availability of data, and how much we know about the links to weather.  (UNILIV, IC3)

Task 2.1d: Test new generalized model with Rift Valley Fever and malaria data (and possibly tick borne diseases) from database (task 1.2c)  (UNILIV, CSE, KNUST, IPD, UCAD, UNIMA, UP, ILRI)

Task 2.1e For regions where there are sufficient disease data, recovered by this project, other modelling approaches e.g. statistical models will be compared with the performance of the dynamical modelling approaches. (UNILIV, CSE, KNUST, IPD, UNIMA, UP, ILRI, UCAD)

D2.1.a Report on dynamic malaria model runs for regions of interest and verification against data sets. M15 (UNILIV, UOC, IC3, CSE, IPD, UCAD, KNUST, UNIMA, UP)

D2.1a Report on dynamic malaria model runs for regions of interest and verificat

D2.1.b Report on the performance of the dynamic and semi-dynamical modelling approaches for selected diseases for regions in Africa. M28 (UNILIV, UOC, IC3, CSE, IPD, UCAD, KNUST, UNIMA, UP)

D2.1b Report on the performance of the dynamic and semi-dynamical modelling appr

M2.1.a Generalized single host, single vector dynamic vector-pathogen-host model. M18 (UNILIV)

M2.1.a – Generalised single host, single vector dynamic vector-pathogen-host mod