Modeling the interplay between climate and population dynamics of cutaneous LEISHmaniasis TO enhance (LEISHTOP),

Budget: 121000

The importance of infectious disease as a determinant (as well as an outcome) of poverty has recently become a prominent argument for international and national investment in the control of infectious disease (WHO 2001), as can be seen in the recently articulated United Nations (UN) Millennium Development Goals (MDGs) (Sachs 2002). Climate variability and land use change have an enormous impact on the spatial and temporal dynamics of a number of key infectious diseases and may yet undermine the potential for achieving the MDGs, especially in Africa, as well as in other regions.

The objective of LEISHTOP is to develop a set of tools for end-users with the capability to predict and assess the risk of propagation, re-emergence or epidemics of cutaneous leishmaniasis (CL), both in endemic and non-endemic areas where CL is currently present, seasons to a few years in advance.

The proposal is specifically addressed to understand and enhance the capacity to control leishmaniasis in northern Africa (Morocco, Tunisia) and eastern Europe (Turkey), as a way to also manage to increase predictability for this disease also in other countries of the Mediterranean region such as Spain. To this end, a unique database spanning at least the last 15 years for a number of countries (Morocco, Tunis and Turkey, covering the whole country by regions, will be made available to the project. New statistical diagnostic tools developed at IC3 that attempt to properly track local dynamics will be applied to all the data, to isolate and quantify the dependence of CL from external factors, and the interplay with internal dynamics.

At a second stage, statistical models with non-linear, non-parametricand/or time-varying regression coefficients and a suite of dynamical disease models will be developed for the different situations and countries with the aim of generating a multimodel ensemble approach to disease prediction. Highly detailed socioeconomic and demographic information will also be obtained to be tested in the human leishmaniasis models for added skill. Environmental (including climate) information will be both processed from station data and gridded products and climate predictions generated at seasonal timescales for the targeted regions. All these potential extrinsic drivers of the disease in the target areas will be assessed for uncertainty and associated errors that limit prediction skill. Field sampling of phlebotomines and bloodtest analyses will be conducted at three sites in Morocco ran by the IPasteur, Morocco. These surveys will seek to uncover the prevalence of the disease in the human and vector population, the Leishmania spp involved and the feeding preferences of the vector. The last phase of the project will include the attempt to improve prediction of CL incidence at monthly-to-seasonal timescales. The new integrated tools will be based for the first time on an innovative and unique approach that will feed on ensemble climate-based probabilistic predictions obtained from a suite of climate models (ENSEMBLES) and the EC-EARTH earth-system model ran at IC3.

LEISHTOP aims at producing the best probabilistic forecasts of disease incidence to date, and if successfully developed, will be of potential application in other areas and of generalization to other important diseases.

Starting date: 01/01/2014
Final date: 31/12/2016

Nationally funded project: Mineco