Bizimana, Jean Pierre

Chirombo, James

Colon-Gonzalez, Felipe

Di Giuseppe, Francesca

Namanya, Didacus

Tompkins, Adrian M

Malaria Journal

PubMed

Adrian M Tompkins

Francesca Di Giuseppe

Felipe Colon-Gonzalez

James Chirombo

Jean Pierre Bizimana

Didacus Namanya

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A dynamical climate-driven malaria early warning system evaluated in Uganda, Rwanda and Malawi

LSHTM Research Online

POSTER PRESENTATION Open AccessA dynamical climate-driven malaria early warningsystem evaluated in Uganda, Rwanda and MalawiAdrian M Tompkins1*, Francesca Di Giuseppe2, Felipe Colon-Gonzalez1, James Chirombo3,4, Jean Pierre Bizimana5,Didacus Namanya6From Challanges in malaria research: Core science and innovationOxford, UK. 22-24 September 2014BackgroundMalaria is a climate-sensitive disease with a significantsocio-economic impact. As monthly and seasonal dyna-mical climate prediction systems have improved theirskill in the tropics over recent years, there is now thepotential to use these forecasts to drive dynamicalmalaria modelling systems to provide early warnings ofclimate-related transmission hazards in holo and hyperendemic settings.Materials and methodsA new pilot dynamical malaria prediction system wasintroduced. Multiple temperature and precipitation fore-casts from the ECMWF monthly and seasonal predictionsystems were used to drive the spatially explicit, dynamicalmalaria model VECTRI that accounts for population den-sity and climate to produce forecasts of up to 4 monthsahead. The malaria forecasts were started from realisticinitial conditions derived from climate observations. Theparameter predicted is the logarithm of the entomologicalinoculation rate (EIR). Forecasts are made on a grid meshwith a spatial resolution of 25km, but are then aggregatedat the administrative district level, and normalized to beevaluated using normalized district level crude incidencedata or sentinel site cases (suspected or confirmed) forthree countries: Uganda, Rwanda and Malawi. The fore-casts are evaluated over a period of approximately a dec-ade, depending on the length of the data record available.ResultsThe results show that for a number of districts in whichinterventions have been limited over the evaluation period,the forecasts were statistically significantly correlated withobserved malaria outcomes, with the forecasts predictingthe years of anomalous transmission and the sub-seasonalanomalies well. This was true for districts of both low andhigher endemicity. However, for the majority of locations,significance levels were lower. The potential reasons forthis are numerous, including incorrect climate forecasts,errors or missing physics (e.g. lack of immunity) in themalaria model, inaccuracies in the cases data itself, andinterventions that have reduced prevalence and transmis-sion risk as a function of time. We demonstrate, usingconfirmed sentinel site data, that data inaccuracies in thedistrict statistics can be significant. Interestingly, the analy-sis and forecast system appear to indicate that the reduc-tion in malaria prevalence in Rwanda over the past decadehas been achieved against a backdrop of increasing trans-mission hazard due to climate. We conclude by discussingthe weaknesses in the system that require addressing andthe next steps to pilot this system in an operationalcontext.Authors’ details1Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy.2European Centre for Medium Range Weather Forecasts (ECMWF), Reading,UK. 3University of North Carolina Project-Malawi, Lilongwe, Malawi. 4MalawiMinistry of Health, Lilongwe, Malawi. 5Department of Geography, College ofScience and Technology, University of Rwanda, Kigali, Rwanda. 6UgandaMinistry of Health, Kampala, Uganda.Published: 22 September 2014doi:10.1186/1475-2875-13-S1-P99Cite this article as: Tompkins et al.: A dynamical climate-driven malariaearly warning system evaluated in Uganda, Rwanda and Malawi. MalariaJournal 2014 13(Suppl 1):P99.1Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, ItalyFull list of author information is available at the end of the articleTompkins et al. Malaria Journal 2014, 13(Suppl 1):P99http://www.malariajournal.com/content/13/S1/P99© 2014 Tompkins et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

http://dx.doi.org/10.1186/1475-2875-13-S1-P99

A dynamical climate-driven malaria early warning system evaluated in Uganda, Rwanda and Malawi

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