%0 Journal Article %1 Tompkins2014Potential %A Tompkins, Adrian M. %A Di Giuseppe, Francesca %D 2014 %I American Meteorological Society %J J. Appl. Meteor. Climatol. %K seasonal climateservices monthly disease forecasting %R 10.1175/jamc-d-14-0156.1 %T Potential predictability of malaria in Africa using ECMWF monthly and seasonal climate forecasts %U http://dx.doi.org/10.1175/jamc-d-14-0156.1 %X AbstractIdealized model experiments investigate the advance warning for malaria presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weather prediction systems. The climate forecasts drive a dynamical malaria model for all Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associated with the monsoon onset. In many of these areas temperature anomalies are predictable from one to two months ahead, while reliable precipitation forecasts are available in Eastern and Southern Africa one month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission three to four months in advance, extending the early warning available from environmental monitoring by one to two months, although the realizable forecast skill will be less than this due to an imperfect malaria model. A preliminary examination of the forecasts for the highlands of Uganda and Kenya show that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across leadtimes. Finally, in addition to country-level evaluation with district health data, issues that need addressing in order to integrate such a climate-based prediction system into health decision processes are briefly discussed.

@article{Tompkins2014Potential, abstract = {AbstractIdealized model experiments investigate the advance warning for malaria presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weather prediction systems. The climate forecasts drive a dynamical malaria model for all Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associated with the monsoon onset. In many of these areas temperature anomalies are predictable from one to two months ahead, while reliable precipitation forecasts are available in Eastern and Southern Africa one month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission three to four months in advance, extending the early warning available from environmental monitoring by one to two months, although the realizable forecast skill will be less than this due to an imperfect malaria model. A preliminary examination of the forecasts for the highlands of Uganda and Kenya show that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across leadtimes. Finally, in addition to country-level evaluation with district health data, issues that need addressing in order to integrate such a climate-based prediction system into health decision processes are briefly discussed.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Tompkins, Adrian M. and Di Giuseppe, Francesca}, biburl = {https://www.bibsonomy.org/bibtex/249b0d1338d338202e725abb9f359d7fc/pbett}, citeulike-article-id = {13478507}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-14-0156.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/jamc-d-14-0156.1}, day = 30, doi = {10.1175/jamc-d-14-0156.1}, interhash = {c56ecb1b754171166f7bf67dcbed341b}, intrahash = {49b0d1338d338202e725abb9f359d7fc}, journal = {J. Appl. Meteor. Climatol.}, keywords = {seasonal climateservices monthly disease forecasting}, month = dec, posted-at = {2015-01-09 08:04:35}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-06-22T18:34:41.000+0200}, title = {Potential predictability of malaria in Africa using ECMWF monthly and seasonal climate forecasts}, url = {http://dx.doi.org/10.1175/jamc-d-14-0156.1}, year = 2014 }