@article{oai:nagasaki-u.repo.nii.ac.jp:00000408,
 author = {Kim, Yoonhee and Ratnam, J. V. and Doi, Takeshi and Morioka, Yushi and Behera, Swadhin and Tsuzuki, Ataru and Minakawa, Noboru and Sweijd, Neville and Kruger, Philip and Maharaj, Rajendra and Imai, Chisato Chrissy and Ng, Chris Fook Sheng and Chung, Yeonseung and Hashizume, Masahiro},
 issue = {1},
 journal = {Scientific Reports},
 month = {Nov},
 note = {Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r?>?0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r?>?0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas., Publisher Correction: A supplementary file containing Fig S1 was omitted from the original version of this Article. This has been corrected in the HTML version of the Article; the PDF version was correct at time of publication. https://doi.org/10.1038/s41598-020-58890-y, Scientific reports, 9(1), art.no.17882; 2019},
 title = {Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model},
 volume = {9},
 year = {2019}
}