Malaria predictions based on seasonal climate forecasts in South Africa: a time series distributed lag nonlinear model

Yoonhee Kim, J. V. Ratnam, Takeshi Doi, Yushi Morioka, Swadhin Behera, Ataru Tsuzuki, Noboru Minakawa, Neville Sweijd, Philip Kruger, Rajendra Maharaj, Chisato Chrissy Imai, Chris Fook Sheng Ng, Yeonseung Chung, Masahiro Hashizume*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)
22 Downloads (Pure)


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.

Original languageEnglish
Article number17882
Pages (from-to)1-10
Number of pages10
JournalScientific Reports
Issue number1
Publication statusPublished - 1 Dec 2019

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


Dive into the research topics of 'Malaria predictions based on seasonal climate forecasts in South Africa: a time series distributed lag nonlinear model'. Together they form a unique fingerprint.

Cite this