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

ABSTRACT: 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 perfor

SUBMITTER: Kim Y 

PROVIDER: S-EPMC6884483 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature