Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model
Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model
复制标题
DOI：
10.1038/s41598-019-53838-3
复制
发表时间：
2019-11-29
影响因子：
4.6
通讯作者：
Hashizume, Masahiro
中科院分区：
文献类型：
--
作者：
Kim, Yoonhee;Ratnam, J. V.;Hashizume, Masahiro
尽管人们对开发疟疾早期预警系统提出了巨大的要求并付出了巨大的努力，但仍然没有可持续的系统。需要组织良好的疟疾监测和高质量的气候预报，以维持疟疾早期预警系统和有效的疟疾预测模型。我们的目标是使用南非林波波省 Vhembe 1998 年至 2015 年的每周时间序列数据（包括温度、降水和疟疾病例）开发基于天气的疟疾预测模型，并将其应用于季节性气候预测。疟疾预测模型在短期预测方面表现良好（相关系数，对于未来 1 周和 2 周的预测，r > 0.8）。预测精度随着提前期的增加而下降，但在 16 周预测之前仍保持相当好的性能 (r > 0.7)。基于季节性气候预测的疟疾预测过程演示表明，短期预测与观察到的疟疾病例密切吻合。我们开发的基于天气的疟疾预测模型可以与熟练的季节性气候预测和现有的疟疾监测数据一起应用于实践。建立基于实时数据输入的自动化操作系统将有利于疟疾预警系统，也可为其他疟疾流行地区提供有益的借鉴。
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.