Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

E. Olaniyan, A. Tompkins, Cyril Caminade
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Abstract

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.
利用区域尺度动态疟疾模型预测西非疟疾高流行区
由于非洲热带地区与疟疾相关的死亡人数居高不下,因此越来越需要开发一个强大的疟疾早期预警系统(MEWS)来采取有效行动,以指导实施具有成本效益的干预措施。本研究采用两阶段分层评估技术,评估 VECTRI 疟疾模型模拟尼日利亚和西非季节性时间尺度(1 - 7 个月)疟疾动态的能力。我们考虑了两组疟疾模拟。第一组是基于观测到的降雨量和温度数据集进行的 VECTRI 模拟(以下称为对照运行)。第二组是基于欧洲中期天气预报中心(ECMWF)System5 集合季节性预报系统(以下简称 "预报运行")驱动的疟疾模拟。采用不同的指标来评估 VECTRI 疟疾模式的技能。基于对照运行的结果表明,该模型可以再现疟疾高流行区和疟疾病例的演变,特别是观察到的病例随人口密度下降而增加的情况。尽管存在明显偏差且相关性较低,但该模型成功预测了尼日利亚各地疟疾病例的年度异常情况,尤其是在疟疾负担较重的热带草原地区。VECTRI 预测运行和 VECTRI 控制运行之间的年度相关性在西非的所有提前期(LT)和每个开始日期(SD)都相对较低,但从几内亚湾到萨赫勒地区,相关性普遍上升。尽管相关性较低,但等级概率技能得分(RPSS)显示,无论起始日期或准备时间如何,该模型在预测所有类别疟疾病例发生率方面都具有显著的统计技能。虽然几内亚森林的 RPSS 值最高,但从第一个到第七个前导时间的技能增减在各地区差异很大。此外,VECTRI 疟疾模型对各地区疟疾病例的变化具有良好的判别能力,其相对工作特征曲线(ROC)下的平均面积(AUC)约为 0.62。我们的研究结果表明,VECTRI 疟疾模型可用作可靠的疟疾预警系统(MEWS),特别是用于在季节时间尺度上识别西非疟疾高流行区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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